Transactions on Intelligent Welding Manufacturing: Volume II No. 4 2018 [1st ed. 2019] 978-981-13-8667-1, 978-981-13-8668-8

Table of contents :
Front Matter ....Pages i-x
Front Matter ....Pages 1-1
On-Line Monitoring and Defects Detection of Robotic Arc Welding: A Review and Future Challenges (Zhifen Zhang, Guangrui Wen, Shanben Chen)....Pages 3-28
Narrow Gap Welding for Thick Titanium Plates: A Review (Qingjie Sun, Junzhao Li, Yibo Liu, Jicai Feng)....Pages 29-54
Front Matter ....Pages 55-55
Thermal Analysis of Belt Grinding Process of Nickel-Based Superalloy Inconel 718 (Xukai Ren, Baptiste Soulard, Junwei Wang, Yanling Xu, Xiaoqi Chen)....Pages 57-74
Development of a Low-Cost Arc Spectrum Sensor for Monitoring Pore Defects in Welding Process (Gang Li, Haiping Chen, Jingyuan Xu, Chao Chen, Na Lv, Shanben Chen)....Pages 75-92
Determination of the Initial Welding Point for Multi-pass Welding Based on Laser Vision (Yanhui Lai, Ruilin Dai, Hao Zhou, Zhen Hou, Huabin Chen, Shanben Chen)....Pages 93-108
Selection of Arc Spectrum Features and Defect Recognition in GTAW Based on Random Forest (Zhe Yang, Guangrui Wen, Wenjing Ren, Zhifen Zhang)....Pages 109-123
Bead Geometry Prediction for Multi-layer and Multi-bead Wire and Arc Additive Manufacturing Based on XGBoost (Junhao Deng, Yanling Xu, Zhangchi Zuo, Zhen Hou, Shanben Chen)....Pages 125-135
Microstructure and Electrochemical Corrosion Properties of 316L Stainless Steel Joints Brazed with BNi5 (Gongxiang Zhao, Jieshi Chen, Qingzhao Wang, Xiao Wei, Jijin Xu, Junmei Chen et al.)....Pages 137-149
Three-Dimensional Printing: Revolutionary Technology for Academic Use & Prototype Development (Bramha Swaroop Tripathi, Ritu Gupta, S. R. N. Reddy)....Pages 151-161
Back Matter ....Pages 163-165

Citation preview

Transactions on Intelligent Welding Manufacturing Volume II No. 4 2018

Transactions on Intelligent Welding Manufacturing Editors-in-Chief Shanben Chen Shanghai Jiao Tong University Shanghai, Shanghai, China

Yuming Zhang Department of Electrical and Computer Engineering University of Kentucky Lexington, KY, USA

Zhili Feng Oak Ridge National Laboratory Oak Ridge, USA

Honorary Editors G. Cook, USA K. L. Moore, USA Ji-Luan Pan, PRC

S. A. David, USA S. J. Na, KOR Lin Wu, PRC

Y. Hirata, JAP J. Norrish, AUS

T. Lienert, USA T. J. Tarn, USA

X. Q. Chen, NZL D. Hong, USA W. Zhou, SGP

D. Du, PRC X. D. Jiao, PRC

D. Fan, PRC I. Lopez-Juarez, MEX

Guest Editors H. P. Chen, USA J. C. Feng, PRC H. J. Li, AUS

Regional Editors Australia: Z. X. Pan, AUS Europe: S. Konovalov, RUS

Asia: L. X. Zhang, PRC America: Y. K. Liu, USA

Associate Editors Q. X. Cao, PRC B. H. Chang, PRC J. Chen, USA H. B. Chen, PRC S. J. Chen, PRC X. Z. Chen, PRC A.-K. Christiansson, SWE Z. G. Li, PRC X. M. Hua, PRC

Y. Huang, USA S. Konovalov, RUS W. H. Li, PRC X. R. Li, USA Y. K. Liu, USA L. M. Liu, PRC H. Lu, PRC Z. Luo, PRC G. H. Ma, PRC

Pedro Neto, PRT G. Panoutsos, UK Z. X. Pan, AUS X. D. Peng, NL Y. Shi, PRC J. Wu, USA J. X. Xue, PRC L. J. Yang, PRC M. Wang, PRC

S. Wang, PRC X. W. Wang, PRC Z. Z. Wang, PRC G. J. Zhang, PRC H. Zhang, B, PRC H. Zhang, N, PRC L. X. Zhang, PRC W. J. Zhang, USA

S. L. Wang, PRC J. Xiao, PRC J. J. Xu, PRC Y. L. Xu, PRC C. Yu, PRC

H. W. Yu, PRC K. Zhang, PRC W. Z. Zhang, PRC Z. F. Zhang, PRC

Academic Assistant Editors J. Cao, PRC B. Chen, PRC Y. Luo, PRC N. Lv, PRC F. Li, PRC

S. B. Lin, PRC Y. Shao, USA Y. Tao, PRC J. J. Wang, PRC H. Y. Wang, PRC

Editorial Staff Executive Editor (Manuscript and Publication): Responsible Editors (Academic and Technical):

Dr. Yan Zhang, PRC Dr. Na Lv, PRC Dr. Jing Wu, USA

More information about this series at http://www.springer.com/series/15698

Shanben Chen Yuming Zhang Zhili Feng •

•

Editors

Transactions on Intelligent Welding Manufacturing Volume II No. 4 2018

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Editors Shanben Chen Shanghai Jiao Tong University Shanghai, Shanghai, China Zhili Feng Oak Ridge National Laboratory Oak Ridge, TN, USA

Yuming Zhang Department of Electrical and Computer Engineering University of Kentucky Lexington, KY, USA

ISSN 2520-8519 ISSN 2520-8527 (electronic) Transactions on Intelligent Welding Manufacturing ISBN 978-981-13-8667-1 ISBN 978-981-13-8668-8 (eBook) https://doi.org/10.1007/978-981-13-8668-8 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Editorials

This issue of the Transactions on Intelligent Welding Manufacturing (TIWM) is another collection of high-quality papers selected from “2018 International Conference on Robotic Welding, Intelligence and Automation (RWIA’2018),” December 7–10, 2018, Guangzhou, China. Two feature articles, six full research papers, and one short paper are included which contribute to the understanding, sensing, and control of welding manufacturing processes. The first featured article in this issue, “On-Line Monitoring and Defects Detection of Robotic Arc Welding: A Review and Future Challenges,” is by Zhifen Zhang, Guangrui Wen, and Shanben Chen which is a joint research team from School of Mechanical Engineering, Xi’an Jiao Tong University and School of Materials Science and Engineering, Shanghai Jiao Tong University. This paper provides brief review of the state-of-the-art online welding process monitoring based on different sensing techniques, including image vision, laser vision and distance, arc optical emission, arc audible sound, and new immerging X-ray computed tomography. It also provides description of feature dimension reduction and selection for multisensory information fusion. The anticipated challenges are discussed from the aspect of data correlation, evaluation, and deep learning. The second featured article “Narrow Gap Welding for Thick Titanium Plates: A Review” is a contribution from State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology. Authorized by Qingjie Sun, Junzhao Li, Yibo Liu, and Jicai Feng, this paper reviews the issues and challenges in welding thick titanium alloys, introduces the process characteristics of automated welding technologies, and finally provides recommendations for future work. While gas tungsten arc welding is widely used, laser welding with filler wire can be a promising alternative in joining thick Ti structure with the characteristics of narrower groove, high welding efficiency, and low-heat input. The first selected research article, “Thermal Analysis of Belt Grinding Process of Nickel-Based Superalloy Inconel 718,” is contributed by Xukai Ren, Baptiste Soulard, Junwei Wang, Yanling Xu, and Xiaoqi Chen from Shanghai Jiao Tong University. This study aims to achieve a better understanding of the thermal behavior of the grinding process in order to develop an automated grinding robotic v

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system. Both analytical and numerical approaches were used to obtain the temperature field in the grinding process. The second selected research article, “Development of a Low-Cost Arc Spectrum Sensor for Monitoring Pore Defects in Welding Process,” is contributed by researchers from Shanghai Jiao Tong University. A low-cost arc spectrum information sensor based on Cherney–Turner optical system is designed and manufactured to detect the formation of hydrogen pore defects in welding process and to provide support for the follow-up research on hydrogen pore defects. Experiments show that the sensor achieves real-time and effective detection and acquisition of arc spectrum information in aluminum alloy welding process. The third research article, “Determination of the Initial Welding Point for Multipass Welding Based on Laser Vision,” by Yanhui Lai, Ruilin Dai, Hao Zhou, Zhen Hou, Huabin Chen, and Shanben Chen, describes an extraction procedure for feature points in laser stripe. The authors show that the procedure can determine the initial welding position with the two feature points for every welding pass in the medium thick plates. The fourth article, “Selection of Arc Spectrum Features and Defect Recognition in GTAW Based on Random Forest,” is from School of Mechanical Engineering, Xi’an Jiao Tong University. An online detection method for multiple welding defects is proposed in this paper. By comparing the results of different feature recognition, the feature selection effectively removes the useless features and redundant features, and improves the computational efficiency of the subsequent models. In the fifth research article, “Bead Geometry Prediction for Multi-layer and Multi-bead Wire and Arc Additive Manufacturing Based on XGBoost,” machine learning is used to predict the geometrical morphology of multilayer and multichannel WAAM forming parts. Compared with the neural network algorithm, the regression prediction model of arc additive manufacturing morphology based on XGBoost has a higher prediction accuracy. The sixth article, “Microstructure and Electrochemical Corrosion Properties of 316L Stainless Steel Joints Brazed with BNi5,” is a contribution from Shanghai Jiao Tong University. The study investigates the microstructure and electrochemical corrosion characteristics of 316L stainless steel joints brazed with BNi5. The study reveals that the corrosion resistances of the brazing joints are poorer than base metal because the different phases in the joint form many microbatteries. The short paper, “Three-Dimensional Printing: Revolutionary Technology for Academic Use & Prototype Development,” presents the “fused deposition modeling” technique of 3D printing and its applications in making real-world objects. This technology helps to develop design skills and methodologies for creativity and to create art effects that can be used as learning aids or as adaptive technologies in special learning settings. This paper mainly describes how to design and develop various objects and prototypes using this technology.

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In addition to the topics of the conference RWIA’2018, the above papers can contribute to the frontier of intelligent welding manufacturing. Furthermore, to better understand welding manufacturing processes, more innovative and creative methods and techniques are needed. Zhili Feng, Ph.D. TIWM Editor-in-Chief [email protected]

Contents

Part I

Feature Articles

On-Line Monitoring and Defects Detection of Robotic Arc Welding: A Review and Future Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhifen Zhang, Guangrui Wen and Shanben Chen Narrow Gap Welding for Thick Titanium Plates: A Review . . . . . . . . . Qingjie Sun, Junzhao Li, Yibo Liu and Jicai Feng Part II

3 29

Research Papers

Thermal Analysis of Belt Grinding Process of Nickel-Based Superalloy Inconel 718 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xukai Ren, Baptiste Soulard, Junwei Wang, Yanling Xu and Xiaoqi Chen Development of a Low-Cost Arc Spectrum Sensor for Monitoring Pore Defects in Welding Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gang Li, Haiping Chen, Jingyuan Xu, Chao Chen, Na Lv and Shanben Chen Determination of the Initial Welding Point for Multi-pass Welding Based on Laser Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanhui Lai, Ruilin Dai, Hao Zhou, Zhen Hou, Huabin Chen and Shanben Chen

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Selection of Arc Spectrum Features and Defect Recognition in GTAW Based on Random Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Zhe Yang, Guangrui Wen, Wenjing Ren and Zhifen Zhang Bead Geometry Prediction for Multi-layer and Multi-bead Wire and Arc Additive Manufacturing Based on XGBoost . . . . . . . . . . . . . . . 125 Junhao Deng, Yanling Xu, Zhangchi Zuo, Zhen Hou and Shanben Chen

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Microstructure and Electrochemical Corrosion Properties of 316L Stainless Steel Joints Brazed with BNi5 . . . . . . . . . . . . . . . . . . . . . . . . . 137 Gongxiang Zhao, Jieshi Chen, Qingzhao Wang, Xiao Wei, Jijin Xu, Junmei Chen, Chun Yu and Hao Lu Three-Dimensional Printing: Revolutionary Technology for Academic Use & Prototype Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Bramha Swaroop Tripathi, Ritu Gupta and S. R. N. Reddy Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

Part I

Feature Articles

On-Line Monitoring and Defects Detection of Robotic Arc Welding: A Review and Future Challenges Zhifen Zhang, Guangrui Wen and Shanben Chen

Abstract Robotic arc Welding is the main manufacturing technology for key structure components in the industries of aerospace, automobile, nuclear, ship and military equipment. Real-time monitoring, controlling and detecting of the welding process and seam quality can improve the stability and reliability of weld quality while increasing the efficiency and accuracy of defect detection. In this paper, we briefly reviewed the state-of-art on-line welding process monitoring based on different sensing techniques, including image vision, laser vision and distance, arc optical emission, arc audible sound and new immerging X-ray computed tomography. Then, a concise review of feature dimension reduction and selection is provided before the multisensory information fusion. The anticipated challenges are carefully discussed from the aspect of data correlation, evaluation and deep learning. We believe that more attention should be paid on topics such as real-time inner defects detection combining with defects micro characterization; problems related to complex-thin-big structure component welding; and applications of the latest deep learning technologies. Keywords Monitoring · Defect detection · Robotic welding · Multiple sensing · Information fusion · Deep learning

1 Introduction Intelligent robotic Welding Manufacturing (IWM) is the core of intelligent manufacturing. For robotic intelligent welding process, guaranteeing its weld quality with certain tolerance of defects in real-time has always been a significant and challenging task [1, 2]. Moreover, manual sampling inspection is often adopted rather than Z. Zhang · G. Wen (B) School of Mechanical Engineering, Xi’an Jiao Tong University, Xi’an 710049, China e-mail: [email protected] S. Chen (B) School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-8668-8_1

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full inspection for some industry, like automotive manufacturing. In this case, it is difficult to insure the quality of each weldment, although both destructive and nondestructive evaluation techniques are used off-line to inspect its quality. Hence, it is quite important to research on the methodology and technique of on-line weld quality evaluation and defect detection for robotic welding process. For one thing, it is able to take the full inspection for each welding seam and give the comprehensive assessment of weld quality during the robotic welding process, which obviously improves the stability and reliability of weld quality. In addition, the accuracy of defect detection will be highly enhanced not to mention the increasing of efficiency and productivity. However, because of the complexity of the welding process and randomness of the process interference, various weld defects, such as surface, metallurgical, processinduced, and inner defects, are usually inevitable. For instance, defects of under penetration, over penetration, and burning through can greatly weaken the strength of the welding joint and are closely related to the wire feeding. Besides, if the wire feeding and the weld pool are interrupted, defects, such as poor surface quality or inner porosity, typically in Al alloys, might occur. Therefore, real-time monitoring of the welding process and seam quality is of great significance in terms of timely detection of seam defects, improvement in the stability of the welding quality and manufacturing efficiency, and promotion of intelligent welding manufacturing. By accurately sensing certain type of information, the welding robot might be “smart” enough to identify the seam quality of aluminum alloy in real-time. In this paper, a broad review of different types of sensing technologies was successively given in Sect. 2. Then, feature selection and information fusion were introduced in Sect. 3 and Sect. 4. A thorough discussion was conducted in Sect. 5.

2 Sensing of Welding Process 2.1 Image Vision Welding vision information mainly includes the liquid welding pool, arc torch, tungsten electrode and filling wire etc., which can directly reflect the dynamic change of welding pool and can provide abundant information about the welding process and welding quality. Huang et al. [3] and his research team studied in situ measurement of welding distortion based on digital image correlation (DIC), as seen in Fig. 1 the out-of-plane distortion of a thin plate during the Tungsten Inert Gas (TIG) welding process was measured by means of numerical simulation and experimental method. It was found that the maximum out-of-plane distortion was larger than 4 mm during the welding process. The relationship between heat input and welding distortion was clearly clarified, which can provide valued information for Optimization of welding heat

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Fig. 1 In-situ measurement of welding distortion based on digital image correlation (DIC) method [3]

input and welding quality. Liu et al. [4, 5] has proposed an innovative vision-based sensing system using laser generator and dot matrix structured light and researched its control applications for GTAW process. In the recent research, it is reported that the human welder’s response against 3-D weld pool surface is learned and transferred to the welding robots to perform automated welding tasks [6] as seen in Fig. 2. Xu et al. [7–9] has done years of research about real-time seam tracking for robotic GMAW system based on vision sensor. In the latest report [7], they developed the comprehensive software together with the seam tracking vision sensing system including the welding process control, image capturing and processing, the welding expert database, robot communication and path planning modules, as shown in Figs. 3 and 4. Their maximum error of welding seam tracking is within ±0.45 mm by randomly moving the welding work-piece. Chen [10, 11] innovatively proposed a reflection model to calculate the index of the weld pool surface height from the passive vision image during GTAW Based on the spherical mirror assumption of the weld pool surface, as seen in Fig. 5. Furthermore, the established to estimate backside width of welding seam was estimated during GTAW by means of two supervised machine learning method including linear regression and bagging trees. Feature selection was carried out using sequential feature selection to determine the best feature subset and improve the performance of regression model. Fan [12] a precise initial weld point guiding method of micro-gap weld is proposed in this paper. This initial weld point guiding method consists of two parts. In the vision sensing part, a structured light vision sensor with a narrow-band optical filter and a LED light is designed to acquire clear image, including laser stripe and micro-gap weld seam. Yang [13] developed a stereo-structured light sensor, based on which 3-D reconstruction of weld path was performed for robotic welding process, as shown in Fig. 6.

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Fig. 2 Detailed view of the 3-D weld pool sensing system [6]

Fig. 3 A purpose-built vision sensor system for seam tracking in robotic gas metal arc welding (GMAW) [7]

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Fig. 4 Proposed system structure diagram of the welding seam tracking system, and the graphical user interface (GUI) of welding seam tracking system [7] Fig. 5 Principle of reversed electrode image acquisition [10, 11]

For V-type butt joint, their maximum path extraction error of is less than 0.7 mm, which can provide sound technologies for the 3-D path teaching task before welding. Image vision sensing technologies has been widely used in industry for the purpose of condition monitoring and seam tracking. However, for the complex, thin and largescale structure plate, usually seen in aerospace, military and civil plane, it is still difficult to achieve the accurate and robust seam tracking. Future work should pay more attention on those problems in order to improve the industry application.

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Fig. 6 3D reconstruction of V-type butt joints based on a stereo-structured light sensor [13]: a, b the images of work pieces; c, d the point cloud of work piece

2.2 Laser Vision and Distance With the increasing application of welding robot, one of the key problems for largescale, complex and thin structure components is precise welding of spatial curve seam. Laser distance sensing is one of the non-contact measurement technologies that can calculate the distance based on triangulation principle. Tao [14, 15] researched the measurement methodology and theory in order to enhance the precision and accuracy of large range measurement based on laser triangulation. Their system and technology have realized the industrial applications. Chen [16–18] has deeply researched the Optimization of Weld Trajectory and Pose Information for Robot Welding. In literature [16], they proposed a new method with high practicality and robust based on laser sensing in order to acquire the trajectory and pose information. They integrated the robot, laser sensor, CCD camera, encoder, working station, and computer into a system to control the robot to complete the automatic welding process with precise position.

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2.3 Arc Optical Emission Arc spectrum emission, as one of the non-intrusive sensing technologies, containing abundant information including chemical elements, their wavelength, and emission intensity, has shown better sensitivity to the dynamic welding process and seam defects [19–22]. On one hand, metal spectrum emission contains vast plasma spectrum information about the chemical elements, such as Al, Fe, Mg, and Mn, which mainly come from welding tungsten, welding wire, and base material. They are the products of the dynamic interaction between the welding arc, wire, and weld pool, and are closely related to the welding quality and defects. Besides, comparing with other types of sensing technologies, it has displayed more comprehensive capability regarding on-line defects detection of welding seam. Based on the different acquiring principle, the acquisition signal for arc optical emission can be mainly divided into three classes including one-dimensional, twodimensional and three-dimensional spectrum signal. Specifically, first kind of optical signal system collects the light intensity from the regions of interest, such as visible, IR and UV, wherein, the acquired signal is one dimension and integrated intensity. The second type of two-dimensional spectrum signal is composed of wavelength and intensity. The third type of three-dimensional arc spectrum is spectrum image, which can be captured by using the CCD camera and certain wavelength of interference filter. The spectrum image can show the space morphological characteristics of the arc plume for the selected element, such as Fe, Mn and Ar.

2.3.1

One-Dimensional Spectrum Signal

The first sensing technology has been reposted for research of laser welding monitoring [23–29], GMAW monitoring [30] and GTAW defect detection [31]. Their signal processing method is commonly similar to any other one-dimension signal, which can extract monitoring parameters from time domain and frequency domain respectively. Reference [30] developed a data processing algorithm which encompasses a Kalman filter to reduce the heavy amount of noise affecting the measured signals. In time domain, statistic parameters, such as RMS (root mean square) of defined sample signal is proved to be an effective monitoring parameters with its clear physical meaning, as seen in Refs. [29, 31]. In frequency domain, FFT (Fast Fourier transformation) is commonly used, such as in Refs. [20, 26], to correlate the frequency component with welding defect. Furthermore, because of the inherent character for laser welding, correlation coefficient analysis was also investigated in Refs. [26, 32–34] in time domain and frequency domain in order to quantify the stability of laser welding process.

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Two-Dimensional Spectrum

The two-dimensional spectroscopy signal is composed of wavelength (x-axis) and its intensity (y-axis). It contains thousands of emission lines (depended on its resolution) of the excited atoms or ions belonging to the metals to be welded or to the gaseous environment (shielding gas or ambient air). It is thus possible to determine the chemical composition and the dynamics of interaction of the different chemical species inside the plume. For the feature extraction, mainly two methods are reported including plasma diagnostic parameters like electric temperature, and morphology or statistic parameters. Comparing with the one-dimensional spectrum in last section, 2D spectrum signal has quite mature products containing both small-scale hardware and matched acquiring software. Figure 7 is the Arc spectra and visual information acquisition system including a low-cost 3648 pixels spectrometer (Ocean Optics HR4000) and an optical fiber (Ocean Optics P400-UV-SR) [35, 36]. In literature [19], several welding states including correct welding, unskilled wire feed, deflected wire feed and weld seam depression were successfully detected based on BP neural network and extracted spectrum features. Furthermore, on-line detection of hydrogen porosity of Al–Mg alloy in pulsed GTAW was experimentally investigated and was realized based on the intensity ratio of H I line at 656.28 nm to Ar I line at 641.63 nm and its statistic features. As displayed in Fig. 11, the position of native and artificial porosity was effectively identified according to the defects’ characterization in longitudinal section. Arc spectrum contains abundant information that relates to welding quality. As shown in Fig. 8, Zhang [31] has proposed the real-time defects detection method while 8 spectrum bands of interests (SOI) were selected out and their statistics were calculated to monitor the weld process. More importantly, a criterion evaluating the sensitivity of the extracted feature parameters was introduced based on signal to noise ratio (SNR) for feature dimension reduction. The occurrence of inner porosity has a high uncertainty and makes real-time detection challenging. For one thing, the generation of porosity may lag behind the real-time spectrum emission; secondly, it is quite difficult to detect and locate inner

Fig. 7 Arc spectra and visual information acquisition system in HW Yu’s research [35, 36]

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porosity with high precision during the dynamic welding process. Real-time detection of inner porosity for aluminum alloy in GTAW has been deeply researched by Huang [37–40]. In their latest report [37], the method of physical model, experiment and metallographic observation were innovatively combined together to reveal the mechanism of porosity formation, as shown in Fig. 9. Models for bubble nucleation and bubble growth were established respectively while the influence of welding current on the critical nucleation radius was investigated. Based on the experiments, the developed physical model shows good consistence with the metallographic observation. The research results have provided theoretical foundation for on-line porosity detection. Zhang [41] recently investigated the correlation between the microscopic feature of porosity and arc spectrum feature. Feature parameters based on the new PCs and coefficient of PCA were extracted for detecting porosity during the Al alloy welding process. A correlation between the microscopic analysis of porosity and spectrum features was discovered. Two types of inner porosity were found for Al alloy in GTAW, e.g., Mg vapor porosity and H porosity, as seen in Fig. 10. Mirapeix [19, 42–45] has done years of deep research about welding quality monitoring and detection based on arc spectrum. As shown in Fig. 11, the ratio

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Fig. 9 Physical model of porosity formation for aluminum alloys in pulsed GTA welding [37]

Fig. 10 Inner porosity characterization and on-line detection [41]

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Fig. 11 Detail of the laser welding head and the attached optical fiber and input optics used during the preliminary tests [19]

of line-to-continuum for the Al I emission line at 396.15 nm was extracted as the monitoring feature. Al contribution, which affects the seam quality of Usibor blanks in the laser welding process, has been quantitatively researched in their report [19]. Song et al. [46] predicted the Al concentration during a laser additive manufacturing process by means of Al/Ti line-intensity ratio and support vector regression. Mazumder et al. [47] invented several patents for the on-site analysis of the composition, phase transformation, and manufacturing defects of an additive manufacturing process based on spectrum features such as the intensity ratio of ions and electron density.

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Spectrum Image

The third type of three-dimensional arc spectrum is spectrum image, which can be captured by using the CCD camera and certain wavelength of interference filter. The spectrum image can show the space morphological characteristics of the arc plume for the selected element, such as Fe, Mn and Ar. Hua and Xiao [48, 49] investigated the theory of arc physics for TIG welding based on arc spectrum image. As show in Fig. 12, the arc image acquisition system was developed using a camera lens, neutral density filters, a narrow band pass filter, and a CCD sensor. Figure 13 displayed the captured monochromatic images of Ar

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Fig. 12 Synchronous arc image acquisition system in TIG welding [48, 49]

Fig. 13 a Arc images of an Ar I 794.8 nm in pure argon, b N I 904.6 nm in pure nitrogen used to measure the temperature and gas composition of the argon–nitrogen arc plasma [48, 49]

I and N I in the pure argon and pure nitrogen arc plasma respectively. By means of Abel inversion and Fowler-Milne method, the arc temperature and gas composition can be calculated which provides valued evidence for arc heat source theory and enriches the arc spectrum theory for complex welding process. In order to reveal the complex mechanism of the heat source during the dynamic welding process, Nomura [50] proposed a multi-directional arc spectrum image measurement system, as displayed in Fig. 14, The proposed system is composed of 12 CCD cameras with three types of narrowband interference filters (one Ar I line and two Fe I lines). The temperature of the central area of the MIG arc plasma was measured and was compared with their previous 2D studies. The similar researches were reported by Shigeta et al. [51] and Nomura et al. [52]. More comprehensive review was given by Murphy [53]. Overall, valued researches have been performed based on arc spectrum for intelligent welding. Although the abundant information of a spectrum emission signal can be acquired during the GTAW process, like thousands of spectrum components corresponding to various chemical elements, at present only several spectrum lines are used for defect detection, which is a low utilization. Intensive research on the

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Fig. 14 Arc spectrum image system containing 12 CCD cameras with three types of narrowband interference filters (one Ar I line and two Fe I lines) [50]

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selection of spectral data relating to seam defects is still lacking. A much stronger correlation between spectrum signals and inner defects needs to be revealed and built based on the clarified porosity generation mechanism of Al alloy in pulsed GTAW. Most of all, the spectrometer is sensitive to the probe position. A slight change in the probe position might greatly influence the acquired spectrum intensity. Thus, establishing more complete spectrum feature knowledge base of defects, studying the effect of all types of defects on the spectrum signal, and selecting the key spectrum information are urgently required, and are the essential precondition for establishing their correlation with the welding quality and then precise defect detection during the welding process. We believe that the effective method and results can not be obtained without the fundamental and plenty of repeated experiments no matter using the physical modelling, artificial intelligence or spectrum theory. Moreover, successful industry applications are still rare, thus the specific technology problems should be investigated more in future.

2.4 Arc Audible Sound As one of non-intrusive sensing tool, arc audible sound, has shown great pretential in robotic weld quality evaluation [20, 54, 55], especially for the complex construction, including fillet weld or circumferential weld where other kinds of quality information (welding image or X-ray detection) are hardly acquired. Obviously, accurate features of arc sound signal are the key to successful identifying of seam quality. Several statistic features in time domain, like RMS, kurtosis [20, 56] calculated from arc sound signal based on certain threshold were proven to be sensitive to seam penetration. Literature [57] successfully conducted the real-time arc length control to acquire stable seam quality. Other features in frequency domain [54] were also studied in terms of its ability of seam defects detection. In addition, literature [58] developed a microphones array surveillance system for excavation equipment recognition in order to pretend underground pipeline network from external destruction. Several acoustic statistical features in time domain and frequency domain were developed to characterize four types of typical excavation equipment. Literature [59] researched about automatic detection of violent content in movies based on audio and visual information. Twelve audio features were extracted to characterize movie contents like Music, Speech, gunshots, screams and so on. Mel-frequency cepstral coefficients (MFCC) and its statistic features are more widely used for speech recognition. For welding quality monitoring, LPCC, typical feature for speech recognition, was effectively applied to establish prediction model of arc sound track [60] as well as MFCC [61] which was reported to be of certain capability in characterizing different seam penetration. Recently, online control of welding penetration via arc sound signal for pulse GTAW welding was achieved by Lv [62], as seen in Fig. 15, which can provide a new way for welding process controlling.

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Fig. 15 The flow diagram of online control of penetration state via arc sound signal [62]

2.5 X-Ray Computed Tomography Back in 90s, Rokhlin [63–66] has done detailed research about real-time radiography for in-process arc weld quality evaluation. By using computer data on the grey levels of the weld images and their histogram distributions, dynamic liquid pool depression and weld penetration were quantitatively characterized and their relationship were also analyzed, finally used for weld tracking and welding current control. Using a real-time X ray imaging technology has certain potential in detecting particular arc welding defects, especially the inner one, like micro porosity, micro slag inclusion. Katayama [24, 67–69] developed a novel real-time imaging system based on micro focused X-ray transmission and high-speed camera. As illustrated in Fig. 16, the bubble is the key formation factor of porosity defect. In dynamic welding process, if it can successfully rise and escape from the liquid welding pool, a porosity-defect free welding seam may be obtained as wish; otherwise, it would cause the porosity defect either micro one or macro one. However, besides the high cost and complexity of the measurement system, the acquired X-ray gray image has its own disadvantages, such as image degradation, low discrimination acuity and so on, which also can be interpreted from Fig. 16. In this case, for those who are

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Fig. 16 X-ray transmission observation results during hybrid welding at laser power of 9 kW, and schematic melt flows inside molten pool [67]

interested with this specific area, a few difficulties would be encountered and need to be firstly solved [70], including image restoration, image enhancement, Image segmentation, image recognition and other related image processing techniques as well as advanced Real-time tracking algorithms. In essence, it has high similarity with the research of machinery fault detection in terms of monitoring the dynamic behavior of certain objectives. Comparing with the two-dimensional X-ray measurement, X-ray computed tomography is a method of forming three dimensional (3D) representations of an object by taking many X-ray images around an axis of rotation and using algorithms to reconstruct a 3D model, which has been applied to additive manufacturing (AM) for real-time monitoring and measurement [71–73]. In literature [73], synchrotron Radiation micro-Tomography was used to measure three-dimensional pore volume, distribution, and morphology in stainless steel at the micrometer length-scale while tensile loading was applied. A comprehensive review of X-ray computed tomography applied for additive manufacturing was given in literature [71]. In short, X-ray and computed tomography methods have more powerful capability in sensing and detecting the inner defect of welding seam or 3D printing products as well as the structure transformation during the dynamic manufacturing process. In this case, it can help us to understand and reveal the scientific theory of material manufacturing, which might provide better foundation for better manufacturing quality. However, the limitation of X-ray and CT is quite obvious due to its harmful emission, which is hard to construct in industry workshop.

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3 Feature Reduction and Selection Feature reduction technology can effectively reduce the dimension of extracted features before the optimal feature subset was selected. For one thing, the monitoring tools and signals have increased as well as the great development of feature extraction technologies. The other thing is that the great amount of information such as arc spectrum emission containing thousands of lines spectrum and elements. Therefore, feature reduction and selection are quire necessary for effective monitoring and detection of welding quality during the dynamic welding process. T-SNE is a powerful tool of data reduction and visualization for high-dimensional data sets and has been as popular as the deep learning method in research focusing on manifold learning. Wu [74, 75] integrated t-SNE and a deep belief network to identify the seam penetration status during the polarity plasma arc welding process and achieved feature dimension reduction and visualization as seen in Fig. 17. Wu [74] has established an extreme learning machine (ELM) model for online predicting of backside weld width in VAPPPW, which has shown a faster computation speed and better prediction accuracy than traditional BPNN. Huang [39] proposed an improved K-medoids algorithm in order to extract the spectral lines of interests for on-line detecting of seam porosity. Principal component analysis (PCA) is one of the linear dimensionality reduction techniques in the area of machine learning and artificial intelligence. It can extract highly relevant information and construct new features by assigning different coefficients to them. Its coefficients for each principal components of each feature can be considered as a quantitative index to evaluate the sensitivity of extracted features to welding process and seam defects. PCA has been applied to arc spectrum emission signal [35, 36, 42] and Audible sound [76]. For arc spectrum signal of Al alloy captured during GTAW process, literature [41] proposed a new feature reduction method

Fig. 17 Framework of VPPAW penetration identification based on t-SNE and DBN [75]

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Modeling Support Vector Machine based on Grid Optimization Identification of Feature Extraction of Audible Search Optimization and Cross Validation sound signal in frequency domain Predicting Seam Penetration (SVM-GSCV) Feature Selection of frequency components Model Grid Search Training Fisher Selection optimization 2.89kHz SVM Classifier 2.93kHz 5.6kHz 9.97kHz

PCA Selection

Principal component F1 Principal component F2 Principal component F3

Letk=0; k=k+1;

Model Test

Model kth Test output Accuracy: Ak

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k> 5 ? Yes

Stop

Mean AccuracyAm=Sum(Ak)/ 5;

Fig. 18 Identification model of seam penetration based on support vector machine using grid search optimization and fivefold cross-validation (SVM-GSCV) [76]

by integrating PCA and tSNE to reduce the PCA dimension and improve the ability to distinguish different classes of porosity seams. Zhang [76] applied PCA to select certain frequency components of arc sound signal in terms of better characterizing the weld penetration defect, as seen in Fig. 18. Besides, the PCA contribution was used to evaluate the data redundancy of arc sound signal in frequency domain, which fluctuates from 68 to 95%. The main purpose of feature selection is not only for optimal feature subset selection, but also to reveal the hidden pattern of dynamic welding process. It can help us to deeply understand the generation of seam defects. In the end, this technology can improve the identification and effectiveness of on-line detection, thus promoting the industry application.

4 Information Fusion The arc spectral sensing, sound sensing and image sensing technologies mentioned above are the key elements to realize online prediction of welding defects. However, the welding process is a complex dynamic process coupled by multiple fields of light, electricity, magnetism and sound. Moreover, the welding defects are characterized by instantaneity, randomness and invisibility [76], its generation mechanism and development is fuzzy and complex. Specifically, the welding dynamic process is affected by many factors such as material metallurgy and heat conduction, any small change may induce defects. Various types of defects in the welding process, including internal defects and macroscopic defects on the surface, Metallurgical defects, process defects and so on [41, 77] might occur during the dynamic welding process making the current online detection of welding defects lack of effective and efficient and reliable solutions. Also, single sensing technologies have shown certain limitations

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Fig. 19 Weld pool, current, voltage, and sound information of five pulse [79, 80]

in terms of the accurate description and comprehensive characterization of welding quality. Multi-information fusion technology provides a new way to solve the problem of accurate prediction and identification of welding defects, which can provide the complementary information and help the welding robot to make a better and more accurate decision in the dynamic welding process. Information fusion, as a kind of data synthesis and processing technology, is the integration and application of many traditional disciplines and new technologies. According to the fusion level, it is divided into data level, feature level and decision-making level. Some researchers have made valuable explorations on the application of multi-information fusion technology in welding process monitoring [24, 28, 83–86], but methodological scientific results have not yet been established.

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In the book of Hall [77], it provides an overview and introduction to data fusion methods across the broad spectrum and applications. Literature [78] has given a broad review of data fusion application in welding process evaluation, including the detailed algorithm for each level of data fusion. Decision-level fusion can be used to fuse identity from each signal sensor. Several decision-level fusion techniques exist, including voting, weighted decision, and Bayesian inference as well as DempsterShafer’s method [78]. Chen [79, 80] have deeply researched the fusion method by fusing the information of welding arc, welding sound, and visual sensors, as seen in Fig. 19, in pulsed gas tungsten arc welding (GTAW) process to improve the on-line prediction performance. The main weakness of D-S evidence theory [81, 82] is that it cannot solve the conflict evidence from single sensor thus resulting in completely wrong output. Bo Chen proposed a new method to dispose the conflict evidence problem in D-S evidence theory, which can effectively solve the conflict evidence problem, sufficiently fuse different sensors’ information, and obtain more precise results than using a single sensor alone. Zhang [54] investigated the feature level data fusion methodology in order to automatically evaluate seam quality in real time for Al alloy in gas tungsten arc welding. Support vector machine model was established by fusing the feature extracted from online arc sound, voltage and spectrum signals, from which it was reported that the multisensory-based classifier has higher accuracy than single sensor-based one in term of recognizing seam penetration defects. You [83] fused the visual image

Fig. 20 Experiment setup in laser welding and multisensory feature curves [83]

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information of keyhole, visible light and ultraviolet light of laser welding to monitor and detect the defects of the laser welding process with high brightness based on the developed multi-sensor experimental system as seen in Fig. 20. It is found that with the increase of feature dimension, the classification accuracy increases from the lowest 74–90%.

5 Challenges and Discussion On-line defects detection has become more and more important and urgent in some automobile companies, not only for detection of surface defects but also the inner defects, such as porosity, cracks and lack of fusion. In this case, the introduced sensing technologies might not provide the best capability in detecting the inner defects. More direct method, like Traditional Non-Destructive Evaluation (NDE) methods could be utilized in both in-process and post-process applications. Slotwinski et al. [84–86] have presented much efforts to develop an ultrasonic sensing based monitoring system for Additive Manufacturing process control, currently, for qualitatively real-time porosity measurements. And under an AM metal powder bed fusion system, they found a linear relationship between the ultrasonic velocity and the degree of porosity. The proposed ultrasonic measurement methodology, they believe, is sensitive enough to detect small absolute changes (~0.5%) in porosity. Multisensory information fusion can provide a more comprehensive knowledge about the complex welding process. Besides the research content introduced in this paper, other problems should be deeply researched as following: 1. Data correlation Usually, a sensor may observe multiple objects or react to different welding states and defects. Associate observations to sensors can be accomplished by defining a measure of association that quantifies the closeness between observation pairs. Commonly used association measures include correlation coefficients, distance measures, association coefficients or probabilistic similarity measures. 2. Evaluation of sensors Prior to the fusion of any data, evaluation of each sensor should be performed in order to evaluate the importance and redundancy of each type of sensor information. For the particular purpose of defects detection, the high correlation feature to defects might be extracted thus decreasing the redundancy of the monitoring system. 3. Deep learning Deep learning method, such as CNN, DBN and so on, might provide better solution for real-time problems because the robustness and generalization ability of DL algorithm are quite obvious under the condition of large scale of data.

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Acknowledgements The work was supported by the National Natural Science Foundation of China (51605372, 51775409, 61873164), the China Postdoctoral Science Foundation Funding (2018T111052, 2016M602805), the Program for New Century Excellent Talents in University (NCET-13-0461).

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Zhifen Zhang was born in Taigu County, Shanxi province, China, in 1984. She received the B.S., M.S. and Ph.D. degrees in Materials Processing Engineering from Lanzhou University of Technology, GS in 2007, 2010, and Shanghai Jiao Tong University, Shanghai in 2015. Since Oct. 2015, she has been a lecturer with the Department of Mechanical Engineering, Xi’an Jiao Tong University, Xi’an, China. Her research interests include quality monitoring of welding manufacturing, multisensory data fusion and mechanical fault detection.

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Z. Zhang et al. Guangrui Wen Professor of Mechanical Engineering at Xi’an Jiaotong University, received his B.S. degree, M.S. degree and Ph.D. degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 1998, 2001 and 2006 respectively. Dr. Guangrui Wen was a postdoctor fellow at Xi’an Shangu Power Co., LTD. Xi’an, China from 2008 to 2010. He is a member of Chinese Mechanical Engineering Society (CIME) and Chinese Society for Vibration Engineering (CSVE). Dr. Wen has published two books and more than 80 articles. His research interests include Mechanical System Fault Diagnosis and Prognosis, Mechanical System Dynamical Signal Processing and Intelligent Instrument Development, Mechanical Equipment Life Cycle Health Monitoring and Intelligent maintenance.

Narrow Gap Welding for Thick Titanium Plates: A Review Qingjie Sun, Junzhao Li, Yibo Liu and Jicai Feng

Abstract Large and thick titanium alloy structures used in aerospace and marine fields need joint integrity to meet the requirements. Welding technology, an important form of joining materials, is crucial for the application and promotion of thick structural components. This paper reviews the issues and challenges in welding thick titanium alloys, introduces the process characteristics of automated welding technologies, and finally provides recommendations for future work. Research indicates that gas tungsten arc welding method with stable welding process is widely used in welding large titanium structures. As a promising alternative to traditional manufacturing method, laser welding with filler wire has been extensively studied in joining thick structure with the characteristics of narrower groove, high welding efficiency and low heat input. The formation mechanism and suppression measures of welding defects such as lack of sidewalls fusion, porosity, weld deformation and microstructural deterioration are discussed. The future work will focus on the welding process control and parameters optimization in automated welding. Keywords Titanium alloys · Thick plate · Narrow gap · Arc welding · Laser welding · Review

1 Introduction Thick structures have been extensively applied in the field of ocean platform, pressure vessels, nuclear power construction and large chemical equipment. Advanced engineering materials and their application in marine engineering equipment have been found common. The structural components of titanium alloys are extensively Q. Sun (B) · J. Li · Y. Liu · J. Feng State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin 150001, China e-mail: [email protected] Q. Sun · J. Li · Y. Liu · J. Feng Shandong Provincial Key Laboratory of Special Welding Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-8668-8_2

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used despite their high reactivity due to their good corrosion resistance by the formation of tenacious protective oxide film, no loss of toughness at low temperatures, good creep, oxidation resistance, high strength to weight ratio and fine weldability [1], so it is necessary to propose effective technologies to process thick titanium alloys. Figure 1 shows the application of thick titanium plate on all-position welding manned capsule. Welding technology is a promising approach to optimize product design and minimize production cost, which is crucial for promotion and utilization of thick structures. The most common processes used for welding of thick plates include gas tungsten arc welding (TIG) [2–4], gas metal arc welding (GMAG) [5, 6], shield metal arc welding (SMAW) [7], electron beam welding (EBW) [8, 9] and laser beam welding (LBW) [10–12]. The weld characteristics such as weld quality, microstructural revolution, welding defects and their control measures have been widely studied by researches. In welding these thick materials, traditional weld methods require more filler wire to fill the large angle groove, causing a large welding deformation, wider heat affected zone and thus low joint properties. In contrast, narrow gap welding method adopts a small angle groove, which can decrease the amount of filler wire, reduce weld passes and improve welding efficiency. Amongst numerous welding technologies, TIG, LBW and EBW are generally employed in welding titanium components. High power source such as LBW and EBW is adopted to achieve deep penetration welding with no filler wire, high welding efficiency and lower heat input. This generally results in a less coarse grain structure and superior mechanical properties. However, for thick welding structures the mechanical properties may vary significantly along the thickness direction due to the heterogeneity in microstructure, and have been demonstrated to be affected by the selected welding process parameters and the geometry of the actual joints [9]. On the other hand, the requirements of high power source and vacuum environment restrain its practical application. Another method is the combination of low power source with narrow gap welding with filler wire to achieve multilayer welding, which can regulate the weld microstructure and

Fig. 1 The application of thick titanium alloys

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improve joint properties [13–15]. This technology with characteristics of low cost, higher welding productivity and low welding heat input is considered to be the most suitable way for welding thick plate. The process control and defects suppression for narrow gap welding are strict to obtain free-of-defect joint. Table 1 compares the weld characteristics of various processes for welding thick plates. The processing of titanium-based alloys is limited due to their chemical activity, higher strain hardening and weld cracking tendency. Titanium will burn in pure hydrogen at 250 °C, in oxygen at 400 °C and in nitrogen at around 600 °C. Oxygen and nitrogen will also diffuse into titanium at temperatures above 400 °C, raising the tensile strength but embrittling the weld joint. Besides, hydrogen can exist in the titanium lattice in the form of interstitial solid solution and some hydride and deteriorate the joint quality [22, 23]. The tendency towards weld cracking, poor impact and ductility properties in fusion zone makes them vulnerable for welding. Moreover, the properties of titanium alloys with high melting point, poor heat conductivity and large viscosity are conducive to the formation of larger pool while welding, which cause grain growth, poor impact, ductility properties and weld cracking [1]. Therefore, welding of titanium alloys is crucial to control heat input to avoid welding defects that deteriorate weld properties.

Table 1 The comparison of various welding process for narrow gap welding [2, 4, 6, 7, 16–21] Welding technology

Features

Modifications

Applications

NG-TIG

Stable welding process; High weld quality; Low welding efficiency

Magnetic assisted NG-TIG; Tungsten vibration NG-TIG; Two electrode NG-TIG

Titanium alloys; Stainless steel

NG-GMAG

High welding efficiency; Large heat input; Wider HAZ; Large welding deformation

Rotation arc welding; Twin-wire narrow gap welding

Low-carbon steel; Aluminum alloys

NG-SAW

High welding efficiency; Poor welding adaptability; Slag inclusion; Low weld quality

Vibration assisted welding; Twin-wire narrow gap welding

Medium and low strength steel

NG-LBW

High welding efficiency; Low welding deformation; Excellent weld quality; Porosity defect

Laser beam oscillation welding; double laser beam welding

Titanium alloys; Stainless steel

Ultra NG welding

High welding efficiency; Narrower weld groove; Slag inclusion

Ultra narrow gap laser welding; Ultra narrow gap arc welding;Flux strips constraining arc

Low-carbon steel; Stainless steel

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The review of available literature in the area of experimental study on narrow gap weld characteristics of various welding method is discussed. The issues and challenges in welding thick titanium alloys are summarized and corresponding measures are proposed to provide ideas for further research and development.

2 Current Development Trends 2.1 Narrow Gap GMAW Welding Narrow gap welding can achieve a weld with small cross section on a thick plate. Arc welding process with high efficiency and quality has been widely applied for manufacturing heavy components. For normal gas metal arc welding (GMAW) process, a welding wire was stretched out straight from the central of contact tip, thus the penetration into bottom bead increased and the melting of groove sidewalls suppressed due to the concentration of arc heat and force into the bottom plate. This phenomenon may cause finger-shaped penetration and lack of sidewall fusion defects [24]. However, GMAW welding process is suitable for thick plate with the advantages of large deposition and high welding efficiency. Ensuring enough penetration into the groove sidewalls is the key point when welding large-scale and thick-wall structures for GMAW welding. Narrow Gap Metal Active Gas arc welding system has a high manufacturing efficiency and is applied to weld AISI 316LN thick plate used in International Thermonuclear Experimental Reactor (ITER) device [25]. The less compositional segregation and smaller dendrite size due to the lower level of heat input in NG-MAG arc welding were considered as the critical role in enhancing the cryogenic toughness, which could be comparable with that of the TIG arc welding. The shielding gas composition on arc properties and wire melting characteristics in narrow gap MAG welding was studied. Adding helium in shielding gas presented a bowl weld bead profile. The depth of sidewall fusion increased as helium content increased [24]. The nozzle head was also designed to accommodate the ultra-narrow gap. The groove wall of thick section was up to a limit and P-GMAW process with vertically placed electrode depositing single bead per layer in weld groove was employed. A defect free ultra-narrow multi-pass weld was produced [26]. Several NG-GMAG approaches such as rotation arc, twist and snake wires processes have been proposed to increase enough penetration into groove sidewalls, which can effectively solve the lack of fusion defects and have widely applied in practice [17]. The schematic diagram of NG-GMAW system is shown in Fig. 2. The modified movement of arc can increase the penetration into groove sidewalls and simultaneously decrease the sectional thickness, thus improving weld quality and avoiding the occurrence of the finger-shaped penetration into bottom bead. The key process variables from torch structure and process parameters were optimized by Wang et al. [27] that the penetration depth of groove sidewalls and surface curvature of weld increased with the

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Fig. 2 Rotation arc system for NG-GMAW welding [17] (D: rotation diameter; N: rotation rate)

increase of swing frequency and sidewalls staying time, while the weld sectional thickness decreased and bottom shape of weld bead varied from single to two peaks. NG-GMAW process has been successfully used for welding thick steel and aluminum alloys. In order to improve deposition rate, the process stability of twin-wire narrow gap welding in a single pool was investigated [6]. The results showed that the excellent appearance of weld of twin-wire narrow gap welding was formed. When the wire feeding rate was over 10 m/min, there was no undercut formation of sidewall. A narrow gap welding model and an arc force model were established to illustrate the influence mechanism of welding parameters on process stability. With the decreasing of the distance between wires and edge, the welding process stability decreased. In order to maintain the welding stability, the distance between wires and side edges must be greater than 2.5 mm. However, GMAW process has poor welding stability and large heat input compared to TIG welding process. The welding of titanium alloys is more focused on joint reliability and quality rather than welding efficiency, so there are few researches on NG-GMAW welding of titanium alloys.

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2.2 Narrow Gap TIG Welding Tungsten inert gas (TIG) welding technology is a very versatile metal joining process with stable welding process and low heat input, and is well suited for welding of titanium alloys that is sensitive to heat input. Due to its small molten pool, the lack of sidewalls fusion was obvious and impeded the development of TIG narrow gap welding. Han et al. [28] used TIG welding process to join 15 mm TC8 by U, V and X-shaped groove. The groove geometries mainly affected the heat distribution, thus changing the microstructural evolution and joint strength. The results showed that the weld zone structures of U and V grooves were basically same, the columnar β phase, acicular and granulate α phase were obtained. The structure of X groove was columnar β phase with dispersed equiaxed and acicular α phase, and the grain size in the X groove was the smallest. The tensile strength and elongation of the joint of U groove were the lowest, while those of X groove were the highest. The fracture of U groove was mainly characterized by brittle fracture, and the fracture of X groove showed the characteristic of quasi cleavage fracture. Advanced fusion reactors like ITER with higher thicknesses like 40 and 60 mm was successfully welded using multi-pass, multi-layer TIG welding process [29, 30]. The defect free joint was achieved, but the welding efficiency was low due to the large weld groove. Cook and Levick [4] proposed the hot wire TIG welding process to solve the lack of sidewall fusion defects, avoid the formation of welding cracking and improve weld efficiency. However, the occurrence of magnetic arc blow due to wire current made the welding process become unstable. Pulsed current was proposed to preheat the filler wire to avoid the magnetic blow [31]. The magnetic blow produced during only the wire pulsed current period and no magnetic blow occurred during non-pulse period. The ratio of the magnetic blow period became reduced and the workability was not so much damaged as the ratio of this pulse period decreased. The interaction of magnetic field and moving electrons can induce Lorentz force, affecting welding arc morphology and thus distributing the heat energy on groove sidewalls [32]. Magnetic arc oscillation [33] could increase weld width and the sidewalls penetration obviously increased. In 1971, Tseng and Savage [34] firstly studied the electromagnetic stirring to refine the microstructure and improve mechanical properties. As the research continues, they found the introduction of magnetic field into narrow gap welding had a great promising prospect [35]. Magnetic assist TIG welding torch and arc feature are shown in Figs. 3 and 4. It can be seen that welding arc under the magnetic field transfers to the groove sidewalls, which can inhibit the incomplete fusion of sidewalls. Ukraine Paton Electric Welding Institute firstly developed the magnetic assisted TIG narrow gap welding device, and the magnetic field can effectively control the welding arc in the groove and redistribute the arc heat to increase sidewalls penetration. This technology needed reasonable and standard welding process parameters and high precision assembly. Yu et al. [3] used magnetically controlled narrow-gap welding technology to weld the 30 and 100 mm thick TC4 titanium alloy. The welding arc under the magnetic field can achieve lateral oscillation periodically. The

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Fig. 3 Magnetic assist TIG narrow gap welding head [33]

Fig. 4 Arc profile under different welding forms: a Non-magnetic field; b Magnetic arc welding without narrow groove; c Magnetic arc welding in narrow groove [33]

external magnetic field can effectively avoid the problem of poor sidewall fusion and achieve joints with good fusion sidewall by oscillating the welding arc. The arc swing and electrode position greatly affected weld formation and process stability. A double magnetic pole was adopted to prevent insufficient sidewall fusion and improve efficiency and quality for thick component welding [33]. Magnetic arc oscillation resulted in the change of arc voltage and welding current flowing through both sidewalls, which in turn caused the redistribution of arc heat. The magnetic field made the welding arc swing, which brought more heat into the sidewalls and ensured the sidewalls penetration. The results showed groove sidewalls can completely melt and excellent joint was formed.

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Moreover, Kobayashi et al. [36] developed a high efficiency TIG welding method, which employed two electrodes within a single torch. This process produced consistent penetration in the groove wall and high deposition rate, making it well suited for application to the construction of large capacity LNG storage tanks and thick walled pressure vessels. Meanwhile, proper welding condition for vertical and horizontal welding position (peripheral welding position) was selected, and sound joint properties were confirmed. Figures 5 and 6 show the arc features and current control for different welding position. Using this technology, a 190 mm thick titanium plate was successfully welded. Moreover, twin wire process was developed to increase the deposition rate. The weld with better quality cladding of less dilution and smaller HAZ was obtained, which also had potential for welding thick plates [37]. Lassaline and North [38] reported the deposition rate of twin wire was higher than single wire welding and the lack of fusion defect was solved. However, the large of groove side limited the application range of this method. Rotating tungsten electrode was designed for narrow-gap welding, as seen in Fig. 7 [16]. The periodically rotating arc can improve sidewall fusion and keep constantly stirring the molten pool. The sufficient fusion of sidewalls was ensured in narrow gap welding at vertical position. It is generally that the narrow gap arc welding often requires a relative large groove and complex welding device, which cause a low welding efficiency and have potential to form lack of fusion defects. On the other hand, the large residual stress, deformation and undesirable joint strength have become the main bottleneck that restricts the development of narrow gap welding technology. Therefore, further compression of groove width and introduction of high energy beam heating source are necessary to improve welding efficiency and quality.

Fig. 5 Schematic illustration of twin electrodes [36]

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Fig. 6 Pulse current control for different welding position [36]

2.3 Narrow Gap Laser Welding with Filler Wire Compared to traditional arc welding technologies, laser heating source presents many advantages for the welding of thick plates, such as energy density concentration, rapid welding speed, narrower heat-affected zone and low thermal distortions [39]. However, some inferior of narrow gap laser welding such as small laser spot, the align-ability of laser and wire/arc, welding splash, also need to be solved immediately. Experimental system of hot wire laser welding is shown in Fig. 8. Narrow gap laser welding with filler wire is a technology that combined the advantages of narrow gap weld and laser weld, and is considered as the most suitable method for thick plate welding [40]. This technology has the advantages of

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Fig. 7 The arc characteristics of rotating tungsten TIG process [16]

Fig. 8 Experimental system of hot wire laser welding [49]

low filling volume, narrower heat-affected zone and lower power source, which can effectively improve the weld quality of thick plate. One disadvantage of the laser welding technique is that it is associated with relatively high cooling rates, which resulted in increased risks for cracking and higher microhardness when compared with arc welding techniques [15, 41]. In order to solve this welding defect, some researchers suggested that preheating was used to prevent the formation of solidification cracking. Preheating could relieve the restraint of the surrounding structures, reduce the thermal gradient in the weld, and slow the cooling rate. Besides, Karhu and Kujanpää [42] used a large heat input in the following pass to remove the hot

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cracking. However, this method had a tendency to deteriorate the weld microstructure and wasted a large amount of heat input. Elmesalamy et al. [43] reported that compared with arc welding, the number of weld passes for narrow gap laser welding with filler wire addition decreased from 43 to 20, when welding 20 mm thick AISI grade 316L stainless steel plates from both sides. And the residual stresses were generally 30–40% lower in magnitude for the narrow gap laser welds in comparison to those for GTA welding. However, the introduction of filler wire made the welding process become complicated. Process parameters such as laser power, welding speed and wire feeding speed, and their interactions are significant to control the weld quality in ultra-narrow-gap laser welding. The statistical modeling and multivariable optimization were used to eliminate voids and lack-of-fusion defects [14, 44, 45]. The process parameters optimization is shown in Fig. 9. The interaction between input parameters and geometries of single bead was studied. The optimized welding parameters were obtained for multi-pass narrow-gap laser welding and the joint with fewer defects was achieved. Phaoniam et al. [46] developed a highly efficient hot-wire laser hybrid process for narrow-gap welding and found the hot-wire laser welding was able to produce complete weld deposition with very low dilution of the base metal. The laser energy reflected from the molten pool was crucial to achieve the melting of side groove wall. The process stability and parameters optimization were studied to understand welding features [47]. Imperfections that have to be avoided are hot cracks, cavities, lack of fusion, and an irregular final weld surface topology. The favorable wetting condition might be achieved by electrically preheating the wire and this technology shows high potential [48]. Furthermore, the hot wire increased the stability of the welding process and improved weld seam formation [49], while reducing the total energy input during the welding process and then increasing the efficiency of energy usage. In the hot-wire laser welding process, a maximum energy savings of 16% was realized over cold-wire laser welding. Figure 10 shows the groove size and cross-section of welded joint of laser welding with filler wire.

Fig. 9 The process parameters optimization for laser welding with filler wire

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Fig. 10 The cross-section of welded joint by laser welding with filler wire [10, 40]

In order to overcome the problem of lack of fusion, Yu et al. [12] found that the lack of fusion defect can be suppressed using the relatively small groove because more laser beam energy can be used to fuse the side wall of groove and augment the size of molten pool. They adopted modified nozzle that can inside the groove, the capillary force can be balanced by the pressure from the side shielding gas, and so the concave meniscus surface in the bottom of weld can be avoid. The concave meniscus surface was beneficial to form defect-free joint. Laser oscillating welding and its effectiveness in narrow-gap welding also were investigated [50], as seen in Fig. 11. The vicinity of the bottom of the gap was directly heated by the oscillating laser welding. However, some wire fragments may fall in a solid state into the molten pool, which caused the unstable welding process and wire component segregation. Laser beam interference was avoided by the top edge of the gap (the test piece front surface) when the laser beam oscillation width was lower than the gap width −0.5 mm. They suggested oscillating laser welding was effective as a heat source for narrow-gap welding and a heat source such as hot wire may be used to melt the wire. The laser oscillating beam can widen weld width and shallow weld penetration, which was preferable for narrow gap welding to avoid a lack of groove sidewall fusion and finger-shaped penetration [51, 52]. On the other hand, the beam oscillation improved the weld morphologies and promoted the formation of equiaxed grain with in the fusion zone due to stirring effect. The ductility was increased by the decrease of weld morphological defects and the increase of equiaxed grains [53]. Li et al. [54] also found that the laser beam oscillation can effectively improve the stability of welding process and suppress the formation of pores.

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Fig. 11 The effect of oscillating laser on bead surface width [50]

Dahmen et al. [55] welded 20 mm plate by dual laser beam welding and the welded joint without incomplete fusion and porosity was formed. The dual-beam laser welding can be flexibly adjusted the welding temperature and weld appearance by changing interbeam spacing and the ratio of laser power. The cooling rate of dualbeam laser welding was greatly lower than that of single-beam process, which was helpful to improve weld microstructure [56–58]. The effects of beam configurations on wire melting and transfer behaviors in dual beam laser welding with filler wire was studied and showed that the transfer stability of side-by-side configuration with the minimum transfer period and critical droplet size was better than the other two configurations [59]. The precondition using oscillating beam and dual beam in narrow gap welding was to avoid the interruption between laser beam and groove sidewalls and ensure stable welding process. Wang et al. [60] adopted YLS-5000 W fiber laser to weld 12 mm TC4 plate by the multiple-pass narrow-gap approach. The results showed that incomplete fusion and porosity were the main defects during laser welding titanium alloys, and the optimized process parameters could improve joint quality. Interestingly, compared to TIG welding process, the welding efficiency by laser welding was improved significantly. The microstructure of fusion zone (FZ) was composed of β columnar crystal and basket weave martensite α phase. The grain size of heat affected zone (HAZ) was obviously refined. The HAZ partially transformed was made up of martensite α phase, transformed α and transformed β. Moreover, the microhardness of weld metal and heat affected zone was higher than that of base metal, and the hardness reached to peak value in the heat affect zone near weld metal (Table 2). Laser welding with narrow gap process is considered as the most suitable for welding thick structure. The above studies proved that the excellent welded joint with no defect and good mechanical properties can be obtained.

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Table 2 The research status for narrow gap laser welding with filler wire Authors

Materials

Thickness (mm)

Weld parameters Laser power (kW)

Welding speed (m/min)

Wire feed speed (m/min)

100

[10]

SUS304

4.5–5.0

0.6–1.0

2.0

[11]

316L

50

8.0

0.4

6

[15]

Ferritic steel

30

7.5–8.0

0.4–0.5

5–6

[40]

Q345D

70

8.5–10

0.3

3.5–4.5

[41]

9Cr1Mo steel

21

3.0

0.3

2.3–6.8

[43]

AISI 316L

20

0.95

0.59

1.08

[45]

AH32 steel

20

4.0

0.5

4

[60]

TC4

12

2.5

1.2

0–4

2.4 Narrow Gap Laser-Arc Hybrid Welding Laser-arc hybrid welding combined the advantages of laser and arc heating source, such as high energy density, stable welding process and good welding adaptability. Laser-MIG/MAG hybrid welding process is the mostly adopted method to weld medium plate with characteristics of high welding speed, deep penetration depth, fine weld appearance and lower assembly accuracy. Though large weld depth can be achieved by laser heating source, the hybrid welding process has a low adaptability for thick plate. The weld penetration depths of narrow gap laser-arc hybrid were 10–22% higher than that of the welds under open space due to the space constraint effect. The constrained space increased the heat transfer efficiency by reducing heat loss and enhances the flow intensity of shielding gas, which suppressed the expansion of laser induced plasma and enhanced the absorptivity of laser energy. Li et al. [13] compared laser wire filling welding and hybrid laser-GMAW welding of 30 mm thick plate using a multi-layer, multi-pass process. The results show that slag and porosity defects occurred often and their position located adhered to some rules during narrow gap laser welding with filler wire. Complete cleaning of welding bead can reduce the occurrence of these defects. The laser-arc hybrid welding process obviously improved these defects because the small narrow groove decreased the droplet transfer frequency or made the droplets adhere to the side walls. The welded strength and microhardness of laser-MAG joint was higher than the base metal [61]. The welding speed could reach to 9 m/min. The hybrid welded joints had the good combination of strength and ductility. Li and Liu [62] studied the laser-TIG welding of titanium alloys. The welding seam was made up of α -phase and β-phase. The acicular α was loose and disheveled because of the stirring effect of pulsed laser and pulsed TIG. The contents of Al and Mn were maintained steadily and there was no component segregation.

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The groove shape, size and processing parameters greatly affected the weld quality of hybrid laser-arc welding. The maximum weld depth of hybrid welding process was determined by the laser power. Most research works within laser welding of thick section steel are so far limited to thicknesses in the range up to 35–40 mm, primarily based on CO2 lasers up to approximately 20 kW. Within the development of ultra-high power source, disc-laser systems up to 32 kW and fiber laser systems up to 100 kW have entered the scene and is now basis for laser hybrid welding research related to even thicker section welding up to approximately 100 mm. Nielsen [63] reported the challenges and perspectives of high power laser hybrid welding for the production of heavy metal constructions. The main problem of laser hybrid welding process for thicker structure was defect control, which means the elimination of cracks and reduction of porosities. This was solved by the addition of filler wire as well as by optimum joints geometries. 40 mm structural steel was achieved by 32 kW disk laser-MIG hybrid welding with I-butt joint from two sides (Table 3). Su [64] studied the microstructure evolution and mechanical properties of laserMIG hybrid welded joint and found the thickness of martensite increased from 0.49 to 0.82 μm with increasing of welding heat input. During multipass welding, the number of thermal cycles between different layers and remelting that the weld experienced was more, thus the tensile strength of the weld was stronger and the impact resistance was lower, and fracture mode was hanged, from ductile fracture to mixed fracture. Cao [65] found that compared to single TIG welding, laser-TIG hybrid welding of titanium alloys could refine the crystals and increase the acicular α phases inside the grains of fusion zone, but the width of heat affected zone in hybrid welding were slightly larger than that in TIG welding, as seen in Fig. 12. Butt welding of titanium plates was achieved with optimized parameters in three types of thickness (12, 15

Table 3 The comparison of narrow gap TIG welding, laser-MIG welding and laser welding with filler wire for thick TC4 plate [3, 60, 64] Welding process

Groove type

Welding layers (No.)

Welding speed (m/min)

Narrow gap TIG welding

5

0.4

Laser-MIG welding

4

0.5

Laser welding with filler wire

5

1.2

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Fig. 12 The automatic wire-feeding experimental equipment [65]

and 18 mm) by hybrid welding. All the tensile strength of welds can be as high as 95% of base metal. However, as the thickness of plate increases, the plasticity of the joint decreased. This is because the heat accumulation by multipass welding led to the growth of grains size in weld zone. Due to the several advantages of laser-arc hybrid welding process, such as high welding efficiency, all-position welding and better weld quality, makes it an attractive and alternative for industries application [66]. The hybrid laser arc welding can be used to weld thin and medium plate using less number of weld passes, which reduces the weld distortion [67]. The major applications of laser arc hybrid welding are found in automobile and ship building industries where a large number of metallic components are welded to build light or heavy vehicles and ships. However, for large thick plate welding, it is generally employed up to a thickness that can be welded in single pass. The weld penetration mainly depends on the laser power, limiting its application for welding thick plates. Figure 13 compares the cross section of TC4 joint with different welding processes.

3 Issues and Challenges in Welding Thick Plate 3.1 Lack of Fusion During thick plate narrow gap welding, the lack of sidewall fusion defects greatly deteriorates joint quality and should be avoided. The occurrence of this defect can be attributed to the unstable welding process or large groove gap. The solidification rate of fusion zone is quicker and the fluidity of liquid metals is poor, causing the deposited metal cannot completely cover the groove. The melted parent metals and deposited metal are hard to mixed, resulting in the lack of fusion defects [68]. This defect greatly associates with the groove parameters, welding parameters and material properties. On most of case, this defect is formed at the interface between adjacent layers. The filling metal that is not re-melted during the following build-up welding leads to

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Fig. 13 The cross section of TC4 joint: a narrow gap TIG welding; b laser-MIG welding; c laser welding with filler wire

these defects formation. Besides, it also appears at the side wall between filling metals and the base plates. The heating source cannot effectively melt the groove sidewalls which cause the lack of sidewall fusion defects. Based the experimental results [69], the concave solidified surface of deposited metals was the most beneficial way to eliminate defects. A quantitative model based on dimension analysis method was established to predict lack of fusion within the welds. Increasing the downward flow within the molten pool can form concave solidified surface by the constraint of groove. On the other hand, decreasing the solid-liquid surface tension at the edge of molten pool, which can reduce the surface wetting angle between groove side wall and molten pool, and then cause a deeper concave on weld surface. In order to overcome this welding defect researchers have proposed some novel methods such as modified shielding gas composition [24], narrow gap arc oscillation welding [17], magnetic assisted TIG narrow gap welding [33], laser beam oscillation welding [19] and ultra-narrow gap laser welding [26, 44], et al. The common point is that these modified methods can redistribute welding heat to the side groove or reduce the groove width. With the increasing of heat input on the sidewalls, the wetting ability between sidewall and weld pool is enhanced to improve the surface morphology. Through optimization the welding process parameters, the defect-free welded joint can be achieved.

3.2 Porosity The porosity defect is more likely to form during welding titanium alloys due to the existence of hydrogen, contamination and improper process parameters. Huang et al. [70] studied the mechanism of porosity formation during the fusion welding

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of titanium and its alloys. They found that the pores in the welds of titanium alloys were circular and had smooth inner surface, suggesting the porosity formation was associated with gas revolution. A hydrogen diffusion-controlled bubble growth model had been proposed, the thickness of the liquid film at the melting front was crucial for bubble nucleation and bubble survival in the weld pool. Many researches indicated that the main problem of narrow gap laser welding was weld porosity. The porosity may be of one or a mixture of two types: firstly micro-porosity formed within the arms of the dendrites during solidification and secondly, larger pores that often align themselves along the weld centerline. Tsukamoto [71] emphasized that the behavior of keyhole during high-energy beam welding was critical for the porosity formation. Keyhole instability can sometime induced porosity, while the mechanism of keyhole instability was very complex and still not sufficiently understood. During narrow gap laser welding with filler wire in solidification stage, the gas in the molten pool constantly diffused into the bubbles, resulting in the increase of bubble pressure and forming large bubbles. With decreasing temperature, the solidification rate was larger than the floating rate of gas, therefore, parts of bubbles was unable to escape from the molten pool and then formed porosities in the weld metals [68]. The formation of porosity had a great relationship with the parameters and distributed random in the welded joints. This was called metallurgical porosity, which was distinguished from the porosity resulted from unstable keyhole [71, 72]. Figure 14 shows the defects of laser welding TC4 plate. The negative effect of pores may lead to the concentration of stress and reduce the effective area for mechanical performance [73, 74]. With an objective of achieving safer weld with greater longevity and integrity, researchers have also suggested some strategies for the suppression of pores. There are several practical approaches which are available to reduce and eliminate keyhole instability-induced porosity, including process optimization [69, 75], the application of reduced ambient pressure condition [76] and beam modulation [77]. It is generally that the hydrogen and cleanliness

Fig. 14 Lack of fusion and porosity defects in TC4 laser welded joint [60]

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is therefore crucial in eliminating the metallurgical porosity. Before welding, the substrate and filler wire must be thoroughly degreased. Besides, the highest purity shielding gas must be used.

3.3 Microstructures Deterioration and Welding Deformation During narrow gap welding processes the titanium alloys experience fierce diffusion and solidification process, which generate complex stress condition and cause severe deformation and even cracking. Cold cracking may occur at the weld zone and HAZ area. The cracking is intergranular and has been thought to be partly the result of volume changes during the beta to alpha phase change coupled with the reduction in ductility. Strict controlling the content of hydrogen and vacuum annealing treatment are effective ways to prevent cracking [78]. On the other hand, the welding process parameters, welding consumables and protective measures greatly affect the weld quality. The narrow solidification range and transformation kinetics for titanium alloys result in large grains in the fusion zone [79]. The microstructure and welding defects restrict the welding of large titanium alloys components. Residual stresses are developed during the final stages of cooling as the weld contracts and the distribution of residual stresses greatly affects the fatigue properties of joint [80, 81]. The structure may become significantly distorted according to the degree of constrain, which hinder further welding process or assembly. A number of techniques are used to restrict the development of weld deformation, including reducing the heat input [82, 83], optimizing the groove size [28, 84], welding and machining sequence [85], trailing ultrasonic vibration [86] and use of heat treatment [87, 88]. Among these methods, reducing the heat input and optimizing the groove size are most commonly used to minimize the weld deformation during welding thick plate. Titanium and its alloys are prone to atmospheric contamination and grain growth with increasing temperatures, both are deleterious to mechanical characteristics of the welded joints. The microstructure of welded joint is greatly depended on the cooling rate [89, 90]. The cooling rates from 525 to 1.5 °C s−1 resulted in martensitic, massive and diffusional phase transformations. Cooling rates above 410 °C s−1 led to a fully martensitic microstructure, a massive transformation being observed between 410 and 20 °C s−1 , and this transformation being gradually replaced by diffusion controlled Widmanstatten with decreasing cooling rate, as shown in Fig. 15. Qi et al. [91] compared electron beam welding, laser beam welding and gas tungsten arc welding of titanium sheet. TIG welding had many defects such as wide weld-seam, big deformation and coarse grains, while LBW had the narrowest weld-seam, the least deformation and the finest grains. The fine grains were good for properties of weld seam. The nature of the transformation depended principally on the cooling rate and the transformation characteristics of the alloy during welding process [92, 93]. The thermal cycle at particular location depended on the welding process and process parameters used the location in relation to the weld centerline. In general, for titanium alloys the HAZ zone can be divided into the near HAZ and the far HAZ by the β

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Fig. 15 Schematic CCT diagram Ti-6Al-4V [89]

transus temperature. The temperature of near HAZ is approximately higher than the β transus temperature and less than the liquidus temperature, while the temperature of far HAZ is less than the β transus temperature. Therefore, the HAZ experienced various thermal cycle, causing the microstructure presented gradient features and forming a series of unbalanced phases. When the temperature of fusion line is higher than β transus temperature, the martensite or the Widmanstatten structure formed due to the faster cooling rate. Far away the fusion line, massive recrystallization occurs and the grain size is not uniform, leading to the fluctuation of mechanical properties [90, 94–96]. On the other hand, the welding heat input varied and changed the heating rate, holding time and cooling rate in the HAZ with welding processes, which caused the different proportion and morphology of microstructure. There is, therefore, a big microstructural difference between HAZ and base metal and especially the near HAZ is weakest zone of welded joint. The fusion zone grain size of multipass welding is roughly proportional to the number of passes, because the heat accumulation and the second thermal cycle remelts the solidification structure [65, 97]. A number of approaches are used to reduce the columnar size, such as pulsed alternating current [98, 99], magnetic oscillating [100, 101], vibration [102, 103] and inoculants [104]. These methods can increase the ductility and fatigue of fusion zone.

4 Conclusion The welding of large and thick components, especially titanium alloys, is the key technology to realize lightweight and integrated manufacturing in the aviation, aerospace and navigation field. With the rapid development of structural component and performance, the difficulties of welding large titanium structures are as follows: the increasing of component thickness, the complexity of welding structure and the high requirement of welding quality and performance. From the current status of research

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for narrow gap welding, the challenge of narrow gap welding of titanium alloy is mainly to solve welding defects, control welding stress and improve efficiency. The following conclusions and future scopes are drawn: 1. TIG welding is probably the most common process to weld titanium alloys due to its low heat input and welding stability. However, the low welding efficiency, complex welding device and much welding consumables limit its application. Laser welding with filler wire can obviously raise welding efficiency with narrower gap, however, the welding stability need to be improved to avoid welding defects. Laser narrow gap welding technology has wide application prospect in welding large scale structures. 2. Improving the reliability and service life, continuous efforts are needed to avoid the failure of welded joint by minimizing the welding defects. Suggestion such as redistribution of heating source and stable welding process is crucial to optimize the weld geometry. Further study the formation mechanism of defects and microstructural evolution is need to improve welding quality, and corresponding measures need to be proposed and further verified. 3. During narrow gap welding, the real-time monitoring and control of welding process in the groove are necessary to optimize welding process and achieve automatic welding in practical industrial manufacturing. Acknowledgements This project is supported by the National Key Research and Development Program of China (2016YFB0300602) and the National Natural Science Foundation of China (51475104).

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Q. Sun et al. Qingjie Sun professor, Ph.D. student advisors, School of Materials Science and Engineering Harbin Institute of Technology. Professor Sun mainly engaged in research on efficient automatic welding technology, including (1) Efficient automatic welding method and equipment; (2) Arc behavior and application with composite energy field; (3) Manufacturing method and interface behavior of heterogeneous metal additive. In recent years, 30 SCI papers and 20 EI papers have been published in mainstream academic journals in the field. Professor Sun have presided over a number of national scientific research projects.

Part II

Research Papers

Thermal Analysis of Belt Grinding Process of Nickel-Based Superalloy Inconel 718 Xukai Ren, Baptiste Soulard, Junwei Wang, Yanling Xu and Xiaoqi Chen

Abstract Development of automated machining robots has gained momentum with many well-known processes realizing automation to improve quality and increase efficiency. However, grinding process is difficult to automate. High temperature at the contact between the grinding wheel and the workpiece is the source of thermal deformation and phase transformations which can lead to thermal damage and affect surface integrity. This study aims to achieve a better understanding of the thermal behaviour of the grinding process in order to develop an automated grinding robotic system. We studied the thermal behaviour of Inconel 718 workpiece during the grinding process. Different models have been established previously to characterize the heat generation and dispersion at the interface between grinding wheel and workpiece. The Rowe model is used to calculate the heat escaping from the workpiece, by convection, and convection with the grinding wheel in order to obtain the heat remaining in the workpiece. Once the heat source intensity is calculated, Jaeger’s model for moving heat sources is used to calculate the evolution of the temperature along the length of a bar being ground and along the depth in steady state. A numerical model is created using ABAQUS to take into account non-linearity such as the temperature dependence of the parameters and complex boundary conditions. An experiment is carried out on an Inconel 718 bar to compare with the analytical and numerical results. The results show that the boundary condition and the temperature dependence properties of Inconel 718 cannot be neglected. The temperature profiles obtained by the numerical model, which includes the boundary condition and temperature dependence properties of material, are more consistent with the experimental results. Keywords Belt grinding · Thermal analysis · Inconel 718 · Numerical model · Temperature profile X. Ren · B. Soulard · J. Wang · Y. Xu · X. Chen Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China X. Chen (B) Manufacturing Futures Research Institute, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC 3122, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-8668-8_3

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1 Introduction In order to study the thermal comportment of the workpiece, we first need to know the heat input, and thus, from mechanical energy, what amount of energy is converted to thermal energy. Shaw and Ramanath [1] modeled heat generation during grinding process. This model is simplified, the heat removed by the chips is not considered and the heat distribution is uniform. In this model, the temperature is uniform on the grinding area. The energy partition coefficient takes into account the thermal properties of the grinding wheel and of the specimen material only. Outwater [2] improved this model by adding the component of the heat take away by the grinding chips. Later, this model has been modified by Hahn to finally have a final partition coefficient which take into account the properties of the grain and the grinding speed. Another model for grinding heat behaviour was developed by Malkin [3, 4] and Kohli et al. [5]. Malkin’s model used the contribution of chip formation, ploughing and sliding to calculate the energy partition ratio. For them, all the sliding energy at the contact between the workpiece and the wear flats of the grains is conducted to the workpiece as heat. Because the ploughing energy generates a deformation without material removal, it also transmitted to the workpiece as heat. Approximately 55% of the chip formation energy is transferred to the workpiece. In brief, except 45% of the chip formation energy, all the grinding energy is transferred to the workpiece as heat energy [4]. Rowe et al. [6–8] created a new model, described in the heat partition with four different heat fluxes. Rowe et al. studied this problem and describe it in the book “Principles of Modern Grinding Technology (second edition)” 2014. In their model, the heat energy is divided in four heat fluxes, namely a part dissipated in the fluid, carried away by chips, stays in the grinding wheel and remains in the working piece, as illustrated in Fig. 1, q is the total energy, qw represents the heat goes into workpiece, q f represents the heat goes into fluid, qs represents the heat dissipated by grains, and qch represents the heat goes into chip respectively. Rowe et al. introduced the work-wheel fraction R was to characterize the part of heat which goes into the chips by passing through the workpiece and the rest which is shared between wheel and workpiece, even if some goes into the fluid. This coefficient is defined as following.

Fig. 1 Representation of the heat partition model

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Rws =

qw + q f qw + q f + qs

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(1)

And so, the heat flux in the workpiece is qw = Rws · (q − qch ) − q f

(2)

The mathematical model developed for this coefficient takes different values approximately from 50% for a super abrasive process to 85% in case of a conventional abrasive process in the steady state.  Rws = 1 +

kg √ βw · r0 · vs

−1 (3)

where, k g is the grain conductivity, and βw is the wheel thermal property: βw =



kw · ρw · cw

(4)

r0 is the grain radius and vs is the wheel speed. In this study, we will use the Rowe et al. model to determine the heat flux entering the workpiece. In this model, we assume that all the energy entering the system is converted to thermal energy. In fact, almost all the grinding power goes into the contact zone as heat, a negligible part accelerates the chips and another small portion is locked into the deformed material. Once the heat energy is known, it becomes interesting to know what part of this thermal energy flows into the surface of the workpiece and generate the thermal background and the thermal damage. As a matter of fact, a part of the heat energy is dissipated. Different studies have defined a coefficient to characterize the percentage of the heat energy remained in the workpiece and establishes the background temperature.

2 Heat Transfer During Grinding Process 2.1 Heat Dissipation Workpiece with the shape of square rod held by a robot (Fanuc M-710iC/70), and contact the high speed spinning abrasive belt with a specific load. Meanwhile, most energy is transferred to heat. As explained above, the heat energy is dissipated in different ways, into the workpiece, into the abrasive grains, into the cooling fluid and into the chips, and the diagram of heat dissipation is shown in Fig. 2, and FT and FN is the tangential force and normal force respectively. Therefore, the heat flux is: q = qw + qs + q f + qch

(5)

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vs

Fig. 2 Heat dissipation in grinding process

Abrasive belt Workpiece

qf qw qs qch

FN FT

Rubber

2.2 Total Heat Flux It is assumed that all the mechanical energy is converted into thermal energy and generates the total heat flux. The heat fluxes are divided by the contact area. Thus, the total heat flux q is equal to the mechanical power P, i.e. the product of grinding wheel speed vs and tangential force FT , divided by the contact area which is the contact length lc multiplied by the grinding width bw . q=

P vs · FT vs · FN · μ = = lc · bw lc · bw lc · bw

(6)

Unfortunately, using a rubber grinding wheel render the assumption that contact length and geometric length are equals obsolete. The contact length is not equal to the geometrical length of the contact between a cylinder (the wheel) and another cylinder (the workpiece when the grinding has begun). This length affects the grinding forces, temperatures and the thermal damage. The real contact length can depend on energy intensity into the workpiece, wear, width, the contact time, the chip thickness, and many others parameters. In the early studies, the geometric length was used. It is shown by [9–12] that the real contact length is longer than the geometrical contact length, mainly due to the effect of normal force which generates deflection. In order to determine the real contact length, a combination of the geometrical length and deformation length, known by using the Hertz theory, is used. lg = l 2f =

 ae · de 8 · Fn · de π · E∗

lc2 = l g2 + l 2f

(7) (8) (9)

where, ae is the grinding depth, de is wheel diameter, Fn is the normal force per width unit, E ∗ combined elastic properties of the grinding wheel and the workpiece.

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Note: in the case of the experiment, there is no grinding depth so the geometrical length is zero and it is not possible to calculate the deformation length because workpiece is not a plane. It is thus assumed that the contact length is equal to the length of the workpiece, i.e. 15 mm.

2.3 Heat into the Chip According to Rowe et al., the heat flux into the chip is the specific heat carried by the chips multiplied by the contact area. The chip’s specific energy is the energy required to increase the temperature up to the chip’s formation temperature. ech = ρw · cw · Tch

(10)

With ρw the density of the material used for the workpiece, cw the specific heat of this material and Tch the temperature rises to generate the chip. This chip energy multiplied by the flux of chip generated gives us the heat flux in the chip for conventional grinding, when there is an advance speed. qch = ae · vw · ρw · cw ·

T lc

(11)

In the experiment, there is only a displacement along the vertical axis h, no advance speed and grinding depth. The expression of the chip flow is replaced by the volume of material ground divided by the time to grind it. qch =

S · h T · ρw · cw · t lc

(12)

2.4 Heat into the Cooling Fluid The part of heat escaping through the cooling fluid is due to convection at the interface, it is therefore the product of the temperature rise and heat transfer coefficient between workpiece material and cooling fluid. q f = h f · T

(13)

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3 Introducing a New Model for Heat Transfer Between the Workpiece and the Grinding Wheel In the previous models, the heat transfer into the grinding belt and grinding wheel is not studied extensively but is covered by the work-wheel fraction Rws . This part is an idea of a new kind of model for this heat escape. Indeed, including this heat lose into the work-wheel ratio omits the effect of the wear of the abrasive belt. It is shown in the experiment that the wear of the belt induces a much higher level of temperatures, consequently, more unexpected defects are susceptible to appear. In this model, it is considered that the heat entering the system grinding wheel and grinding belt only, enter the grains which act as a thermal tank. Thus, the heat flux into the grinding wheel is the amount leaving the grinding zone through the grains per second. Firstly, in order to determine the energy entering the grains, a single grain is studied. Only one grain is considered, this grain is moving along the hot surface (temperature Ts , length lc ) at the speed vs , and the contact grain—workpiece model is shown in Fig. 3. During the process, no grinding fluid is used. Due to the large conductivity of the grain relative to the air, it is reasonable to assume that the heat entering the grain only conducts into the grain without being removed by convection with the air. Thus, it is assumed that the sides are adiabatic, i.e. there is no horizontal heat flux. This assumption has been validated in some previous studies. With a thermal fluid, the heat flux from the grains into the grinding fluid was lower than 0.4% of the heat to the grain [13]. Because there is no horizontal heat flux but only one along z-direction, the temperature is only time t and z dependent. Moreover, to simplify the study, it is interesting to use a semi-infinite model. It is already assumed that the temperature is constant along x and y-axis, to use a semi-infinite model, it is required that the boundary layer, where the main part of the heat is located (here it is arbitrary choose 90% of the attenuation), is smaller than the real width.

Fig. 3 Contact grain-workpiece model

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√ z bl = 2.32 a · t = 2.32



63

lc λ · ρ · c vs

(14)

where, λ ρ·c lc t= vs

a=

(15) (16)

It is considered that all the workpiece is in contact with the grinding belt and that aluminum oxide grains are used, the grinding conditions and relative parameters are listed in Table 1. For a semi-infinite with temperature initial conditions, we have:   z T (z, t) − Ts (17) = erf √ T0 − Ts 2 a·t It is commonly assumed that the error function can be simplified with a low error to obtain:  ⎛ 

2 ⎞  z √ −4  T (z, t) − Ts 2 a·t ⎟ ⎜ (18) = 1 − exp⎝ ⎠  T0 − Ts π That gives us the expression of the temperature depending on the time and z.

Table 1 Grinding conditions

Nomenclature

Specification

Value

lc

Length of contact surface

15.1 × 10−3

vs

Speed of abrasive belt

16 m/s

λ

Coefficient of heat conduct

18 W m−1 K−1

ρ

Density of workpiece

3690 kg m−3

c

Specific heat capacity

880 J/K

r0

Length of one grain

200 × 10−6 m from grade 80

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 ⎛ 

2 ⎞  −4 2√za·t  ⎟ ⎜ T (z, t) = Ts + (T0 − Ts ) ⎠ 1 − exp⎝ π

(19)

To obtain the energy accumulated by this grain during the contact with the workpiece, the temperature difference in the grain is multiplied by the specific heat, the density and integrated on the volume of this grain. Q(t) = −ρ · c ∫(T (z, t) − T0 )d V

(20)

Q(t) = −ρ · c · Ag ∫(T (z, t) − T0 )dz

(21)

Ag is the contact surface between a grain and workpiece. This is the energy accumulated by one single grain, in order to know the total amount of energy leaving the workpiece it is necessary to know the flow of grain per second. q=

Q ·n t

(22)

n is the number of grains leaving the grinding zone during the time t. This relation could be interesting if a relation between the contact surface of the grains and the workpiece is found with the wear of the belt. Ideally, a relationship with the time in certain conditions is needed to input it in the variable Ag.

3.1 Heat Flux Entering the Workpiece The heat flux entering the workpiece is the part of the heat remaining from the generated heat after removing the heat flux into the fluid, the heat flux in the chip and the heat flux in the grinding wheel. With Rowe et al. model, the model used for the calculations in this study, the heat flux entering the workpiece is the following: qw = Rws · (q − qch ) − q f

(23)

where all the variables are calculated using the previous parts, in our case, q f is equal to zero because there is no grinding fluid and convection which is neglected. With the calculation of the heat transfer to the grinding belt as presented in the previous part, the heat flux entering the workpiece becomes: qw = Rws · (q − qch )

(24)

where the variables are calculated following the previous steps introduced in this chapter.

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3.2 Heat Flux Distribution at the Interface Now we know the amount of heat entering the workpiece, it becomes important to know how this heat flux is distributed along the interface between the workpiece and the work wheel. Many researchers had worked on this and studied different heat distribution. The simplest one is the rectangular distribution which assumes a constant heat flux on the contact length. The right triangular heat flux has been studied either. The work achieved by Guo and Malkin [14] proposes that the most accurate distribution is the right triangular distribution for shallow grinding. This distribution shape is coinciding with the experimental results of the measurement of the transient temperatures in the workpiece subsurface produced by Kohli et al. [5] and some other works [15]. In fact, the experience has shown that the heat flux is approximately right triangular slightly shorter than the contact length. Other heat flux profile had been proposed like square law by Rowe et al. [16], parabolic by Brosse et al. [17], quadratic curve with Li et al. [18], and so on. And typical heat distributions are shown in Fig. 4. Many researchers realized that inverse analysis is effective and direct approaches to find the heat distribution form the experiment. The inverse analysis is used to determine empirically some parameters impossible to obtain analytically by comparing the experimental results with a model while implementing iteratively the parameters. As highlighted by Lavisse et al. [19], Guo and Malkin [20] using different methods such as inverse matching method, the integral method and the sequential method. The rectangle triangular heat distribution is mostly used in modeling conventional grinding process, as shown in Fig. 5.

Fig. 4 Different heat flux distributions [21]

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Fig. 5 Moving heat flux with a triangular distribution

    lc lc 2z ,z ∈ − ; q(z) = q 1 + lc 2 2

(25)

The current studies lead to a reassessment of the accuracy of the right triangular distribution. In effect, using new technologies, Brosse et al. [17] showed that the most accurate distribution is a parabolic one. In the experiment studied in this paper, the diameter of the grinding wheel is 25 times longer than the workpiece section, the contact is considered as a plane and the pressure is constant on the top surface of the workpiece. The heat distribution is thus constant because generated by the friction, directly related to the local pressure.

4 Numerical Model and Results 4.1 Numerical Simulation To solve the heat equation, different approaches exist. The first approach developed is the analytical solution. The main limit of this solution is that the temperature dependence of the material properties is neglected. In this part, a numerical solution is described using finite element modelling. Moreover, the boundaries conditions are more accurate than in the analytical analysis, where the convection is neglected. To numerically solve this problem of a moving heat source, a computation has been realised on a 3D workpiece with a moving heat flux crossing this workpiece. Each time step, temperature and heat flux are calculated by the software, this simulation allows a transient analysis of the temperature rise. The numerical simulation is used in order to forecast the temperature profile in the workpiece. In this numerical model, only the workpiece is modelled. The effect of the soft grinding wheel and grinding belt is simplified as only a moving plane heat source with the same heat distribution and intensity than the plane source used in the analytical solution.

4.1.1

Influence of Thermo-Dependent Parameters

The nickel-based super-alloy GH4169 also called INCONEL 718 is a high nickelchromium-based super-alloy, it presents the same benefit with steel with better characteristics and are oxidation and corrosion resistant. It is a material perfectly adapted

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for extreme environment like high pressure and high temperature. It finds application in aerospace industry or energy industry for high temperature and high-speed part like turbine blades. JMatPro is a powerful software to calculate thermodynamic properties if material form the material composition. The following data obtained by this method are shown in Fig. 6. As we can see from Fig. 6a, phase γ and phase δ increase and decline respectively with the rise of temperature. At the same time, the density and thermal conductivity of Inconel 718 are temperature dependent parameters, and the tendency to change is shown in Fig. 6b.

Fig. 6 Thermodynamic properties of Inconel 718 calculated by JMatPro: a phase γ and phase δ; b density and thermal conductivity

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Fig. 7 Temperature at the interface with independent and thermal dependent properties of the material calculation realized with ABAQUS for different heat input

To confirm that the temperature dependence of the properties of the material are significant, some simulations have been performed using ABAQUS for different heat input with and without using dependent properties. The results are shown in Fig. 7, and it is remarkable that even from low temperatures, temperature variations (between temperature dependent and independent data results) appear for all the different heat input. Moreover, when temperatures start to be high, phase transformation can occur and as each phase has different properties, it is important to take temperature dependent parameters into account.

4.1.2

Boundary Conditions

In this problem, it is considered that all the equipment is at the ambient temperature before grinding. The initial condition is at t = 0 s, T (t = 0) = 20 °C the workpiece. The thermal problem uses heat conduction, for the heat entering the workpiece and convection on the sides of the workpiece to cool the system. The conduction coefficient depends on the material and different convective coefficients are used. From 0 W m−2 K−1 which means there is no convection; 10 W m−2 K−1 , the order of magnitude commonly used for natural convection on a plane surface and 100 W m−2 K−1 an acceptable value for the convection coefficient in case of forced convection, with moving air. The heat flux entering the workpiece is a plane moving along the bar.

Thermal Analysis of Belt Grinding Process of Nickel-Based … Table 2 Grinding conditions used in numerical simulation

4.1.3

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Conditions

Values

Grinding pressure

266 kPa

Grinding force

60 N

Rotate speed

8 m/s

Friction coefficient

0.6

Grinding duration

30 s

Thermal Load

In ABAQUS, it is not possible to define a moving heat and control its distribution with the graphic interface. A FORTRAN subroutine, DFLUX, is used to integrate the thermal load in the finite element model. However, it is not possible to define a moving surface heat flux if the moving source is not contained in a plane. A body heat source is thus used, the heat source created in FORTRAN is thicker than several meshes and has the same cross-section with the workpiece.

4.1.4

Analysis

The procedure used for this analysis is heat transfer in transient state. ABAQUS uses an iterative scheme based on Newton iterative method to solve heat transfer problems. The time increment is automatically determined by the software using Euler method. The model is built with DCC3D8 8-node convection/diffusion brick. The analysis is performed in one step, the heat load: the plane heat source is moving across the workpiece and creating the temperature background in the workpiece. The conditions used in numerical simulation are those used during as shown in Table 2.

4.2 Results Two different kinds of results are obtained with the thermal analysis using ABAQUS computation in order to validate the theoretical analysis and to study the correlation with the experimental results. It is reasonable to assume that the temperature at the interface between the grinding belt and the workpiece is the highest. It is the same during the numerical calculation. In order to plot the temperature at the interface where the heat is generated, the following results are obtained by plotting the maximum temperature in all the nodes of the workpiece with respect to time. Contrary to the experiment, during the numerical analysis the material is not removed while the heat plane source is moving through the workpiece. This leads to a heat transfer from the bottom part to the upper part. Using the Jaeger’s and Carslaw’s theory, the heat generation entered in the DFLUX subroutine is twice the heat practically generated.

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(a) Heat flux

(b)

Fig. 8 The result of simulation: a temperature in an Inconel 718 bar during crossing by a moving plane heat source; b the study addresses

The result of the simulation is the temperature in the entire workpiece, as shown in Fig. 8a. The body heat zone is at this time located where the temperature is the highest. In order to compare the ABAQUS model with the results of the experiment, it appears natural to work with a view cut to visualize only the part in which the study addresses, and this part is shown in Fig. 8b.

4.2.1

Maximum Temperature Curve

Different simulations have been completed; the heat input is the same for all the simulations. The only modification is the convection coefficient. The graph on Fig. 9a describes the evolution of the maximum temperature in the workpiece within the grinding time. According to these results reaching steady-state requires a long time of grinding. The increasing of the convection coefficient decreases the maximum temperature in the workpiece. For a low coefficient, the temperature does not change a lot; it starts to be significant in the case of forced convection, which is not the case in our experiment. The effect of cooling by convection is noticeable when looking at the side of the bar. Indeed, the temperature is no more uniform. Figure 9b summarizes as well the evolution of the maximum temperature in the workpiece with respect to the time. The difference is that the data have been calculated using temperature dependent data. Using temperature dependent data decreases the temperature gap between the different results obtained with different convection coefficients. The thermal dependent data also creates a sort of instability of the temperature which is more visible with the highest convection coefficient. The convection decreases the highest temperature, located at the interface between the grinding belt and the workpiece but the temperature background in the workpiece

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Fig. 9 Temperature at the interface grinding wheel-belt with different convection coefficient: a independent material data; b temperature dependent material data

is not really affected. Although convection does not affect the temperature background, particularly in the middle of the surface, it is highly susceptible to affect the residual stress. Indeed, convection affects the cooling rate which is one of the dominant parameters for the phase transformation.

5 Experiment and Validation An experiment has been conducted to measure the temperature behavior of an Inconel 718 bar during grinding. The experiment was conducted with a belt grinding system containing mainly a FANUC robot with a force control sensor and a belt machine. The robot can grasp the specimen and provide a constant grinding force at the interface between workpiece surface and abrasive belt. The grinding belt (CubitronTM , 984F-

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80+, 2100 mm × 50 mm, 3M China Limited) is a single layer of special aluminium oxide AL2 O3 abrasive grains fixed on an elastic paper strip reinforced with fibres. The Infrared thermal camera (R500EX, NEC Avio Japan) is used to monitor the temperature on specimen surface during grinding process. The grinding conditions are listed in Table 3. The validation of heat transfer model presented in this paper through comparing the maximum temperature and temperature profile with the results obtained by infrared camera. Figure 10 represent the temperature profile on a side of the workpiece when convection is taken into account, it is visible that the temperature is lower on the sides of the workpiece due to the heat transfer to the ambient atmosphere. On the numerical side, different calculations have been performed with different heat input and different boundary conditions (different convection coefficients). Considering there is no extra cooling condition during grinding, but the streaming air around the grinding area stirred by the rotating belt and wheel cannot be ignored. There is no exact method to select a reasonable value of convection coefficient. Several simulations are conducted to find a sound convection coefficient. When the convection coefficient is set as 50 W/(m2 °C), the general shape of the temperature obtained with those simulations is the same with the measurement. However, the data are different, the maximum temperature is not exact. This difference comes from the theoretical calculation of the heat input which is not perfectly accurate due to some unknown on some parameters. Moreover some adjustments are needed to take into account the wear of the belt.

Table 3 Grinding conditions Belt speed

Grinding force

Time per cycle

Number of cycles

8 m/s

60 N

30 s

24

(a)

516.4

*

(b)

502.3

*

Fig. 10 Temperature profile from simulation and infrared camera: a simulation; b infrared camera

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6 Conclusion This paper analysis the heat distribution in abrasive belt grinding, and establishes a mathematical model of the heat distribution. And then, a numerical analysis and an experiment have been carried out. The numerical computation allows using more sophisticated boundary conditions which are neglected in the theoretical model, such as convection on the sides of the workpiece. Moreover, it allows using temperature dependent properties for the material. The results of simulation show that both boundary condition and temperature dependent properties of the material have certain effect on the simulation results. The proposed novel heat transfer model has been validated experimentally, and the results further proof that the boundary condition cannot be neglected even if there is no extra cooling condition.

References 1. Shaw MC, Ramanath S (1988) Abrasive grain temperature at the beginning of a cut in fine grinding. J Eng Ind 110(1):15–18 2. Outwater LO (1952) Surface temperatures in grinding. Trans ASME 74:73–78 3. Malkin S (1985) Current trends in CBN grinding technology. CIRP Ann Manuf Technol 34(2):557–563 4. Malkin S, Guo C (2007) Thermal analysis of grinding. CIRP Ann 56(2):760–782 5. Kohli S, Guo C, Malkin S (1995) Energy partition to the workpiece for grinding with aluminum oxide and CBN abrasive wheels. J Eng Ind 117(2):160–168 6. Rowe WB, Pettit JA, Boyle A et al (1988) Avoidance of thermal damage in grinding and prediction of the damage threshold. CIRP Ann Manuf Technol 37(1):327–330 7. Rowe WB, Black SCE, Mills B et al (1997) Grinding temperatures and energy partitioning. Proc Math Phys Eng Sci 453(1960):1083–1104 8. Rowe WB (2013) Principles of modern grinding technology. William Andrew, New York 9. Saini DP, Brown RH (1980) Local elastic deflection in grinding. Ann CIRP 29(1):189–194 10. Verkerk J (1975) The real contact length in cylindrical plunge grinding. Ann CIRP 24:259 11. Gu DY, Wager JG (1988) New evidence on the contact zone in grinding—contact length, sliding and cutting regions. CIRP Ann Manuf Technol 37(1):335–338 12. Zhou ZX, van Lutterwelt CA (1992) The real contact length between grinding wheel and workpiece—a new concept and a new measuring method. CIRP Ann Manuf Technol 41(1):387–391 13. Lavine AS, Jen TC (1991) Thermal aspects of grinding: heat transfer to workpiece, wheel, and fluid. J Heat Transfer 113(2):296–303 14. Guo C, Malkin S (2000) Energy partition and cooling during grinding. J Manuf Process 2(3):151–157 15. Tkaya MB, Mezlini S, El Mansori M et al (2009) On some tribological effects of graphite nodules in wear mechanism of SG cast iron: finite element and experimental analysis. Wear 267(1):535–539 16. Rowe WB, Black SCE, Mills B et al (1995) Experimental investigation of heat transfer in grinding. CIRP Ann Manuf Technol 44(1):329–332 17. Brosse A, Naisson P, Hamdi H et al (2008) Temperature measurement and heat flux characterization in grinding using thermography. J Mater Process Technol 201(1–3):590–595 18. Li B, Zhu D, Pang J et al (2011) Quadratic curve heat flux distribution model in the grinding zone. Int J Adv Manuf Technol 54(9–12):931–940

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19. Lavisse B, Lefebvre A, Sinot O et al (2017) Grinding heat flux distribution by an inverse heat transfer method with a foil/workpiece thermocouple under oil lubrication. Int J Adv Manuf Technol 92(5–8):2867–2880 20. Guo C, Malkin S (1996) Inverse heat transfer analysis of grinding, part 1: methods. J Eng Ind 118(1):137–142 21. Li HN, Axinte D (2017) On a stochastically grain-discretised model for 2D/3D temperature mapping prediction in grinding. Int J Mach Tools Manuf 116:60–76

Xukai Ren was born on 20 January 1991. Education Background: Ph.D. student. Organization: Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China. His research interests include evolution mechanism of superalloy surface in robotic belt grinding, modeling of dynamic removal process of superalloys treated by robotic belt grinding and polishing.

Xiaoqi Chen was born on 10 November 1963. Education Background: Doctor of Philosophy. He is Professor of Engineering and Deputy Director. Organization: Manufacturing Futures Research Institute, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC 3122, Australia. He is visiting Chair Professor. Organization: Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China. His research interests include advanced materials processing, manufacturing systems and automation, mobile and assistive robotics.

Development of a Low-Cost Arc Spectrum Sensor for Monitoring Pore Defects in Welding Process Gang Li, Haiping Chen, Jingyuan Xu, Chao Chen, Na Lv and Shanben Chen

Abstract Commercial spectrometers commonly used in the market have the advantages of high resolution, small size, strong portability and high sensitivity. However, the commercial spectrometers are often unable to meet the needs of spectral information analysis in specific welding scenarios due to the lack of sufficient pertinence in the analysis of arc spectral information. Especially commercial spectrometers are generally expensive, and the software interface is not open, which means that we will not be able to real-time analyze and process the data obtained by spectrometers. Based on this, a low-cost arc spectrum information sensor based on Cherney-Turner optical system is designed and manufactured to detect the formation of hydrogen pore defects in welding process, and to provide support for the follow-up research on hydrogen pore defects. Specifically, we can effectively detect hydrogen content in arc atmosphere by monitoring the spectral intensity of hydrogen atom in arc light during welding process and detect hydrogen pore in molten pool. Experiments show that the sensor achieves real-time and effective detection and acquisition of arc spectrum information in aluminum alloy welding process. Keywords Aluminum alloy AC TIG welding · Hydrogen porosity · Arc spectrum information · Cherney-Turner optical path · Linear array CCD sensor · Design of spectrometer

1 Introduction Porosity is a common internal welding defect, which widely exists in various welding methods, and the number of welding defects is relatively large, about 70% [1]. The existence of pore defects has a very negative impact on the mechanical properties of G. Li · H. Chen · J. Xu · C. Chen · N. Lv (B) · S. Chen (B) Intelligentized Robotic Welding Technology Laboratory, School of Materials Science and Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China e-mail: [email protected] S. Chen e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-8668-8_4

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welds, mainly reflected in the fact that pore will destroy the overall gas tightness of welds and weaken their effective working section; pore causes stress concentration, which reduces the toughness and strength of welds; and even causes cracks in serious cases [2–4]. In addition, it is difficult to detect and control the stomatal defects. All these factors make it very important and necessary to study the pore defects in the welding process. In order to further improve the welding quality, reduce the weld defects, it is essential to inhibit the formation of the pore defects in the welding process [5]. The main pore defect in welding process is hydrogen pore. The formation of hydrogen pore is mainly due to the huge difference in solubility of hydrogen between liquid and solid aluminum alloy, which makes hydrogen precipitate in the solidification process of the molten pool, undergo nucleation, growth and floatation process, and the bubbles that fail to escape from the molten pool become hydrogen pore [6]. Existing studies have shown that the formation of hydrogen pore is related to the hydrogen content in arc atmosphere during welding [7]. There are three main methods to detect stomatal defects: X-ray detection, ultrasonic detection and spectral detection. Compared with X-ray detection and ultrasonic detection, spectral detection based on welding arc has a series of advantages, such as rich information, high signal sensitivity and accuracy and objectivity, importantly, it is essentially related to the internal defects of the weld [1, 8]. In conclusion, the hydrogen element in the arc atmosphere can radiate the characteristic spectrum of hydrogen, the existence of hydrogen is the cause of hydrogen pore defect in welding process, so the spectral intensity of hydrogen is positively correlated with the hydrogen content in the arc atmosphere. Thus, the hydrogen content in the arc atmosphere can be detected by detecting the hydrogen spectral intensity of the arc light, so as to achieve the purpose of detecting the hydrogen pore in the molten pool [9]. There are three distinct characteristic lines of hydrogen in the visible band (300–800 nm). They are 434.05, 486.14 and 656.28 nm. After inquiring the optical parameters of HI lines, it can be seen that the relative intensity of hydrogen atomic (HI) lines with 656.28 nm wavelength is obviously stronger than that of 434.04 and 486.14 nm. In combination with the previous analysis, we selected the spectral intensity of hydrogen atom (HI) 656.28 nm to characterize the hydrogen content in arc atmosphere during welding [10]. However, in the actual welding process, HI spectrum information will be disturbed by other non-hydrogen fact (such as other welding parameters). Therefore, for any hydrogen spectrum whose intensity changes, we can not judge whether the change of the spectrum intensity is caused by the change of hydrogen content in arc atmosphere or by the change of background continuous spectrum produced by welding parameters [11–13]. In order to solve this problem, the ratio of the intensity of the HI 656.28 nm line and the nearest ArI 641.63 nm line intensity IH/IAr can be used to effectively remove the influence of background continuous spectral changes caused by welding parameters [14]. Therefore, the ratio IH/IAr can be used to characterize the hydrogen content in arc atmosphere during welding, thus realizing the detection of hydrogen hole defects in aluminum alloy welding [15]. There are three reasons that prompted us to develop a spectral sensor instead of a commercial spectrometer. Firstly, when the spectrometer is used to collect the

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spectral signal of welding arc, we do not require the spectral sensor to have such high resolution and sensitivity as the commercial spectrometer, so we can design the spectral sensor that meets the welding process requirements to reduce the cost. Secondly, the general spectrometer is not specific for the spectral acquisition of welding process. For example, the wavelength coverage of universal spectrometer is generally 200–1100 nm or wider [16, 17]. However, previous analysis shows that the wavelength of hydrogen atom and argon atom lines needed to be collected are 656.28 and 641.63 nm, respectively, in the hydrogen pore detection of TIG welding of aluminum alloy, so the spectral sensor we designed only needs to cover this band. In addition, commercial spectrometers generally do not open software call interfaces, which means that commercial general spectrometers can acquire real-time spectral acquisition data, but can not synchronously calculate the ratio of spectrum intensity IH/IAr, which characterizes the hydrogen content in arc atmosphere [18, 19]. Therefore, online prediction of pore defects can not be realized. Based on this, we designed and produced a low-cost dedicated spectrum sensor that can meet the requirements of aluminum alloy TIG welding hydrogen hole detection, so that the arc spectrum signal can be extracted and processed in real time, and the hydrogen hole can be directly monitored.

2 Design and Fabrication of Spectral Sensor for Welding Arc 2.1 Design of Spectral Sensor 2.1.1

Selection of Spectral Sensing Types

The core problem of spectral sensing is how to disperse the incident light into the spectrum in the order of wavelength, that is, how to split the light. There are two kinds of spectroscopes commonly used in spectral sensors: prism and grating. The advantages and disadvantages of prism spectrum sensor and grating spectrum sensor are analyzed as follows: prism spectrum sensor has no double-grade problem and high sensitivity, but prism spectrum sensor needs different materials when splitting light in different bands, which limits the band of incident light. In addition, the prism is a kind of the lens, which inevitably produces chromatic aberration and aberration, which will affect the image quality. Compared with prism spectral sensor, grating spectral sensor has better dispersion and resolution, but its disadvantage is that it has double-grade problem. Considering the performance of the spectrometer, the difficulty of optical system design and the characteristics of welding arc spectrum, we finally choose to design and fabricate a grating spectrum sensor for the specific aluminum alloy pulse GTAW welding application scenario.

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Optical Path Selection for Spectral Sensing

The structure of optical system is the key factor for the optical performance of spectral sensors. Reasonable optical structure can not only simplify the workload of designing and manufacturing spectral sensors, but also reduce the influence of some negative factors such as coma, aberration and spherical aberration on spectral sensing. Cherney-Turner optical path has the advantages of simple structure, wide spectrum application, no color difference and low cost, which is conducive to the construction of optical sensor system. Therefore, we choose Cherney-Turner optical path structure to meet the design requirements of high imaging quality and small volume of the sensor. Specifically, the Cherney-Turner optical path can be divided into two types: M-type and cross-type. The cross-type has more compact optical path structure, but the resolution stability is poor and the coma is large. Therefore, the Cherney-Turner M optical path structure is chosen in this paper.

2.1.3

Design and Calculation of Optical Parameters

The specific Cherney-Turner M-type optical path diagram is shown in Fig. 1. Its basic principle is as follows: firstly, the arc light is introduced into the concave mirror M1 through the optical fiber for beam collimation. Secondly, the reflected parallel light is reflected at a certain angle on the grating G, so that the dispersed light is uniformly arranged according to the wavelength size. Thirdly, the optical signal which focused by the concave mirror M2 is projected onto the linear array CCD to generate a corresponding electrical signal. The imaging quality of spectral sensing is greatly affected by the aberration in the system. The Cherney-Turner M-type optical path structure is a lensless system, so it can effectively avoid aberration such as chromatic aberration and spherical aberration, but coma is inevitable, so the main consideration in our imaging system is coma. The coma aberration makes the imaging symmetry unbalanced, seriously affects the imaging contour, reduces the resolution of the system. Therefore, we should minimize and avoid the coma aberration in the design process of the spectral sensor.

Fig. 1 Overlay drawing of adjacent weld bead

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The wave aberration W generated by the concave spherical mirror coma is as follow: W =

H D3 8R 3

(1)

H is off-axis distance, D is beam aperture, R is the radius of curvature of concave mirror. In order to compensate the coma difference between the upper and lower parts of the optical system, the following formula should be satisfied. H1 D13 H2 D23 = 3 8R1 8R23

(2)

The following formula can be obtained from geometric relations. D1 = W · cosi

(3)

D2 = W · cosθ

(4)

W is grating width, i is incident angle, θ is diffraction angle. The problem can be simplified as two concave spherical mirrors with the same radius of curvature and arranged asymmetrically (H 1 = H 2 , R1 = R2 = R). Equation (2) can be transformed into the following formula. H1 cos3 θ = H2 cos3 θ

(5)

The radius of curvature of the spherical mirror is twice the focal length of the spherical mirror. It can be seen that the geometric conditions are as follows. ϕ  1 H1 = 2h1 = 2f · sin (6) 2 ϕ  2 H2 = 2h2 = 2f · sin (7) 2 In summary, the Shafer elimination equation can be obtained as follows.     sin ϕ21 H1 cosθ 3  = = H2 cosi sin ϕ22

(8)

It can be concluded that if the structural parameters i, θ , ϕ 1 , ϕ 2 of Cherney-Turner M-type optical path satisfies the Shafer equation, the coma error in the system can be compensated.

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From the dispersion equation of grating, the following formula can be obtained when the diffraction order m is 1. (sin i + sinθ ) = nλ

(9)

N is the groove density of the grating and λ is the intermediate wavelength. From the geometric relationship in Fig. 7, the following formula can be obtained. i +θ =ϕ

(10)

ϕ1 + ϕ2 = ϕ

(11)

It is easy to know that the structural parameters of the spectral sensor with CherneyTurner M optical path need to satisfy the four formulas of Formulas (8)–(11). The relative positions of two concave mirrors and gratings (i, θ , ϕ 1 , ϕ 2 ) of CherneyTurner M-type spectral sensor can be solved by the four formulas when the grating groove density n, the angle ϕ of incident light and diffraction light are known and the intermediate working wavelength of the sensor is determined. Resolution is an important index reflecting the resolution of spectral sensors, which satisfies the following formulas. ω=

s · cosi n · f1

(12)

S is the aperture of the optical fiber, i is the incident angle, n is the groove density, and f 1 is the focal length of the concave mirror. From the geometric relationship in Fig. 7, the structural parameters of the spectral sensor can also be obtained as follows. SM1 = M1 G = f 1 /cos(ϕ1 /2)

(13)

SM2 = M2 G = f 2 /cos(ϕ2 /2)

(14)

H1 = 2 f 1 sin(ϕ1 /2)

(15)

H2 = 2 f 1 sin(ϕ2 /2)

(16)

From Formulas (13)–(16), it can be seen that the structural parameters related to the overall size of the spectral sensor are only related to the focal length of the concave mirror f 1 , f 2 , and the angle of the concave mirror ϕ 1 , ϕ 2 . After comprehensive consideration, the angle ϕ between the incident light of the grating and the reflected light is taken as 30°. The common groove densities of planar reflective gratings on the market are 600, 1200 and 1800 lines/mm. The higher the grating groove density is, the higher

Development of a Low-Cost Arc Spectrum Sensor for Monitoring … Table 1 Structural parameters of optical system

Parameter

Value (°)

Parameter

81

Value (mm)

i

6.9

H1

17.8

θ

36.9

H2

34.4

ϕ1

10.2

SM1 = M1 G

100.4

ϕ2

19.8

S M2 = M2 G

101.5

the resolution of the spectral sensor is, but the corresponding price is higher. After comprehensive consideration, the plane reflection grating with the groove density n of 1200 lines/mm is selected. After the previous analysis, we know that there are two spectra that need to be paid attention to in real-time monitoring of hydrogen voids in pulsed GTAW welding of aluminum alloy. They are 656.28 nm for hydrogen atom (HI) and 641.63 nm for argon atom (ArI). Therefore, the intermediate working wavelength λ of the spectral sensor we designed can be set to 650 nm. The relative positions of the two concave mirrors and gratings of the spectral sensor can be calculated by substituting the angle ϕ = 30, the groove density n = 1200 lines/mm, the intermediate working wavelength λ = 650 nm into the Formulas (8)–(11). The solution is i = 6.9°, θ = 36.9°, ϕ 1 = 10.2° and ϕ 2 = 19.8°. Considering the overall size and resolution of the sensor, the concave mirror with focal length f 1 = f 2 = 100 mm is selected, and the calculated i, θ , ϕ 1 and ϕ 2 are substituted into Formulas (13)–(16). The structural parameters of the spectral sensor are H 1 = 17.8 mm, H 2 = 34.4 mm, SM1 = M1 G = 100.4 mm, S M2 = M2 G = 101.5 mm. The structure parameters of the optical system are listed in Table 1. According to Formula (12), the smaller the aperture of the optical fiber, the larger the resolution. The common apertures of optical fibers on the market are 50, 100 and 200 µm. In this paper, the aperture of optical fibers s = 50 µm is selected. The theoretical resolution of the designed spectral sensor can be calculated as 0.41 nm by substituting s = 50 µm, i = 6.9°, n = 1200 lines/mm and f 1 = 100 mm into Formula (12).

2.2 Fabrication of Spectral Sensor 2.2.1

Selection of Optical Components

1. Optical Fiber and Optical Fiber Adapter When the sensor is debugged and operated, it is necessary to keep the arc light into the optical platform at a stable position and angle so as to ensure the spectral effect of the spectrometer. Therefore, it is necessary to install an optical fiber adapter on the side wall of the spectral sensor to fix the optical fiber. The parameters of laser optical fibers selected in this paper are shown in Table 2, and the physical objects of optical fibers and optical fiber adapters are shown in Figs. 2 and 3.

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Core diameter

Available band

Working temperature

200 µm

200–1200 nm

−40 ~ 180 °C

Fig. 2 Laser optical fiber

Fig. 3 SMA905 optical fiber adapter

2. Collimating and Focusing Elements In this paper, concave spherical reflector is used as collimating and focusing element of spectral sensor. Considering the imaging characteristics of the optical path, the Ultraviolet-enhanced concave spherical reflector with diameter of 30 mm and focal length of 100 mm is selected. The corresponding parameters are shown in Table 3 and the physical drawings are shown in Fig. 4.

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Table 3 UV-enhanced aluminum concave spherical mirror parameter table Diameter

Focal length

Center thickness

30 mm

100 mm

5 mm

Fig. 4 UV-enhanced aluminum concave spherical mirror Table 4 Planar reflective grating parameter table Specification

Thickness

Line density

Blaze wavelength

Available band

12.7 mm × 12.7 mm

6 mm

1200 lines/mm

500 mm

500–800 nm

3. Plane Reflection Grating Grating is the core component of welding arc spectrum sensor. In this paper, plane reflection grating is used. As described in the previous section, after considering the resolution and cost of the sensor, the grating line density chosen in this paper is 1200 lines/mm. In order to meet the requirement of real-time monitoring hydrogen pore in pulsed GTAW welding of aluminum alloy, the detection range of the designed spectral sensor is set to 500–800 nm. The specific parameters of the planar reflection grating are shown in Table 4 and the physical figure is shown in Fig. 5. 4. Photoelectric Conversion Sensor The data acquisition system based on CCD can sample and store spectral information quickly, so it is widely used in the field of spectral acquisition. TCD1208AP, a linear array CCD product produced by TOSHIBA, is selected in this paper. It provides a rich dynamic link library. It can develop corresponding software to match according to the demand. It perfectly solves the problem that commercial universal spectrometer

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Fig. 5 Planar reflective grating Table 5 Chart of CCD performance parameters Spectral response

Dynamic range

Driving clock

Conversion frame rate

Sample caching

500–800 nm

750

0.6 MHz

25

4K

Fig. 6 Linear CCD image acquisition card

does not support secondary development. The specific performance parameters of the CCD are shown in Table 5, and the physical objects of the linear array CCD image acquisition card are shown in Fig. 6.

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Design of Shell and Internal Bracket of Spectral Sensor

Spectrum sensor is a sophisticated optical experimental equipment. When debugging and working, it needs a completely closed dark environment to reduce and avoid the interference of external stray light on the optical path. Therefore, it is necessary to design a closed casing to meet this requirement. In addition, in order to make the optical platform inside the sensor located on the same horizontal plane, we designed a series of component brackets to make the optical element center inside the sensor located on the horizontal plane 30 cm above the bottom of the sensor. The overall and individual parts of the specific spectral sensor are shown in Figs. 7 and 8. Figures 7 and 8 are 3D maps of the spectral sensor drawn by SolidWorks software. By this method, the sensor can be easily modified and pre-assembled in the software.

Fig. 7 Overall picture of spectral sensor

a. Dimmer

b. Concave mirror bracket

e. Lid

Fig. 8 Spectral sensor parts drawing

c. Concave mirror bracket

f. Shell

d. Grating bracket

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The overall and part drawings after determining the specific structure size can be directly used to guide the actual processing. In the actual drawing process, the imaging characteristics of Cherney-Turner optical path and the size of each component should be taken into account to determine the specific position of each component in the spectrometer. The top view of the bottom plate of the spectral sensor drawn after comprehensive consideration is shown in Fig. 9. After the overall size design, the shell size of the spectral sensor is determined to be 150 × 120 × 60 mm, which meets the portable requirements of the sensor. In addition, the spectral sensor designed in this paper uses photosensitive resin material to process in the way of 3D printing, which greatly reduces the weight of the spectral sensor. The actual weight of the sensor measured is 670 g. The internal optical path diagram of the spectral sensor is shown in Fig. 10, and the physical diagram of the sensor is shown in Fig. 11.

Fig. 9 Top view of spectrum sensor shell base plate

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Fig. 10 Schematic diagram of the internal light path of the spectrum sensor

Fig. 11 Physical map of spectral sensor

3 Experimental Platform and Spectral Sensor Debugging 3.1 Experimental Platform As shown in Fig. 12, the experimental platform for pulse GTAW welding of aluminum alloy is mainly composed of welding system and data acquisition system. The welding system consists of central control computer, welding robot, arc sensing system, welding power source and other peripheral auxiliary equipment. The specific system hardware is M-10iA welding robot produced by FANUC, two-axis displacement turntable of robot, TIG water-cooled welding gun, HC-71 wire feeder, AC/DC dual-purpose AVP500 welding power source produced by OTC, protective cylinder, cooling water tank and fixture for welding parts.

3.2 Spectral Sensing System As shown in Fig. 13, the spectrum sensing system is mainly composed of an arc light acquisition probe, a fiber optic sensor and an arc spectrum sensor. Its working principle is as follows: firstly, the acquisition probe imports the arc light collected

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Fig. 12 The experiment platform diagram

Fig. 13 Schematic diagram of spectral sensing system

during the welding process into the arc spectrum sensor through the fiber optic; secondly, the linear array CCD sensor in the arc spectrum sensor converts the spectrum signal obtained by grating splitting light into electricity. Thirdly, the converted electric signal is transmitted to the computer through the USB interface integrated with the linear array CCD sensor. Finally, the data are analyzed and processed by the secondary development of the spectral signal processing software (MPS linear array CCD image acquisition software) at the computer end.

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Fig. 14 Physical map of spectral sensor

3.3 Debugging of Spectral Sensors The spectral sensor designed in this paper has an intermediate working wavelength of 650 nm and a wavelength detection range of 500–800 nm. If the incident aperture of the optical fiber is irradiated by a red laser emitter with 650 nm wavelength, the optical path elements should be adjusted properly so that the red light points emitted by the imaging objective lens are positioned in the middle of the sensitive strip of the linear array CCD image acquisition card. After debugging, fix the optical elements inside the sensor. Figure 14 is the spectrum of the red laser emitter obtained by the MPS linear array CCD spectrum analysis software after debugging, the horizontal axis represents the photosensitive length of the CCD and the vertical axis represents the relative light intensity. It can be seen that the spectrum peaks collected by the welding arc spectrum sensor designed in this paper are wider, indicating that the resolution of the sensor is not high enough, but at the same time it can be noted that the peaks of the spectrum lines are sharp, which shows that the resolution of the spectrum sensor designed in this paper is sufficient. It can be used in welding scene, and the cost of spectral sensor can be greatly reduced by designing and manufacturing scheme.

3.4 Aluminum Alloy Pulsed GTAW Welding Spectrum Acquisition Experiment The optical fiber wire of the spectrum sensor is fixed on the fixture of the welding robot arm which is used to fix the optical fiber, so that the optical fiber incident aperture can always align with the tungsten pole of the welding torch along with the movement of the manipulator arm. Set the welding current to 180 A. When

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Fig. 15 Arc spectrum of aluminum alloy GTAW welding sampled by spectral sensor

the experiment is ready, the welding robot is activated and the AC pulse GTAW welding experiment of aluminum alloy is started. The spectrum sensor collects the arc spectrum signal in real time during welding process, and transmits the signal to the computer through USB interface. The collected arc spectrum is analyzed by using MPS linear array CCD spectrum analysis software. Figure 15 is the arc spectrum collected by the spectral sensor designed in this paper. The horizontal axis represents the photosensitive length of the CCD and the vertical axis represents the relative light intensity. It can be seen from the graph that the sensor can collect obviously spectral lines. The intensity signal is weak in the band with wavelength less than 650 nm and strong in the band with wavelength greater than 650 nm. From the NIST Atomic Spectrum Database, we can see that the main spectral lines in 650–800 nm are ArI, and the main spectral lines in 500–650 nm are alloy elements in aluminium alloy. The wavelength of hydrogen atom and argon atom spectral lines needed to be collected are 656.28 and 641.63 nm, respectively. There is no doubt that this band is within the detection range of the wavelength of the spectral sensor, so the hydrogen pore detection task in pulsed GTAW welding process of aluminium alloy can be accomplished by this spectral sensor.

4 Conclusion In this paper, a low-cost spectrum sensor for hydrogen pore detection in pulsed GTAW welding of aluminum alloy is designed and manufactured. The sensor adopts Cherney-Turner M-type optical path and linear CCD signal acquisition system, which

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can better meet the experimental requirements. The red laser emitter debugging and pulsed GTAW welding experiments on aluminum alloy verify that the spectral sensor has the ability of spectral acquisition, and the detection range of wavelength is about 500–800 nm. Because of the influence of low cost and installation accuracy, the resolution of spectral sensor is not high enough, but it can be further improved. The resolution can be further improved by selecting better optical elements with better performance parameters and more detailed installation and debugging. Acknowledgements This work is supported by the National Natural Science Foundation of China (51575349).

References 1. Modenesi PJ, Apolinário ER, Pereira IM (2000) TIG welding with single-component fluxes. J Mater Process Technol 99:260–265 2. Huang JL, Warnken N, Gebelin JC et al (2012) On the mechanism of porosity formation during welding of titanium alloys. Acta Mater 60(8):3215–3225 3. Chen K, Yang WX, Huang R et al (2010) Porosity control of 1420 Al-Li alloy by dual-beam laser welding. Infrared Laser Eng 39(1):133–137 4. Christner B, Lovell R, Campbell M (1998) Developing a GTAW penetration control system for the Titan IV program. Weld Met Fabr 66:29–38 5. Huang YM, Zhang ZF, Lv N et al (2015) On the mechanism and detection of porosity during pulsed TIG welding of aluminum alloys. In: Robotic welding, intelligence and automation, vol. 363, pp 133–143 6. Haboudou A, Peyre P, Vannes AB et al (2003) Reduction of porosity content generated during Nd:YAG laser welding of A356 and AA5083 aluminum alloys. Mater Sci Eng A 363(1):40–52 7. Huang YM, Chen HB, Chen SB (2017) Investigation of porosity in pulsed GTAW of aluminum alloys based on spectral and X-ray image analyses. J Mater Process Technol 243:365–373 8. Yu HW, Xu YL (2015) On-line monitor of hydrogen porosity based on arc spectral information in Al-Mg alloy pulsed gas tungsten arc welding. Opt Laser Technol 70:30–38 9. Yu HW, Chen SB (2013) Application of arc plasma spectral information in the monitor of Al-Mg alloy pulsed GTAW penetration status based on fuzzy logic system. Int J Adv Manuf Technol 68:2713–2727 10. Zaeh MF, Huber S (2011) Characteristic line emissions of the metal vapour during laser beam welding. Prod Eng 5(6):667–678 11. Xia G, Qu BX, Peng Liu et al (2012) Astigmatism-corrected miniature Czerny-Turner spectrometer with freeform cylindrical lens. Chin Opt Lett 10(8):81–201 12. Turko BT et al (1992) Gamma ray spectrometer readout with linear CCD sensor. IEEE Trans Nucl Sci 39(5):1336–1340 13. Gaylin MY, Nadim LM, Paul AH et al (1997) Miniature spectrometers for biochemical analysis. Sens Actuators A 58(1):61–66 14. Xu MM, Jiang QW, Liu WQ et al (2014) An improved method for optical system design and optimization of double grating spectrometer. Infrared Laser Eng 43(1):184–189 15. Ye B, Wang F et al (2011) Optical design of spectrum observation system in ZnO temperature sensor. Chin J Laser 38(7):0716001 16. Dalton ML (1966) Astigmatism compensation in the Czerny-Turner spectrometer. Appl Opt 5(7):1121–1123

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17. Robert EH (1992) Atmospheric ultraviolet remote sensing. Academic Press 18. Xu Y, Yu H, Zhong J et al (2012) Real-time seam tracking control technology during welding robot GTAW process based on passive vision sensor. J Mater Process Technol 212:1654–1662 19. Jarvis BL, Ahmed NU (2000) Development of keyhole mode gas tungsten arc welding process. Sci Technol Weld Joining 5(1):1–7

Gang Li was born in china in 1994. He received the bachelor degree in materials processing engineering from Central South University, Hunan, China, in 2016. He studies in intelligentized robotic welding technology laboratory for a master’s degree in Shanghai Jiao Tong University since September 2017 until now. His research direction is detection of welding defects by spectral information.

Na Lv graduated from Shanghai Jiaotong University in 2014 with a doctorate in engineering in the direction of robotic welding intellectualization in material processing. Her research interests include intelligent welding process information acquisition and information processing research, robotic intelligent system application, robotic automatic welding technology and advanced material forming research.

Determination of the Initial Welding Point for Multi-pass Welding Based on Laser Vision Yanhui Lai, Ruilin Dai, Hao Zhou, Zhen Hou, Huabin Chen and Shanben Chen

Abstract Most of the welding robots in practice are still teaching and playback mode. They are lack of the function to perceive the external environment, so it is difficult to carry out adaptive welding pass arrangement and position planning for medium thick plate. In this research, extraction procedure for feature points in laser stripe is presented. We get the weld groove profile with laser scanning. After processing the laser stripe with some image process methods, we get the two lowest feature points of the laser stripe including binarize image, medium filter for image, extracting the center of laser stripe. Then, two target feature points are extracted with linear fitting and distance formula. Finally, we convert the image coordinate value to the TCP coordinate of the robot according to the calibration matrixes. As a result, we restored the physical cross section of the weld bead, and can decide the initial welding position with the two feature points for every welding pass in the medium thick plates. Keywords Medium thick plate · Welding pass plan · Laser vision · Image processing · Coordinate transformation

1 Introduction The faster development of digitization and information technology have effectively promoted the progress of automatic welding technology, mainly in the application of digital welding machine, and the number of welding automation equipment represented by welding robot has greatly increased [1]. In the fields of shipbuilding, automobile, motorcycle, construction machinery, heavy machinery and aerospace, welding robot is widely used to improve productivity. Instead of manual welding, Y. Lai · R. Dai · H. Zhou · Z. Hou · H. Chen (B) · S. Chen (B) Intelligentized Robotic Welding Technology Laboratory, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China e-mail: [email protected] S. Chen e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-8668-8_5

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robotic welding frees workers from harsh working environment, improves labor productivity and promotes development of manufacturing [2]. For robotic welding, the welding torch is installed in the end of the robot, and the workpiece is installed on the platform with welding fixture or displacement machine, or it is transferred to the welding position by the production line. Whatever in anyway, the accuracy of location and repeat positioning for workpieces are limited [3], and the thermal deformation of the welding process may cause weld position or surface shape changes. However, the current welding robot with “teach-play” mode can only be carried out in accordance with the taught path, and is unable to perceive the change of the weld groove in real time. In view of the deficiency of traditional robot, the vision sensor is used to realize the real-time perception for weld groove and adjust the position and posture of welding gun to realize more intelligent welding operation [4]. At present, in the field of welding, the research on vision sensor mainly focuses on the initial point recognition of welding seam, welding seam tracking, welding path planning, adaptive welding, welding quality supervision and evaluation [5–9]. Chen and Lin implements an easy way to locate the initial welding position with template matching based on vision sensor [10]. Xi presented a method for weld image feature extraction. With the image processing result, the robot can adjust the arc and the relative position of weld center to make the welding arc track the weld accurately [11]. Yang calculated the three-dimensional point cloud data of the workpiece by image processing technology and put forward the automatic welding robot path planning system [12]. Fan controlled the penetration of welding pool by observing the molten pool image information from three directions based on vision sensor [13]. Shuang et al. controlled the penetration effectively by adjusting arc current, welding speed with image information on the back of molten pool [14]. Medium thick plate welding structure parts are widely used in key parts of automobile girder steel plate, automobile static steel plate, automobile rear bridge and automobile bogie. Compared with thin plate, the welding groove is larger, the welding time is longer, the welding process is complex, and it needs higher welding quality. In the welding of medium thick plates, multi-pass welding is involved. In the actual welding, the next pass is often taught after the completion of the previous one. To complete a workpiece welding, it often requires intense teaching work. Therefore, if we can pre-plan the arrangement of the weld bead and adjust the position of the welding gun to carry out the next welding work adaptively according to the filling situation of the previous weld, it will greatly reduce teaching work and improve labor productivity. Yang [15] obtained the characteristic information of weld groove through image processing, used the characteristic information to carry out the path planning of multi-multi-pass welding, and established the fuzzy rule table to correct the welding path appropriately. Zhang [16] has established an analytic model of bead programming, through which the correction of welding speed and other parameters can be calculated. Gu [17] explored the programming model under variable gap in weld. For the feature extraction of V groove weld, there are different methods to extract different number of feature points. Yang [18] has developed a kind of recognition algorithm combined projection with least-squares curve fitting for feature points of V-groove welds. Zhang extracted the four turning points of the

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laser stripe in V groove by designing multiple slope smoothing algorithm [19]. Li put forward a kind of weld structure light image processing and feature extraction method, which effectively overcame reflective problem in structured light image and extracted the characteristics of weld position in the image [20]. In this paper, an image processing algorithm based on two feature points is proposed to solve the problem of welding gun position and posture planning in multi-pass welding of medium thick plate. In the experiment, the multi-pass weld path of medium and thick plates was preplanned through the custom method. Then, the laser vision sensor system which was independently developed by our laboratory was used to scan each pass. After image processing, two feature points of weld contour were extracted and we transformed the two points to the base coordinate of the robot. As a result, the subsequent position of welding gun can be planned to improve production efficiency through these two feature points.

2 Experimental Setup and Procedure 2.1 System Component As Fig. 1 shows, the experiment system mainly consists of FANUC M-10iA robot, FANUC R-30iB robot controller with the FANUC Arc-Tool arc welding tool soft kit, laser vision sensor, the host computer, Fronius welding power source and peripheral auxiliary equipment.

Fig. 1 The experiment system

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Fig. 2 Welding pass image acquisition diagram

As we can see in Fig. 2, the laser vision sensor, which is designed by our own laboratory, consists of a CCD camera and a line laser transmitter. The laser emitted by the line laser emitter forms a laser stripe with a specific shape on the surface of the workpiece, which is captured as images by the CCD camera and transmitted to the host computer for subsequent processing. The welding workpieces brand is Q235, with thickness of 16 mm, butt joint gap of 2 mm, single groove angle of 45°. The welding shielding gas is 80% Ar + 20% CO2 . The welding wire is 1.2 mm solid core protection wire.

2.2 Pre-planning for Weld Bead As Fig. 3 shows, firstly we make an approximation for the weld bead shape. The bottom welding is approximately trapezoid, and the first n − 1 pass of each layer is parallelogram, and the nth pass is trapezoid, where n is the number of welding passes of each layer. In order to avoid heat accumulating on the same side of parent metal, interlayer is in a sequential program. At the same time, in order to guarantee the final weld

Fig. 3 The welding pass arrangement

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Table 1 The pre-planning for welding parameter and weave parameter Pass no.

1

Electricity Weld (A) speed (cm/min)

The angle of welding torch

0

Weave parameters Frequency The (Hz) weave amplitude (mm)

Stay time in left

Stay time in right

0

0

0

0

180

24

2

260

24

0

1.2

4.5

0.1

0.1

3-1

220

24

15

1.2

3.5

0.1

0.1

3-2

220

24

0

1.2

3.5

0.1

0.1

4-1

250

24

0

1.2

4.0

0.1

0.1

4-2

250

24

0

1.2

4.0

0.1

0.1

4-3

250

24

0

1.2

4.0

0.1

0.1

forming regularity and the reliability of welding quality, welding was used on both sides before the middle layer of the same welding layer. Based on the artificial experience, choosing the proper welding parameter for bottom bead. Then planning the next number of weld and welding parameters. As a result, the pre-planned welding parameters are shown in Table 1, bead diagram is shown in Fig. 3, for a total of four layers and seven passes.

3 Image Features Extraction and Coordinates Transformation 3.1 Image Process and Features Extraction The original laser stripe images were obtained by the laser sensor, then we extracted the central line of the laser stripe by image processing. Take the laser stripe of the third weld pass as example, the image processing is shown in Fig. 4. The image’s foreground and background are separated by binarization, and the image noise is removed by median filtering. Then, the center line of laser stripe is extracted by means of mean method. Finally, we get the two lowest feature points through some methods. Notice that we want to transform the image coordinate value of the two feature points, we cannot change position of the laser stripe. In other words, we cannot rotate the image and extract the region of image. We must make processing algorithm in the original image. In order to extract the laser stripe, we must separate the image foreground and background. The gray value of the foreground is set to 255, the background is set to

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Binarize

ROI

(3)

(2)

(1)

Medium fiter

Extract the laser center

Extract the feature points

(6)

(5)

(4)

Fig. 4 The procedure of image processing

0, so that to extract the information of the foreground image more accurately. Since the original image laser stripe has a much higher grayscale than the background, it only needs to take the threshold between 100 and 200 to separate the foreground and background successfully. The threshold segmentation formula is as follow:  h(x, y) =

255, g(x, y) ≥ N 0, g(x, y) < N

(1)

N is the threshold value. After binarizing image, using median filter to filter out scattered light spot. Then we use the average method to extract the center line of laser stripe. Searching the image by row to obtain each row of the laser coordinate values in the image. Keep the horizontal coordinate of each point unchanged, and calculate the average value of the ordinate of each point according to the column. Connect the calculated coordinate points of each column to get the final laser stripe center line. Firstly, we find the lowest point according to the minimum value of the horizontal ordinate. The we calculate the gradient of the point with its higher point and lower point. If the gradient of higher if larger than the lower one, we can judge that the other one target point is in the higher. And we fit the line with the higher ones. Next, we calculate the distance of every higher point to the fitting line, if the distance is larger than the set value, and the point is what we want, as shown in Fig. 5. If the lowest point is in the higher one, and the algorithm to find the second one is the same to former, it is just different in the search direction.

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Fig. 5 The distance of some points with fitting line

3.2 Coordinates Transformation As we know, the geometry information about the laser stripe is in image coordinate after we get the images and laser stripe from the laser vision sensor, so we must transform the image coordinate into the three-dimension real coordinate. And the transformation depends on the calibration procedure including the hand-eye calibration and laser plane calibration. Firstly, we use the laser plane calibration to transform the laser stripe in image frame to camera frame, then use the hand-eye calibration to transform the camera frame to tool frame. Lastly, we transform the tool frame into robot base frame, whose transformation relationship is usually offered by robot controller as robot position value. As a result, we can express the whole transformation as follow [21]: Mi = ρb H c c T i mi = ρb H t t H c c T i mi

(2)

mi —The homogeneous pixel coordinates of laser stripe points in Fi, such as [x, y, 1]T ; i c T —The 3 * 4 laser conversion matrix; c b H —Transformation matrix from camera coordinate system F c to robot base coordinate system F b ; M i —The homogeneous coordinate in F b , such as [x, y, z, 1]T ; ρ—Any coefficient used to convert homogeneous coordinates;

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Fig. 6 The transformation of the coordinates in system

The transformation from F c to F b can be decomposed into two steps: bH tH

c

=b H t t H c

(3)

c

—The eye-hand transformation matrix; t H —The transformation matrix from the Tool Coordinate Point Frame (F tcp ) to b Robot Base Frame (F b ). In order to better describe the relative position relationship about robots, welding torches, welding workpieces, cameras, lasers in the system, the coordinate systems are shown in Fig. 6. The main coordinate system is: the robot base coordinate system F b that is used as the world coordinate system. The tool coordinate system F tcp with the tip end of the welding torch as the origin point. The camera coordinate system Fc sees the center point of the camera optical axis as the origin point, the imaging plane as the two axes, and the third axis is perpendicular to the imaging plane. The robot base coordinate system F b is used as the world coordinate system in the robot controller, which is the end point of the conversion. F tcp is used as the world coordinate system with TCP (Tool Center Point) as the origin. The TCP in the welding robot system is the wire tip point. And the transformation from F tcp to F b is attained by calibration with the tool coordinate system of the robot. These two are important world coordinate systems. We can get the two feature points according to the former image processing. Finally, we convert the image coordinate value into the basic coordinate system of the robot according to the camera matrix and eye-hand matrix that has been calibrated in advance. We make the first point as P1 (x 1 , y1 , z1 , w1 , p1 , r 1 ) and the other point as P2 (x 2 , y2 , z2 , w2 , p2 , r 2 ). And the welding initial point for next pass is P(x, y, z,

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w, p, r). We can make y as Formula (4) or Formula (5) and z as Formula (6). The m is the count of unfilled pass in the same layer, if the unfilled pass is closed to P1 , we use 5-3 to decide the y value. Instead, we use 5-4. The z value is set as 5-5, ψ is the distance of welding wire and the filled bead in z axis. In this paper, we set it to 1.5 mm. For y, w, p, r, it is set to default. y = y1 + (y2 − y1 )/2m

(4)

y = y2 − (y2 − y1 )/2m

(5)

z = (z 1 + z 2 )/2 + ψ

(6)

4 Acquisition of Weld Deposit Amount As Formula (7) shows, the wire feeding speed, welding speed and wire diameter determine the filling area of the weld bead, where D represents the wire diameter, V f represents the wire feeding speed, Vw represents the welding speed, and η represents the welding efficiency which less than 1 due to the presence of weld spatter.  S =η∗π ∗

D 2

2 ∗

Vf π D2 V f =η Vw 4Vw

(7)

In the actual welding, since the welding current and the wire feeding speed of the automatic welding machine are automatically adjusted and matched, the qualitative analysis of the welding current can be equivalent to the wire feeding speed. Therefore, it can be said that the cross-sectional area of the bead is determined by the welding current, welding speed, wire diameter and deposition efficiency. In order to explore the relationship between the welding current and the cross-sectional area of the bead in multi-pass welding, the welding experiments were performed every 20 A in the current range of 160–260 A. The corresponding laser stripe images were collected. The feature points of laser stripes were extract by the above methods and converted to the robot base coordinates by coordinate transformation. Figure 7 is the actual bead contour drawing, the adjacent bead profiles are superimposed, and the trapezoidal integral Formula (8) is used to calculate the cross-sectional area of the bead at the corresponding current, where zi represents the difference value of the adjacent bead at the height, the yi represents the step size. xn

∫ f (x)d x ≈ 0

n   1 i=1

2

 (z i + z i−1 )(yi − yi−1 )

(8)

As Fig. 8 shows, the obtained data are represented in the coordinate graph and fitted with linear relationship. As can be seen from the figure, with the increase of

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(a) The first pass

(b) The second pass

(c) Weld overlay Fig. 7 Overlay drawing of adjacent weld bead

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Fig. 8 Diagram of welding bead section and current

welding current, the cross-sectional area of welding bead also increases, and they are close to a linear relationship.

5 Experimental Verification In order to verify the accuracy of the above image processing and feature point extraction process, we calculate the position information of the welding torch by extracting the feature point information, and compare it with the actual arc initial point to analyze the error range. Figure 9 is the actual picture for each weld bead. Figure 10 is the laser stripe for each pass in the workpiece. As is shown in Table 2, the y-axis and z-axis coordinate value of the welding bead arc initial point determined by the feature point extraction algorithm and coordinate transformation are compared with the actual coordinate, and the error is within 1.2 mm. Figure 11 shows the actual picture of the workpiece after welding. In order to see the layout order and general section shape of the welding bead, the end length of the current welding bead was deliberately made about 2 cm shorter than that of the previous welding bead in the experiment. It can be seen from the figure that each welding bead is well formed and the workpiece has a good forming appearance after welding. At the same time, the collected laser profile of each bead section is converted to three-dimensional coordinates by the matrix obtained by calibration, and the coordinates of the y-axis and the z-axis are represented by a coordinate graph. As is shown

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(a) 1st

(b) 2nd

(d) 5th

(c) 3rd

(e) 6th

(d) 4th

(f) 7th

Fig. 9 Post-weld workpiece Table 2 Error analysis for arc initial points Pass sequence

Y value

Z value

Calculation result of features recognition

Actual welding initial point

Error/mm

Calculation result of features recognition

Actual welding initial point

Error/mm

2

176.243

3.1

172.382

175.931

0.312

204.758

203.925

0.833

171.215

1.167

208.008

208.912

0.904

3.2 4.1

180.774

180.041

0.733

208.000

209.121

1.121

169.241

168.228

1.013

211.334

210.813

0.521

4.2

183.037

182.529

0.508

211.443

210.256

1.187

4.3

177.059

177.826

0.767

211.543

210.485

1.058

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(c) 3rd

(e) 6th

(d) 4th

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Fig. 10 Laser contour for each bead

Fig. 11 The workpiece appearance after welding

in Fig. 12, the actual restoration drawing of the weld cross section can be obtained. It can be concluded that in the actual welding, the shape of the weld bead is irregular, we can simplify it theoretically, but in the actual situation, the bead feature points should be used to make corresponding corrective measures according to different purposes.

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Fig. 12 Laser overlay of the bead

6 Conclusion In this paper, the welding initial point of torch for every pass in medium thick plate using laser stripe sensor is studied. We develop the image processing process to extract the two feature points, and convert them into the basic coordinate system of the robot with the calibrated matrixes. According to the research, we can draw the following conclusions: 1. In order to test the accuracy of the result, we compare the calculated position of the welding torch with the actual position and the error is within 1.2 mm in x and z axis. 2. At last, we convert the all laser stripes into three-dimension coordinate, and we can see the shape of every pass clearly. The shape of is irregular, not parallelogram or trapezoid. Acknowledgements This work is supported by the National Natural Science Foundation of China (61873164).

References 1. Zhou LP, Han YG (2011) Current status and development trend of welding automation technology in China. Sci Technol Inf 19:120–124 2. Wang B (2001) Current status and development trend of welding automation technology in China. Weld Technol 31(6):3–7 3. Zhao J, Lin SB (2010) Introduction to laser sensors for automated welding. Electr Weld Mach 40(11):1–5 4. Xu YL, Lin T, Chen SB (2010) Application status and research development trend of welding robot. Metal Work 8:32–36 5. Chen HB, Kong M, Lv N et al (2017) Status and development of vision on intelligentized robotic welding technologies. Electr Weld Mach 47(3):1–16 6. Chen SB, Wu J (2009) Visual sensing systems for arc welding process. In: Intelligentized methodology for arc welding dynamical processes. Springer, Berlin, Heidelberg, pp 35–55 7. Chen SB, Lv N (2014) Research evolution on intelligentized technologies for arc welding process. J Manuf Process 16(1):109–122

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8. Hou Z, Xu YL, Huang SJ et al (2016) Application status of vision sensing used in robotic welding. J Shanghai Jiao Tong Univ (s1):55–58 9. Lin T, Chen HB, Li WH et al (2009) Intelligent methodology for sensing, modeling, and control of weld penetration in robotic welding system. Ind Rob 36(6):585–593 10. Chen XZ, Chen SB, Lin T (2006) A simple method for initial welding position positioning of plane weld based on vision. Trans China Weld Inst 27(3):73–76 11. Xi F, Song YF (2006) Study of weld tracking control system based on visual sensor. Instrum Techn Sens 5:30–31 12. Yang W (2015) Automatic welding robot path planning and application instance based on 3D vision technology. Electr Weld Mach 45(3):37–42 13. Fan C, Lv F, Chen S (2009) Visual sensing and penetration control in aluminum alloy pulsed GTA welding. Int J Adv Manuf Technol 42(1–2):126–137 14. Shuang YQ, Chen WZ, Wang KJ et al (2008) Vision system for closed-loop control of the penetration in laser-MIG hybrid welding. J Tsinghua Univ (Sci Tech) 11:1891–1894 15. Yang CD (2015) The intelligentized robotic double-sided arc welding technology and system for thick plate of offshore drilling platform legs. Dissertation, Shanghai Jiao Tong University 16. Zhang X (2015) Research on robotic welding system and multi-pass planning based on laser vision sensor. Dissertation, Shanghai Jiao Tong University 17. Gu F (2017) Research on robotic MAG adaptive welding and planning based on laser vision sensor. Dissertation, Shanghai Jiao Tong University 18. Yang XJ, Xu YL, Huang SJ et al (2016) A kind of recognition algorithm for feature points of V-groove welds based on structured light. J Shanghai Jiao Tong Univ 10:1573–1577 19. Zhang HJ, Zhang GJ, Cai CB et al (2009) Laser-based visual recognition of multi-pass seam in robot arc welding. Trans China Weld Inst 30(4):105–108 20. Li Y, Xu D, Shen Y et al (2006) A image processing and features extraction method for structured light image of welding seam. Trans China Weld Inst 27(9):25–30 21. Zhang L, Xu Y, Du S et al (2017) Point cloud based three-dimensional reconstruction and identification of initial welding position. Trans Intell Weld Manuf 1(3):61–77

Yanhui Lai was born in China in 1993. He received the bachelor degree in materials science and engineering from Central South University, Changsha, China, in 2016. He studies in intelligentized robotic welding technology laboratory for a master’s degree in Shanghai Jiao Tong University since September 2016 until now. His research direction is multi-pass welding with laser vision in robot automation.

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Selection of Arc Spectrum Features and Defect Recognition in GTAW Based on Random Forest Zhe Yang, Guangrui Wen, Wenjing Ren and Zhifen Zhang

Abstract Because of the high redundancy of welding spectrum, the efficiency of welding recognition model cannot meet the requirements. Aiming at these problems, an on-line detection method for multiple welding defects is proposed in this paper. The background spectrum of the original spectrum is removed in order to remove trend items, and the multi-dimensional line ratio characteristics are extracted. Then, the feature importance index is constructed based on the MDA (Mean Decrease Accuracy) and MDG (Mean Decrease Gini). The feature selection is carried out by quantitatively evaluating the multi-dimensional spectral features, and the important spectral features are selected and analyzed. Normal and three types of defects, including incomplete penetration, burn through, and porosity, are distinguished by a random forest model effectively. By comparing the results of different feature recognition, the feature selection effectively removes the useless features, redundant features, and improves the computational efficiency of the subsequent models. The results of the important spectral feature analysis can provide guiding significance for the subsequent feature line selection. Comparing with RBF and BP neural network, the random forest model achieves higher identification rate, more stable results, and can be applied for on-line detection of welding defects. Keywords Welding defects · On-line detection · Arc spectrum · Random forest

1 Introduction With the rapid development in aerospace technology, welding technology has been widely used in the manufacture of complex equipment of aerospace [1, 2]. Arc welding, which is a complex physical and chemical process. Welding arc is used as a carrier of welding energy and contains a large amount of information about welding quality [3]. The spectral information of the arc deriving from the welding arc radiation contains information on the composition of the arc. The characteristic Z. Yang · G. Wen · W. Ren · Z. Zhang (B) School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-8668-8_6

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lines of each specific wavelength of the spectrum have clear physical meanings corresponding elements. It can reflect different welding defects by detecting the changes of different elements, which is very suitable for on-line detection of defects in the welding process [4]. At present, there are two methods for the detection of welding quality in terms of arc spectral information detection based on plasma temperature Te and arc spectral intensity respectively. Mirapeix [5] found that the Te has obvious mutations in the defects of burn through and oxidized burning in the TIG welding of stainless-steel plate; Alfaro [6] collects the arc spectrum on the surface of the molten pool in GMA welding. Te is calculated in terms of the emission lines of Fe and Mn elements, and it is further found that the standard deviation of Te is larger when welding defects occur. However, the plasma temperature calculation formula has a lot of error, and the positioning accuracy of the experiment is very high. In addition, because of the inherent randomness and complexity in the welding process cannot accurately reflect the change of the welding process. The intensity of the arc spectrum directly reflects the intensity of the arc radiation, which can more fully reflect the dynamic changes of the welding process, and does not bring quadric errors, so it is more suitable for actual welding. Li [7] studied the characteristics of different welding method defects, and found that different characteristic bands have different characteristic responses under different interference factors. Huang [8] used the argon spectrum and hydrogen line intensity ratio characteristics to get quantitative detection of aluminum alloy weld porosity defects successfully. The arc spectrum includes much of redundant information. The redundant information not only reduce the efficiency of information processing, but also may have bad effects on the analysis and modeling process. Developing a multi-defect recognition model with less effective key feature information and high recognition efficiency is a key problem in on-line defect detection using spectral information [9]. Mirapeix [10] applied the SFFS algorithm to classify and weld the weld spectrum information including the characteristic line spectrum and the continuum. Yu [11] applied the PCA (principal component analysis) to reduce the dimensionality of the spectral features. They studied the dimensionality reduction and spectral line selection of spectral information, and made preliminary selections of spectral features, but did not extract a significant feature subset of the spectrum yet. In this paper, the spectral line strength of several elements in the welding process is extracted, and two methods are used to remove the trend term, which improves the universality of the features. Through the constructed feature importance evaluation index, a 6-dimensional salient feature subset was selected from the multi-dimensional spectral features. The validity of the feature evaluation index was verified by comparing different features. The random forest model is used to realize the effective identification of multiple defects in welding. This method effectively removes a large number of redundant features and useless features, and has fast recognition speed and high accuracy.

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2 Random Forest Random forest [12] is a kind of important method on integrated learning. It has a wide range of applications in various classification and regression problems. Its basic unit is the decision tree. Decision trees have good characteristics, such as low computational complexity, fast prediction process, and displaying models easily. The proudure of random forest using to solve the classification problem as follows: (1) Supposing the size of the forest to be constructed is k and the size of the training set is N. Each tree is resampled by Bootstrap method, and is sampled k times independently. Each time N samples are randomly selected, k training data sets S can be formed, and they are IID (independent and identically distributed). (2) Each autonomous sample set is used to build a classification tree. Let the characteristic dimension of be M, and the growth process of the single  the sample √  tree is as follows: m m ≈ M features are randomly selected from the M features, and the decision tree selects the best split from each of the m features every time when decision tree is splitting. It allows each tree to grow sufficiently until the purity of each leaf node is minimized with no pruning process. (3) According to the constructed classifier, a new unknown sample is predicted, and the classification result of the unknown sample is determined according to a simple majority voting method of the voting result of each tree classifier. In the random forest model, there are two indicators of the importance of the characteristics. One is based on the OOB (out of bag) error method, called MDA (Mean Decrease Accuracy), which randomly smashes the eigenvalues of the sample data outside the bag, then retests the OOB error of each tree. The MDA is the average of difference of the two OOB tests error. And its formula is as follows: ⎛ ⎞  1  1 ⎝   x¯ j I h k (i) = yi − I (h k (i) = yi )⎠ Ja (x j ) = k B ∈C |Bk | i ∈B / k

k

where: yi is the classification label in the ith OOB, I is an indication function, and x¯ h k (i) is a classification label of the sample i predicted by the data set Bk . h k j (i) is a classification label after the replacing feature. The other is based on the Gini impure method, called MDG (Mean Decrease Gini), which is calculating the difference between the Gini index before the classification and the Gini index after the classification. The Gini index expression is: Gini( p) =

n  i=1

pk (1 − pk ) = 1 −

n 

pk2

i=1

Among them, pk the probability of the nth category among the n categories. Its formula is as following:

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Jb (x j ) = Gini(D) −



|D2 | |D1 | Gini(D1 ) + Gini(D2 ) |D| |D|

Among them, D1 , D2 are two categories in which the set D is divided according to the features. It can be seen that the two characteristic importance indicators in the random forest model are larger, the importance degree of the features is higher from the calculation process and the results. Both methods evaluate the importance degree of features are based on the quality of the classification results.

3 Experiment Method The test piece using for the test was a 5A06 aluminum alloy with a length of 300 mm, a width of 50 mm and a thickness of 4 mm. The current using in the experiment is pulse square wave alternating current, the peak period and the base period are both 0.5 s, the peak current is 240 A, the base current is 50 A, the current pulse frequency is 1 Hz, and the AC frequency is 50 Hz (Fig. 1). The welding robot in the welding spectrum acquisition system is Yaskawa NX100/HP6 robot with AvaSpec-1350F-USB2 spectrometer which has the highest range up to 65,000 counts, the number of pixels is 1322, the shortest integration time is 0.7 ms, and the measurable wavelength range is 360–1100 nm. To ensure that the spectrometer probe is relatively stationary with the arc during the welding process, the spectral probe is fixed to the welding robot arm aligned with the arc center. In order to ensure a moderate range of the acquisition process, a full-wavelength dimming plate with a transmittance of 10% is placed in front of the probe. The distance between the probe and the arc center is 21 cm, and the spectral integration time is 1.5 ms. In order to prevent the welding quality from because of the low temperature of the base metal in the initial stage of welding, the welding gun is kept stationary for 8 s before the arcing, the welding speed is 16 cm/min, the arc length is kept at 3 mm, and the argon flow rate is 15 L/Min, the wire feed speed is 12 mm/s. According to different experimental conditions, different welding parameters were controlled and adjusted. After obtaining good welding effect parameters, the

Fig. 1 Arc spectrum data acquisition system

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Fig. 2 Welding defect specimen

(1) Porosity defect

(2) Incomplete penetration

(3) Burn through

incomplete penetration defect test pieces by canceling the base material preheating time. A burn-through defect test piece was obtained is obtained by reducing the local thickness of the base material. In this paper, the porosity defects are obtained by applying asphalt at the fixed position of the weld. At this stage, the serious porosity defects are studied qualitatively. The subsequent research will be carried out on minor defects. The front and back welds of the welded defect test piece obtained from the experiment are shown in Fig. 2.

4 Spectral Signal Preprocessing and Feature Extraction 4.1 Spectral Signal Preprocessing There are a lot of spectra line in the spectra which are closely related to the welding process state, among which there are three kinds of lines: hydrogen lines, argon line and metal line. By comparing the experimental data with the atomic radiation spectrum data provided by the National Institute of Standards and Technology (NIST), we found 20 argon spectral lines, 1 hydrogen spectral line, and 4 metal spectral lines which are apparently distinguishable lines. The arc spectrum is formed by superposition of continuum and line spectra at a certain moment. The continuum is complicated and hard to analysis. Generally, the line spectrum is need to select as the analysis object, so the line spectrum is separated from the background spectrum. In this paper, the local minimum value of

Strength/counts

Fig. 3 Removal of continuous spectrum by spectral signal

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4

x 10

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the step search is used, and the local minimum value obtained by the search is used to perform cubic spline interpolation to obtain the lower envelope signal of the spectral signal. The background spectrum value can be calculated perfectly. The signal, that is, the line spectrum signal is obtained when the background spectrum is subtracted from the original spectral signal. As can be seen in Fig. 3, the line spectrum and the continuous spectrum can be better separated. In this paper, the pulse alternating current is used, so the spectral intensity at the current base value and peak value is quite different. At the peak current, the arc light is strong, the spectral signal intensity is high. At the weak current the arc light is weak. the spectral signal intensity is weak. So the spectral signal exhibits a periodic variation of the same frequency as the current. In order to remove the pulse interference and compare the sensitivity of the base value and the peak value to the defect, the mean value of the base value and the peak mean value in the cycle are calculated as shown in Fig. 4. In Fig. 4, there is a significant burn-through defect at the mark of the weldment. It can be seen that the spectral information can reflect the defect at the peak of the current, while the spectral information at the base value changes little during the entire welding process. Therefore, in the subsequent calculations, the spectral signal at the peak of the current is used, which reduces the amount of calculation effectively.

4.2 Feature Extraction When aluminum alloy is welded, liquid aluminum can dissolve a large amount of hydrogen while hardly in solid state. Therefore, most of the porosity produced by aluminum alloy welding are hydrogen porosity. The formation of porosity is a process that the gravity and flow resistance of liquid metal and the metal vapor coming to

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Fig. 4 Mean value of argon spectrum signal for burn through defect

14000

Fig. 5 Peak value of spectral line of porosity defect

Al spectrum line H spectrum line Mg spectrum line

12000

strength/counts

10000 8000 6000 4000 2000 0

0

20

40 Time/s

60

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a dynamically balanced. Hydrogen may carry some metal ions during the escape process, resulting in an increase in the concentration of metal ions in the arc range and an increase in the intensity of the line. The mean value of the spectral intensity can be seen from Fig. 5. The hydrogen line and the metal line have a significant improvement when porosity defects occur. In different penetration states, the effective arc length is different. It has been shown that [11, 13], the intensity change of the argon spectrum can reflect the change of the arc length effectively, so the penetration state can be reflected by the intensity of the argon spectrum. However, during the welding process, the whole spectral intensity of the welding has a tendency of decreasing. The welding experiments of different batches are interfered by random factors, and the signal intensity is a little different. In order to eliminate the influence of the environment on the characteristic signals and improve

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Al spectrum line H spectrum line Mg spectrum line

12000

strength/counts

10000 8000 6000 4000 2000 0

0

20

40 Time/s

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Fig. 6 The ratio of spectral to background spectrum of porosity defect

the versatility of the features, a dimensionless index of different spectral intensity ratio is used instead of the absolute intensity index. The hydrogen spectrum and the metal spectrum adopt the ratio of line spectrum intensity and background spectrum as it can be seen in Figs. 5 and 6, the method effectively removes the influence of the trend term. However, the number of argon spectral lines is large and has a similar trend. Due to the response characteristic curve of the spectrometer, the argon spectrum with smaller wavelength difference is more similar. In order to reflect the trend of argon spectrum change to let the characteristics of the argon spectrum mutation are more prominent, the method of using ratio of adjacent argon spectra is adopted. As shown in Figs. 4 and 6, the method can not only remove the trend, but also makes the features of the defect more significant (Fig. 7). By calculating the mean value of the line intensity, 19 argon spectrum ratio characteristics, 4 metal spectrum and background spectrum ratio characteristics, and 1 hydrogen spectrum and background spectrum ratio characteristics were obtained. At the same time, the 1-dimensional hydrogen spectrum and the argon spectrum ratio of the literature was extracted [8]. So there is a total of 25 dimensions. However, the signals with the same mean value are not exactly change the same. In order to further describe the fluctuation of the signal around the mean, the peak mean and standard deviation of the intensity ratio of each line are calculated. Therefore, this paper has extracted the 50 features of the line intensity ratio.

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Strength ratio

1.75

Normal Burn through

1.7 1.65 1.6 1.55 0

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60

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Strength ratio

1.65 1.6 1.55 1.5 0

10

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40 Time/s

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Normal Incomplete penetration 60 70 80

Fig. 7 Argon spectrum line (763.51 nm/706.72 nm) ratio of different welding defects

5 Spectral Feature Selection and Defect Classification 5.1 Spectral Feature Selection Based on Random Forest In this paper, the intensity ratio characteristics of different spectral lines are calculated, and two time-domain indexes, mean value and standard deviation are calculated. But there are also many redundant and useless features. In order to select the most effective feature which can reflect the welding defects, a feature importanceindex based on Gini index and accuracy is constructed in this paper. The feature importance is evaluated qualitatively. According to the recognition accuracy curve, the 6-dimensional significant spectral features are selected from the multidimensional features. Among the 190 samples, 43 are normal, 43 are burned out, 44 with porosity defects, and 60 with incomplete penetration defects. In order to verify the stability of the algorithm and its adaptability to various situations, 150 samples were randomly selected in each experiment to construct a random forest model, and the remaining 40 samples were used for testing. Generally, the larger the forest scale is, the more stable the model will be. In order to ensure the stability of the model, the random forest scale is set to 500, and the samples are input into the model. Two important indicators of characteristics are obtained as shown in Fig. 8. As can be seen from Fig. 8, although the two methods are different, the results are very similar. In order to facilitate the study of feature importance, the two feature importance indices are normalized and the mean values of the two indices are calculated. The feature importance indices based on Gini index and accuracy are obtained. The formula is:

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Fig. 8 Importance index diagrams with different characteristics

Mean decrease in Accuracy

magnitude

0.08 0.06 0.04 0.02 0

0

10

0

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magnitude

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30 40 feature Mean decrease in Gini index

50

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Fig. 9 Model accuracy of different characteristic numbers

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X: 6 Y:10.9712 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 0.55

 Jc (i) =

0



k=a,b

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20

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50

max(Jk ) − Jk (i) /2 max(Jk ) − min(Jk )

According to the importance-index, the features are sorted in descending order, and the first n (n = 1, …, 52) features are input into the random forest for recognition. The number and accuracy of the features are shown in Fig. 9. As it can be seen from Fig. 9, the average accuracy rate reaches 97.12% with the input of the six most important features. When more features are input, the recognition accuracy fluctuates. It shows that only first six features are needed to identify the correct rate has been stabilized at a higher level, and when the input features continue, the accuracy rate will be slightly higher. The reason is that after inputting 6-dimensional features and then continuing to input features, some worse

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features will be inputted, which reduces the recognition accuracy of the model. With the more features inputted, the feature completeness becomes higher, making the model tend to be stable, but at the same time the feature redundancy becomes larger. From the above analysis, the most important six features basically cover the most effective features for identifying different welding conditions. The experimental computer environment is configured as Inter (R) Core (TM) i34150 [email protected] GHz, the test environment is Windows 8.1, and the programming environment is MATLAB R2014a. Table 1 compares the recognition accuracy and calculation speed of 6-dimensional salient features with all 50-dimensional features. It can be seen that the calculation speed of 6-dimensional salient features is increased by 72.52% when the accuracy of 6-dimensional salient features is almost unchanged compared with all features, which can effectively improve the recognition efficiency of welding defects. The physical meaning of 6-dimensional salient feature importance ranking is shown in Table 2. It can be seen that the 6-dimensional salient features include the ratio of the metal spectrum, the ratio of the hydrogen spectrum to the background spectrum, and the ratio of the different argon spectra. On the one hand, it is shown that different spectral lines have different weld defects. Sensitivity, on the other hand, shows that the various line intensities change when they are in different welding states. The significant features selected are the peak mean characteristics of the line intensity, indicating that the mean line intensity is more likely to reflect the change in the weld state than the standard deviation.

Table 1 Recognition results of different input numbers feature

Table 2 Six salient features of physical meaning

Input characteristic number

Average accuracy rate

Time

6

97.12

0.0739 s

50

98.15

0.2689 s

Change rate (%)

−1.03

72.52

Importance ranking

Physical meaning

1

Mean value of Al (394.61 nm) to background spectrum

2

Mean value of H (656.28 nm) to background spectrum

3

The mean value of Ar (811.53 nm) and Ar (810.36 nm) ratio

4

The mean value of Ar (842.46 nm) and Ar (840.82 nm) ratio

5

The mean value of Ar (811.53 nm) and Ar (826.45 nm) ratio

6

Mean value of Mg (383.83 nm) to background spectrum

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Ar(842.46nm)

strength/counts

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Ar(840.82nm)

6

x 10

Ar(826.45nm)

7

Ar(810.36nm) Ar(811.53nm)

Fig. 10 Spectral local signal map

845

850

There are a lot of argon spectral lines between 600 and 900 nm in the arc spectrum, which are rich in redundant information. In this paper, 19-dimensional argon spectral ratios are extracted. After constructing the characteristic importance index, 3 argon spectral ratios are extracted from the 6-dimensional salient features. 5 argon spectral lines involved in the three argon spectral ratios are found in the spectral information. The 3 argon spectral ratios can be characterized by the following characteristics: (1) It can be seen from Fig. 10 that the wavelength difference between the two argon spectra is small. The reason is that the response characteristic curve of the spectrometer has not changed much. When the analog signal is converted into the digital signal, the spectral signal characteristics are similar. (2) The background spectrum is small and the background spectrum is stable. The formation mechanism of arc signal in welding process is complex, the background spectrum is relatively stable and the intensity is small, which indicates that this wave band signal is mainly excited radiation, avoiding other forms of radiation nonlinear interference. (3) The intensity of spectral radiation is high, the spectral lines are obvious, there is no interference from other elements around the spectral lines, and the reading error of spectral line intensity is small. The summary of the characteristics of argon spectra can be helpful to the selection of spectral lines for many elements in the future.

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Table 3 Recognition accuracy of 6 features with different feature importance indexes Feature

Method

Highest correct rate (%)

Minimum correct rate (%)

Average correct rate (%)

Variance

The most important 6 features

RF

100

90

97.37

0.0001

RBF

65

20

43.55

0.0098

BP

97.5

52.5

83.7

0.0128

The least important 6 features

RF

60

35

49.63

0.0058

RBF

40

17.5

28.75

0.0037

BP

52.5

10

31.9

0.0073

5.2 Recognition of Welding Defects Based on Random Forests MDA and MDG, which are used in this paper, are calculated from the classification results of random forests. It is still necessary to verify the applicability of other models. In order to verify the validity of the constructed feature importance index, three methods, random forest, RBF neural network and BP neural network, are used to carry out the comparison. As can be seen from Table 3, for the six-dimensional features with different feature importance index, the three recognition methods are feature recognition with high feature importance index. The correctness rate is significantly higher than that of the low feature importance index, which shows that the feature importance index constructed in this paper can effectively evaluate the importance of features in the model. In order to compare the classification effect of different models, the same features are put into different models. Compared with RBF neural network and BP neural network, the recognition accuracy of random forest is the highest and the result is the most stable.

6 Conclusion In order to solve the problem that the intensity of spectral lines decreases with the welding process, the method of ratio of the same spectral line spectrum to the background spectrum and ratio of different spectral lines of the same element are adopted. The trend term is removed effectively and the universality of the characteristics is improved. The change of the numerical value can reflect the welding quality. With abundant spectral information, it also has great redundancy. In this paper, a feature importance index based on Gini index and accuracy is constructed. The 6-D salient feature selected from 50-D feature contains the most effective feature for identifying different welding defects. In this paper, the characteristics of 3-D argon

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spectra are analyzed. It is found that the wavelengths of 3-D argon spectra are close, the background spectra are relatively low and flat, and there is no interference from other spectral lines. The analysis results have guiding significance for the selection of characteristic spectral lines of elements. By comparing the feature recognition results with different importance indicators, the validity of the constructed feature importance indicators is verified. By comparing with BP and RBF neural networks, it is found that the random forest method has high recognition accuracy and stable recognition results. On-line inspection of normal and incomplete penetration, burn through, and porosity weld defects is achieved. Acknowledgements The work was supported by the National Natural Science Foundation of China (51605372, 51775409, 51365051 and 51421004), the China Postdoctoral Science Foundation Funding (2018T111052 and 2016M602805), the Program for New Century Excellent Talents in University (NCET-13-0461).

References 1. Liu ZH, Zhao B, Zhao Q (2002) Prospects for welding technology of aluminum alloy in aerospace industry in 21st century. Missile Spacefl Transp Technol 5:63–68 2. Fang LJ, Kan TT, Liu XJ et al (2015) Aluminum alloy welding process in aeronautics and astronautics. Sci Technol Innov Appl 17:49 3. Jiang F, Li YF, Chen SJ (2018) Current situation and prospects of welding arc monitoring technology. J Mech Eng 54(2):16–26 4. Yang YQ, Li JY, Li H et al (2001) Test and control technique in developing of welding arc spectrum. Welding 6:11–13 5. Mirapeix J, Cobo A, Conde OM (2006) Real-time arc welding defect detection technique by means of plasma spectrum optical analysis. NDT & E Int 39(5):356–360 6. Alfaro SCA, Mendonça DS, Matos MS (2006) Emission spectrometry evaluation in arc welding monitoring system. J Mater Process Tech 179(1–3):219–224 7. Li ZY, Gu XY, Li H et al (2009) Study on the arc spectral information for welding quality diagnosis. Spectrosc Spect Anal 29(03):711–715 8. Huang Y, Wu D, Zhang Z et al (2017) EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM. J Mater Process Tech 239:92–102 9. Yu HW, Ye Z, Zhang ZF et al (2013) Arc spectral characteristics extraction method in pulsed gas tungsten arc welding for Al-Mg alloy. J Shanghai Jiao Tong Univ 47(11):1655–1660 10. Mirapeix J, Garcíaallende PB, Conde OM et al (2012) Welding diagnostics by means of particle swarm optimization and feature selection. J Sens 2012(3):11 11. Yu HW (2013) Research on dynamic process and defect features of aluminum alloy pulsed GTAW based on the welding arc spectral information. Dissertation, Shanghai Jiao Tong University 12. Leo B (2001) Random forests. Mach Learn 45(1):5–32 13. Li P, Zhang YM (2002) Robust sensing of arc length. IEEE Trans Instrum Meas 50(3):697–704

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Zhe Yang was born in Shandong Province, China, in 1993. He received the B.S. degree from Shandong University, Weihai, China, in 2016. He is currently working toward the M.S. degree at the School of Mechanical Engineering, Xi’an Jiaotong University. His research interests include welding defect detection and welding spectral data mining.

Zhifen Zhang was born in Taigu County, Shanxi Province, China, in 1984. She received the B.S. and M.S. degrees in materials processing engineering from the Lanzhou University of Technology, Lanzhou, China, in 2007 and 2010, respectively, and the Ph.D. degree in materials processing engineering from Shanghai Jiao Tong University, Shanghai, China, in 2015. Since October 2015, she has been a Lecturer with the Department of Mechanical Engineering, Xi’an Jiao Tong University, Xi’an, China. Her current research interests include quality monitoring of welding manufacturing, multisensory data fusion, and mechanical fault detection.

Bead Geometry Prediction for Multi-layer and Multi-bead Wire and Arc Additive Manufacturing Based on XGBoost Junhao Deng, Yanling Xu, Zhangchi Zuo, Zhen Hou and Shanben Chen

Abstract In the process of multi-layer and multi-bead wire and arc additive manufacturing (WAAM), the geometry of each layer has an important influence on the dimensional accuracy and surface quality of the final forming parts. In this paper, machine learning model is used to predict the geometrical morphology of multi-layer and multi-channel WAAM forming parts. A series of experiments of WAAAM under different parameters were carried out by rotating combination experiment, and the bead geometry of the forming parts were obtained by visual sensing system developed by ourselves. Aiming at the problem of less data samples which would lead to over-fitting in the process of model training, this paper introduces the XGBoost algorithm for modeling. Compared with the neural network algorithm, the regression prediction model of arc additive manufacturing morphology based on XGBoost has a higher prediction accuracy. Keywords WAAM · Cladding forming · XGBoost algorithm · Prediction model · Point cloud · Machine learning

1 Introduction With the ability to resolve the shortcomings of low energy utilization, low production efficiency and high cost in the additive manufacturing process of some metal materials, WAAM (Wire and Arc Additive Manufacturing) has broad application prospects in the integrated progressive forming of large parts. However, the dimensional accuracy and surface quality of parts fabricated by multi-layer multi-bead arc additive manufacturing are still poor due to the accumulation of heat and forming errors. To solve this problem, the current research mainly focuses on forming J. Deng · Y. Xu (B) · Z. Zuo · Z. Hou · S. Chen (B) School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China e-mail: [email protected] S. Chen e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-8668-8_7

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path planning [1, 2], experimental process parameters [3, 4], and forming process monitoring [5–7]. In an intelligent wire and arc additive manufacturing system, only when the size of each welding bead is detectable, predictable and controllable can we get ideal modeling parts. To determine the optimal experimental parameters, it’s necessary to establish a predictive model which can estimate the dimension of formed parts on the basis of experimental parameters. Nowadays, many machine learning models have been used for modelling in the field of welding. For example, in literature [8], the extreme learning machine (ELM) was used to predict the depth of fusion. literature [9] used the neural network to establish a penetration state prediction model based on the sound signal. literature [10] used the data collected by the automobile manufacturer to establish a prediction model of weld width based on the decision tree model. Practice has proved that the use of machine learning is very effective in predicting the forming process of the WAAM, but most of these studies use the data (sound, arc, etc.) in the arc forming process to model, and mainly focus on the control of depth of fusion and penetration during the welding process. Less variables and data can be obtained from the research on shape prediction based on the initial forming parameters in WAAM, and the accuracy of the model is not high. As a result, there is little research in this area. During the multi-layer multi-bead WAAM process, heat accumulation has a great influence on the morphology of the formed parts. At present, the inter-pass temperature is simply set as constant value and it is objectively not comprehensive and accurate. In this paper, the initial temperature of deposition is introduced in the modeling process besides deposition current, deposition voltage along with travel speed, and a machine learning model was established based on the dimensions (height H, width W) of the formed parts obtained by the visual sensor. Since the modeling of dimension prediction model for components fabricated by WAAM is a highly nonlinear process, fewer training samples make it difficult for the trained model to learn the mapping relationship in the process of manufacturing and easy to be caught up in the problem of overfitting. In the field of machine learning, boosting integrated lifting algorithm has strong learning ability and it is a better method for resolving over-fitting problems. The XGBoost algorithm is an excellent engineering Boosting algorithm. This paper uses it to model the size of the dimension of welding bead in arc additive manufacturing and compares it with the model established by neural network. It is found that XGBoost algorithm has higher prediction accuracy and better generalization ability.

2 Experimental Process 2.1 Experimental System The experimental equipment used for wire arc additive manufacturing control mainly consists of industrial robot (Fanuc M-20ia), GMAW welding power supply, industrial

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Fig. 1 The experimental system

computer and supporting software, line structured light vision sensor, infrared temperature sensor, hall voltage sensor, etc. The self-developed structured light vision sensor is used for measuring the size of the weld bead during the forming process. The infrared temperature sensor is used to collect the temperature of deposited layers at the beginning of the deposition of each bead, and the Hall voltage sensor is applied to collecting the voltage in the process of WAAM (Fig. 1).

2.2 Design of Experimental Method There are many parameters affecting the dimension of welding bead fabricated by multi-layer multi-bead WAAM. Based on the heat input process, the controllable parameters are selected as deposition current, deposition voltage and travel speed, and the inter-pass temperature at the beginning of the deposition of each bead was introduced as an adjustment parameter as heat accumulation during deposition has a great influence on the morphology of welding bead. Through the preliminary experimental research, the ideal range of each experimental parameter is obtained. With the purpose of reflecting the influence of each test parameter on the morphology of welding bead with less trials, this paper uses the rotary combination method to design an experiment and the experimental parameters are shown in Table 1. In order to simulate the multi-bead forming environment of multi-bead deposition in WAAM, a single-layer multi-channel arc additive manufacturing process was carried out on a Q235 steel plate, and the size of the plate is 50 mm * 100 mm * 30 mm. During the experiment, each steel plate was preheated before deposition, then the deposition process was carried out under a set of parameters composed of the same

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Factor levels

Deposition current (A)

Deposition rate (cm/min)

Interpass temperature (°C)

−1.682

120

20

70

−1

145

23

90

0

175

25

115

1

200

28

130

1.682

250

30

150

Fig. 2 Experimental forming parts: a physical drawing of forming parts; b schematic diagram of linear structured light scanning

deposition current and travel speed. Various initial inter-pass temperature between different welding beads was obtained by changing the cooling time (Fig. 2).

3 Measurement of Welding Bead Point cloud data reflecting the three-dimensional shape of the welding bead can be obtained through the line structured light vision sensor during the process of scanning the workpiece. As shown in Fig. 3, a multi-layer multi-bead arc additive manufacturing software method used for appearance detection and prediction was developed based on C++, which can obtain the height and width of the welding bead at present and fit the cross-section profile of the welding bead. In the shape prediction area, the shape of the welding bead can also be predicted according to the deposition parameters input. A series of processing algorithms have been developed for the point cloud data obtained in the software, as shown in Fig. 4. After the process shown in Fig. 5, a two-dimensional point cloud reflecting the profile of the formed part will be obtained. The software selects the feature points of the welding bead by establishing the firstorder and second-order height difference histograms of each point. Afterwards, by fitting the points between the feature points and correcting the error, the height H

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Fig. 3 Main interface of the software for detecting and predicting the forming morphology of WAAM Fig. 4 Algorithm flow chart of bead geometry detection based on point cloud

and the width W as well as the surface equation of the cross section of welding bead were then obtained. Finally, through experimental verification, it was found that the average detection error of height and width was 7.1 and 5.2% respectively, achieving a high accuracy.

4 Bead Geometry Prediction Model Machine learning model has strong nonlinear fitting ability, thus it can be used to solve the nonlinear problems in arc forming, such as building the model which predicts deposition geometry from deposition parameters. The modeling process of machine learning is to utilize existing data to learn a model that can reflect the

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Fig. 5 Point cloud data preprocessing process diagram

mapping relationship from input data to output data. The quantity and quality of training data is an important factor affecting the accuracy of machine learning model. Theoretically, the more learning samples there are, the better the machine learning model will be. When there are few training data, the trained model can only partially reflect the mapping relationship between input data and output data, but when the data does not appear in the training data set, the prediction accuracy of the model is greatly reduced, that is the problem of overfitting. In the construction of arc additive manufacturing prediction model, the acquisition of training data is the most difficult part, because a large number of experiments are required for each set of experimental data. XGBoost algorithm is a very efficient algorithm in the field of data mining at present, which is based on the principle of integration learning. It introduces many methods to prevent overfitting problem, which makes it have good prediction accuracy in various data sets. In this paper, XGBoost is used to solve the problem of low accuracy and overfitting for learning subject of limited samples, which always occurs in the process of arc additive manufacturing morphology prediction.

4.1 XGBoost Algorithm The XGBoost algorithm is short for the eXtreme Gradient Boosting algorithm. It is an excellent engineering algorithm based on the Gradient Boosting Decision Tree (GBDT) algorithm developed by Dr. Chen Tianqi of the University of Washington [11]. In GBDT algorithm, the learning principle of each tree in the direction of gradient descent is as follows: 1. For the model F m (x) under the current iteration number of round m, it can be expressed as: Fm (x) = Fm−1 (x) + argmin

n  i=1

loss(yi , Fm−1 (xi ) + f (xi ))

(1)

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Fm−1 (x) is the model obtained by the m − 1 iteration, f (x) is the minimized loss function of the function(1), and xi , yi are training samples. 2. To further reduce the loss function, according to the gradient descent method, the direction of movement of the vector is the negative gradient direction of the loss function, i.e.   ∂loss(yn ,Fm−1 (xn )) 1 ,Fm−1 (x 1 )) ∂loss(y2 ,Fm−1 (x 2 )) v = − ∂loss(y (2) , , . . . . . . , ∂ Fm−1 (x1 ) ∂ Fm−1 (x2 ) ∂ Fm−1 (xn ) 3. Get the new training set as shown in (3) and train the next tree.  n i ,Fm−1 (x i )) xi , ∂loss(y ∂ Fm−1 (xi ) i=1

(3)

XGBoost algorithm does a lot of engineering optimization on the basis of traditional GBDT algorithm. Aiming at the modeling goal of this paper, it is mainly as follows: 1. Adding a regular term to the loss function of the algorithm to controls the complexity of the model, which helps to prevent overfitting. 2. A second-order Taylor expansion of the cost function is performed during the algorithm learning process, so that the algorithm can obtain more information. 3. In the process of algorithm learning, the training data samples and features are sampled to reduce the variance of the model.

4.2 Establishment of Prediction Model 4.2.1

Data Preparation and Preprocessing

A total of 125 sets of experimental data were obtained in the experimental part above, and then 120 sets of experimental data were finally obtained by removing some unqualified data. In this paper, deposition current I, cladding voltage U, deposition velocity V and initial interlayer temperature T are set as input data, which is dataset X, and the corresponding deposition height H and width W obtained by the visual sensor are set as output data, which is dataset Y. 110 sets of data were randomly selected as the training set, and 10 sets of data were used as the test set for the training establishment of the model. Table 2 shows the test set data table.

4.2.2

Model Training

In this paper, linear regression algorithm, neural network algorithm and XGBoost algorithm are selected respectively for the prediction and modeling of bead geometry of WAAM. And Mean-square error (MSE) was selected as the evaluation standard of

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Table 2 Test data set Group

I (A)

U (V)

V (cm/min)

T (°C)

W (mm)

H (mm)

1

145.00

21.65

23.00

139.81

10.31

3.03

2

145.00

21.59

20.00

141.53

10.69

3.10

3

250.00

11.79

30.00

112.25

16.50

4.21

4

145.00

21.21

23.00

109.23

10.73

3.11

5

120.00

22.79

28.00

85.36

10.00

2.97

6

145.00

21.06

25.00

100.00

10.46

3.06

7

250.00

11.25

28.00

117.57

16.90

4.28

8

145.00

21.22

23.00

97.57

11.11

3.18

9

175.00

18.89

20.00

75.38

12.01

3.35

10

175.00

19.35

25.00

105.08

11.57

3.27

model prediction accuracy. Because the parameters of the model have great influence on the final precision of the model, it is necessary to adjust parameters to select the best model parameters. For XGBoost algorithm, its main parameters are learning rate, iteration times, decision tree parameters, regularization parameters, etc. The basic process of parameter tuning in this paper is as follows: 1. Select a higher learning rate eta, and find the ideal iteration times n_estimators with the grid search method. 2. For a given ideal learning rate and iteration times, gradually adjust the relevant parameters of the decision tree (gamma, max_depth, etc.). 3. Perform regularization parameters (lambda, alpha) tuning 4. Gradually reduce the learning rate eta and get the final optimal model parameters. Aiming at the neural network algorithm, this paper obtains the optimal neural network algorithm model with sigmoid function as the activation function, learning rate of 0.01 and hidden layer structure of 4 * 8 * 12 * 2.

4.3 Model Results and Analysis In order to verify the prediction accuracy of the XGBoost algorithm, the neural network algorithm and the linear regression algorithm, this paper inputs the test set data into the above trained model, and the predicted results are shown in Table 3. The comparison of the predicted results of the height H and the width W is shown in Fig. 6. As can be seen from Table 3 and Fig. 6, the prediction accuracy of the neural network model and the linear regression model is similar, and both are lower than that of the XGBoost prediction model. It can be concluded that when the experimental data are relatively small, the neural network model cannot well learn the complex nonlinear mapping relationship from the forming parameters to the bead morphology

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Table 3 Comparison of prediction results of each model with actual measurement results Group

Measurements

Linear regression

Neural networks

XGBoost

W (mm)

H (mm)

W (mm)

H (mm)

W (mm)

H (mm)

W (mm)

H (mm)

1

10.31

3.03

10.89

3.24

10.65

3.28

10.75

3.12

2

10.69

3.10

11.76

3.38

10.70

3.36

10.87

3.14

3

16.50

4.21

15.37

4.01

15.66

3.99

16.53

4.21

4

10.73

3.11

10.12

2.91

10.40

3.37

10.93

3.15

5

10.00

2.97

9.01

2.71

8.89

2.81

9.74

2.92

6

10.46

3.06

10.02

2.91

10.00

3.21

10.54

3.08

7

16.90

4.28

16.12

4.10

16.30

4.17

16.82

4.26

8

11.11

3.18

10.01

3.06

10.66

3.10

11.07

3.18

9

12.01

3.35

12.96

3.51

12.83

3.56

11.83

3.31

10

11.57

3.27

11.95

3.40

12.39

3.48

11.66

3.29

Maximum relative error (%)

9.92

8.92

11.08

8.19

4.29

2.78

Average relative error (%)

6.79

5.72

4.85

5.75

1.44

1.05

in the process of wire and arc additive manufacturing. The prediction results of the Bead geometry prediction model trained by XGBoost algorithm are in good agreement with the measurement results, and the prediction accuracy is the highest. The maximum relative errors of the predicted width W and height H are 4.29 and 2.78% respectively, and the average relative errors are 1.44 and 1.05% respectively, achieving a high prediction accuracy.

5 Conclusion In this paper, machine learning is used to predict and model the bead geometry of multi-layer and multi-bead WAAM forming parts. In order to solve the influence of heat input in the process of multi-layer and multi-bead WAAM on the appearance of cladding bead, the initial temperature between cladding layers is introduced into the prediction parameters. After that, 125 sets of experimental data of different parameters were obtained by combined experiments of cladding current, cladding voltage and cladding speed. By using the algorithm software developed by ourselves, the active visual inspection of the cladding track in arc additive manufacturing is carried out, and the shape of the cladding track is measured. By using the obtained experimental data for machine learning modeling, it is found that the prediction accuracy of XGBoost algorithm is the highest. The maximum relative error of the prediction of width W and height H is 4.29 and 2.78% respectively, and the average relative error is 1.44 and 1.05% respectively, achieving a high prediction accuracy. In the experiment, the prediction accuracy of the neural network algorithm is similar to the

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Fig. 6 Comparison diagram of prediction results of each model

linear regression, indicating that when the training data samples are small, XGBoost can better learn the complex nonlinear relationship within the arc cladding process compared with the neural network algorithm, which is suitable for the prediction modeling of wire and arc additive manufacturing forming morphology. Acknowledgements This work is partly supported by the Shanghai Natural Science Foundation (18ZR1421500), and the National Natural Science Foundation of China (61873164 and 51575349).

References 1. Ding DH, Pan ZX, Cuiuri D et al (2016) Bead modelling and implementation of adaptive MAT path in wire and arc additive manufacturing. Robot Comput Integr Manuf 39:32–42

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2. Pan ZX et al (2018) Arc welding processes for additive manufacturing: a review. In: Transactions on intelligent welding manufacturing. Springer, Singapore, pp 3–24 3. Montevecchi F, Venturini G, Scippa A et al (2016) Finite element modelling of wire-arcadditive-manufacturing process. Procedia CIRP 55:109–114 4. Zhang C, Li YF, Gao M et al (2018) Wire arc additive manufacturing of Al-6Mg alloy using variable polarity cold metal transfer arc as power source. Mater Sci Eng A 711:415–423 5. Kwak YM, Doumanidis CC (2002) Geometry regulation of materials deposition in near-net shape manufacturing by thermally scanned welding. J Manuf Processes 4(1):28 6. Xiong J, Zhang GJ (2013) Online measurement of bead geometry in GMAW-based additive manufacturing using passive vision. Meas Sci Technol 24(11):115103 7. Xu FD, Dhokia V, Colegrove P et al (2018) Realisation of a multi-sensor framework for process monitoring of the wire arc additive manufacturing in producing Ti-6Al-4V parts. Int J Comput Integr Manuf 31(8):785–798 8. Wu D et al (2016) Weld penetration identification for VPPAW based on keyhole features and extreme learning machine. In: 2016 IEEE workshop on advanced robotics and its social impacts (ARSO), IEEE, pp 96–99 9. Lv N, Xu YL, Li S et al (2017) Automated control of welding penetration based on audio sensing technology. J Mater Process Technol 250:81–98 10. Ahmed F, Kim KY (2017) Data-driven weld nugget width prediction with decision tree algorithm. Procedia Manuf 10:1009–1019 11. Chen T et al (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, ACM, pp 785–794

Junhao Deng was born in china in 1994. He received the bachelor degree in material processing and control engineering from Huazhong University of Science and Technology, Wuhan, China, in 2016. He studies in intelligentized robotic welding technology laboratory for a master’s degree in Shanghai Jiao Tong University since September 2016. His research focuses on the use of intelligent control methods to improve the forming accuracy of robotic wire and arc additive manufacturing.

Yanling Xu was born in china in 1980. He received the Ph.D. degree in materials processing engineering from Shanghai Jiao Tong University, Shanghai, China, in 2012. Since April 2014, as a lecturer, he works in Shanghai Jiao Tong University, Shanghai, China, intelligent robotic welding technology laboratory. He mainly engaged in researches in robotic welding automation technology, machine vision sensing technology and wire and arc additive manufacturing.

Microstructure and Electrochemical Corrosion Properties of 316L Stainless Steel Joints Brazed with BNi5 Gongxiang Zhao, Jieshi Chen, Qingzhao Wang, Xiao Wei, Jijin Xu, Junmei Chen, Chun Yu and Hao Lu

Abstract The study investigates the microstructure and electrochemical corrosion characteristics of 316L stainless steel joints brazed with BNi5. The experiment was carried out at 1150 °C and the holding time was 10 and 30 min respectively. The gap size was 30, 60 and 100 μm. The interface microstructures of the brazing joint were analyzed by Scanning Electron Microscopy (SEM) and the element composition was measured by Energy Dispersion Spectrum (EDS). The potentiodynamic polarization tests were conducted in the 3.5% NaCl solution using the CHI660E electrochemical workstation. The result shows that there are three zones in the brazing joint: Athermally Solidification Zone (ASZ), Isothermally Solidified Zone (ISZ) Diffusion Affected Zone (DAZ). A lot of Ni based solid solutions are formed in the ISZ and some intermetallics distribute in the solid solution layer parallel to the boundary. The ASZ is in the center of the gap, which contains eutectic structure and some brittle compounds. The Tafel curves reveal that the corrosion resistances of the brazing joints are poorer than base metal because the different phases in the joint form many micro batteries. With the decrease of gap size smaller or the increase of holding time, the corrosion resistance becomes better, because the longer holding time and smaller gap size are beneficial to the formation of Ni based solid solution with high corrosion potential. Keywords 316L stainless steel · BNi5 solder · Vacuum brazing · Microstructure · Corrosion resistance

G. Zhao · Q. Wang · X. Wei · J. Xu · J. Chen · C. Yu · H. Lu (B) Key Lab of Shanghai Laser Manufacturing and Materials Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China e-mail: [email protected] J. Chen School of Materials Engineering, Shanghai University of Engineering Science, Shanghai 201620, China © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-8668-8_8

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1 Introduction With the development of the automobile industry, exhaust pollution has been one of the main sources of air pollution, causing public concern widely. Particulate matter (PM) and oxides of nitrogen (NOx ) are the main pollutants in the exhaust gas which harm to human health and environment [1]. In the past few years, many ways have been taken to reduce the content of PM and NOx in the tail gas. The standard of exhaust emission becomes more and more strict [2]. Therefore, more effective technology must be developed. The cooled exhausted gas recirculation (EGR) system is expected to meet the requirement of reducing the emission of the pollutant [3–5]. The EGR cooler is a heat exchanger used in diesel engine. Its function is to cool a part of the exhaust gas in the EGR cooler and circulate back to the cylinder stator to reduce the combustion temperature and oxygen content in the cylinder, which can obviously reduce the emission of nitrogen oxide and smoke, and save the oil consumption. Due to the requirements of environmental protection and economy, EGR oil cooler has become one of the most important components of automobiles. To improve the efficiency of heat transfer, the structure of the EGR is complex and compact, which require a lot of internal joints [6]. It’s difficult to weld complex and tiny structure to use traditional connection mode. In addition, surface oxide film of base metal leads to bad joint performance in other brazing methods. So, vacuum brazing is a better way to weld complex systems on account of oxide film removed in vacuum environment. Because a small amount of sulfur is contained in the fuel, sulfides will be produced after burning, combined with water vapor to form sulfuric acid. At the same time, there will be more nitric acid due to the existence of NOx in the exhaust gas. In consideration of the high temperature of the exhaust gas and strong acids condition, the materials must have good corrosion resistance. Therefore, stainless steel and Ni-based filler metal are chosen in the practical application [7]. Due to the harsh working environment and structural requirements, it is necessary to have good mechanical and corrosion properties for stainless joints brazed by Nibased filler metals [8]. 316L stainless steel has excellent corrosion performance because it consists of lots of Ni and Cr elements. The single austenite phase is stable due to the high Ni element, avoiding the formation of micro batteries. Cr element can increase the self corrosion potential while the chromium depleted region formed after welding reduced the resistance to intergranular corrosion [9]. Because of its high content of Ni, Ni-base brazing filler metal is more popular in brazing stainless steels than other solders. Continuous nickel based solid solution formed during the brazing process can improve the properties of the joint. B, Si and P added in the solder can reduce the melting point and speed up the diffusion, but these elements are easy to form brittle eutectic with other elements [10]. Contrast with other Ni based solders, BNi5 performs better in the corrosion environment due to high content of Cr elements. In addition, no phosphorus and boron contained in the BNi5 decreases

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the formation of the brittle compound, which improve the mechanical properties and corrosion resistance [11]. The brazing parameters are the main factors affecting the properties of the joints. Compact oxide film on the surface of stainless steel help to prevent corrosion but it hinders the wetting, leading to a poor quality of brazing joint. At high temperatures, vacuum conditions are conducive to the decomposition and volatilization of oxides causing the removal of oxide films on stainless steel surfaces [12]. The higher brazing temperature can accelerate the diffusion of the atoms, leading to fuller metallurgical reaction between base metal and filler metal [13]. The holding time is the duration of isothermal solidification, which decides the amount of the solid solution and its distribution. However, excessive temperature and too long holding time may cause coarse grain, the evaporation of alloying element and formation of chromium-poor zone in base metal. The gap size is another important factor in the quality of brazing joint because it decides the distance of atom diffusion [14, 15]. In this search, reaction mechanism and microstructure of interface in brazing joint are studied. In addition, the influence of gap size and brazing time on the corrosion properties of 316L stainless steel brazed with BNi5 is investigated by electrochemical test and microstructure analysis.

2 Experiments 2.1 Materials and Method The chemical compositions of the 316L stainless steel base metal and BNi5 brazing filler metal are listed in Tables 1 and 2. Si contained in BNi5 act as melting point depressants (MPD) to speed up the diffusion and wetting of the solder to the gap. Two 316L sheets are stack up with BNi5 in the interface as shown in Fig. 1. The brazing filler metal is placed at one side and the braze gaps are controlled by pure nickel foil. Brazing was conducted at 1175 °C under vacuum conditions at 5 × 10−3 Pa with the 10 and 30 min holding time respectively and the specimens were furnace-cooled to room temperature under vacuum. The interface microstructures were observed by Scanning Electron Microscopy (SEM) and the element composition was measured by Energy Dispersion Spectrum (EDS).

Table 1 Composition of 316L SS base metal (wt%)

Table 2 Composition of BNi5 filled metal (wt%)

Alloy

Fe

Cr

Ni

Si

Mn

P

S

316L

Bal

16–18

12–15

1.0

2.0

0.035

0.03

Alloy

Ni

Cr

BNi5

Bal

18.5–19.5 –

Fe

Si

C

9.75–10.5 0.1

P

B

–

0.03

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Fig. 1 Schematic of the sandwich structure

2.2 Electrochemical Test Sample from the sandwich structure was embedded into resin block, connecting a wire. Specimen surface was sanded with 240#, 400#, 800#, 1200#, and 1500# grit silicon carbide paper and polished with the polishing cloth to surface light. The surface was exposed to the 3.5% NaCl solution. Potentiodynamic polarization tests were performed by the standard three-electrode system at room temperature. The counter electrode is a piece of platinum net. A silver-silver chloride electrode serves as the reference electrode which is connected with the sample and the test cell through a salt bridge. The scan rate was 1 mv/s and the scan were performed twice for every specimen to ensure the data reliable.

3 Results and Discussion 3.1 Microstructures of the Brazing Joint The microstructure of brazing joint is exhibited as Fig. 2. Athermally Solidification Zone (ASZ), Isothermally Solidified Zone (ISZ) and Diffusion Affected Zone (DAZ) can be observed clearly [16]. To further confirm specific phases of different zones of brazing joint, the compositions were analyzed by Energy Dispersion Spectrum (EDS). The results were shown in Fig. 3 and the components of main elements composition of five points are listed in Table 3. In the brazing process, with the temperature increasing continuously, filler metal is melted and the liquid solder fills the gap due to the capillary force. During the heat preservation stage, liquid solder came into contact with solid base metal accompany, with the base metal dissolution and element diffusion. According to the result of EDS, the Si, Cr and Ni element content at DSZ (point 1) is higher than the original content of base metal due to the diffusion. Because silicon has low solubility in iron, the Ni-Si participates are formed along the boundary of the base metal. On the contrary, the element of Ni and Fe can be resolved by each other to form the Cr-rich Ni-Fe solid solution. The solid solution forms on the side of the base metal by the mechanism of heterogeneous nucleation when the crystallizing temperature reached. With the extension

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Fig. 2 The microstructure of brazing joint

Fig. 3 The EDS result of line scanning

Table 3 The EDS results of different zones of brazing joint

Component of main element composition (wt%) Point

Ni

Cr

Si

Fe

1

23.40

18.32

2.13

50.11

2

52.42

19.44

4.84

14.27

3

52.57

18.40

6.98

10.60

4

52.76

18.64

5.78

8.35

5

70.54

3.86

12.90

1.89

6

44.45

19.56

10.83

5.45

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of the time, the solid-liquid boundary moves from the surface of 316L stainless steel to the center of the gap due to the continuous formation of solid solution and the ISZ was formed. At the same time, the content of Si decreased and Si concentrates in the center liquid. It is seen in the microstructure image that there is a wide light gray layer in the ISZ, and a small quantity of black structures distribute in the layer by the linear. From ISZ to ASZ, the content of Si increases but Cr and Fe decrease gradually. From the element content of point 2 and 4, it suggests that the gray zones in ISZ were Si-rich Ni-Cr-Fe solid solution. But the solid solution at point 2 contains more Fe due to the shorter diffusion distance. The black line is silicide because Si can not be completely dissolved in Ni-based solid solution [17]. With the holding time finished, the isothermal solidification ended and the cooling process began. Eutectic reaction took place in the central liquid brazing metal and many brittle compound and precipitated phase generated in the center of the gap to form the ASZ. The result of line scanning shows that the content of Si, Cr and Ni element fluctuate heavily which indicates that different phases distribute alternately in lamellar. The ASZ is composed with grey zone (point 5) and black zone (point 6). Because Si was concentrated in the liquid in the isothermal solidification, it suggests that the eutectic structure is Si-rich γ-solid solution, laves phase and silicide.

3.2 Effect of Gap Size and Holding Time on the Microstructure of Brazing Joint Different combinations of the brazing parameters were employed to reveal the effects of the gap size and holding time on the microstructure of joint (Fig. 4). Figure 5 shows the SEM image of 30 μm brazing joints with 10 and 30 min respectively. Because of the dissolve of base metal, the gap width of brazing joint with 30 min holding time increases. There are a narrower ISZ and a wider ASZ in the joints with 10 min holding time than the ones with 30 min holding time. It shows that the long brazing time is beneficial to the formation of the solid solution and the diffusion of the atom. With the continuous formation of solid solutions towards gap center, the liquid decreased gradually. Therefore, the amount of the eutectic structure decreases. When the holding time is 30 min, it can be seen that there are large quantities of eutectic structures in the center of the joint with 100 μm gap size. With the decrease of the gap size, the volume fraction of the solid solution and ISZ grows gradually. This is because the narrower gap can shorten the diffusion distance, which makes atomic diffusion more sufficient. Therefore, the isothermal solidification speeds up and the amounts of solid solution increase. Due to the sufficient diffusion, the compositions of brazing solders are similar to the ones of base metal, which make the properties of solder interface similar with base metal [18].

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Fig. 4 The microstructure (BSE) of brazing joint with different holding time

3.3 The Electrochemical Corrosion Behavior of the Brazing Joint Figure 6 shows the potentiodynamic polarization curves of the brazing joint. From the curve, we can obtain many electrochemical parameters which can indicate the corrosion properties of the brazing joints. The corrosion potential (E corr ) is an electrochemical kinetics parameters of corrosion process and represents how difficult the material begins to be corroded. The higher value of E corr means that the material is more difficulty to be corroded. The corrosion current density (icorr ) implies the corrosion rate [19]. There is a proportional relationship between current density and corrosion rate. Passive range and passive potential (E p ) suggest the ability of forming passive film to resist corrosion. Rp indicates the difficulty of charge transfer across the interface between specimen and electrolyte solution and it also is used to evaluate corrosion resistance. The higher of the value of the Rp , the harder the charge

144 Fig. 5 The microstructure (BSE) of brazed joints with different brazing gap

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(a) 10 min

(b) 30 min Fig. 6 Potentiodynamic polarization curves of brazing joints with different holding time

transfer is, furthermore the greater corrosion resistance. The polarization resistance (Rp ) could be calculated by formula (1). Tafel anodic slope (β a ) and Tafel cathodic slope (β c ) can also be obtained from the curves. All these parameters are displayed in Table 4. R p = βa βc /[2.33i corr (βa + βc )]

(1)

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Table 4 Electrochemical parameters of specimen with different brazing gap and holding time Specimen

E corr (mV)

icorr (A/cm2 )

β a (V)

β c (V)

Rp ( cm2 )

Passive range

10 min 30 μm

−416

1.01 × 10−5

0.111

0.148

2.695 × 103

−235 to 214

10 min 60 μm

−403

1.22 × 10−5

0.278

0.126

3.05 × 103

−90 to 125

10 min 100 μm

−432

2.03 × 10−5

0.558

0.147

2.459 × 103

−310 to −110

30 min 30 μm

−418

0.69 × 10−5

0.351

0.221

8.435 × 103

−278 to 187

30 min 60 μm

−409

0.83 × 10−5

0.434

0.180

6.579 × 103

−270 to 44.8

30 min 100 μm

−427.3

1.01 × 10−5

0.437

0.208

5.988 × 103

−290 to 64.5

316L

−361.9

0.78 × 10−6

0.223

0.104

3.9 × 104

−150 to 385

−460.3

10−5

0.102

0.255

3.362 × 103

BNi5

0.93 ×

The base metal 316L has an excellent corrosion resistance (highest corrosion potential, lowest current density, wider passive range) as a result of the high content of Cr and Ni. Ni can promote steel to form a single austenitic phase, which makes the micro battery not be formed. Cr absorbs the electrons of iron to prevent anodic reactions, resulting in passivation. The corrosion potential of isolated BNi5 is the lowest because several kinds of phases such as solid solution and silicide are generated after brazing. These phases can produce many micro batteries. In the brazing joint, Fe moved from base metal to the joint forming the Fe-Ni solid solution. Corrosion potential can be increased by dissolving chromium into Fe based solid solutions. This is why the potential of all joints is higher than the one of isolated BNi5. There is little difference in corrosion potential of all brazing joint but the icorr and Rp reveal the differences in corrosion properties of the brazing joints. As mentioned above, when brazing, there are solid solution, precipitate and eutectic structure. Therefore, many galvanic couple have been formed, which result in the poor corrosion resistance. The influence of the holding time and gap width on icorr is showed as Fig. 7 showed. The current increases with the gap size larger and icorr with 30 min is lower than ones with 10 min. As we know, with the gap size get smaller, the diffusion gets more sufficient and the more Fe atom enter into the gap, which make the joint have similar performance with base metal. In addition, long holding time is beneficial to the solid solution formation which can decrease the corrosion current and widen the passive range [20].

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Fig. 7 The icorr correlated with holding time and gap size

4 Conclusion According to the results of microstructure and the electrochemical test, the summary can be concluded as followed: (1) The results from the study indicated that the brazed joint can be which divided into three parts. The ASZ is composed of eutectic structure and brittle intermetallic compound. The main organization of ISZ is Ni-based solid solution and silicide. In the DAZ, the compound forms due to the diffusion of the Si and Ni (2) The longer holding time is beneficial to form more solid solution and wider isothermally solidified zone. With the decrease of the gap width, the distance of atom diffusion is shortened. Furthermore, the diffusion of the elements gets more sufficient, which speed up the isothermal solidification process. (3) Different phases form in the gap when brazing and the formation of micro batteries results in the poorer corrosion resistance than 316L stainless steel. Due to the diffusion of Fe atom, there are many Fe-rich Ni based solution to improve the corrosion resistance. (4) With the gap size smaller and holding time longer, the brazing joint has the better corrosion resistance, because the volume ratio of its solid solution is the larger and the Fe content in the solid solution is high. Another reason is less eutectic structure and precipitate forming in the joint. Acknowledgments The work was supported by the National Natural Science Foundation of China (51405297, 51575347 and 51805316) and the Shanghai Science and Technology Development Funds (18FY1424900).

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References 1. Liu L, Ling X, Peng H (2013) Complex turbulent flow and heat transfer characteristics of tubes with internal longitudinal plate-rectangle fins in EGR cooler. Appl Therm Eng 54:145 2. Abd-Elhady MS, Zornek T, Malayeri MR (2011) Influence of gas velocity on particulate fouling of exhaust gas recirculation coolers. Int J Heat Mass Transf 54:838 3. Park SK, Lee JK, Kim HM (2014) Experimental study on the spiral and oval spiral EGR cooler efficiencies in a diesel engine. Heat Mass Transf 50:1783 4. Hong KS, Park JS, Lee KS (2011) Experimental evaluation of SOF effects on EGR cooler fouling under various flow conditions. Int J Automot Technol 12(6):813 5. Jang SH, Hwang SJ, Park SK et al (2012) Effects of PM fouling on the heat exchange effectiveness of wave fin type EGR cooler for diesel engine use. Heat Mass Transf 48:1081 6. Philips NR, Levi CG, Evans AG (2008) Mechanisms of microstructure evolution in an austenitic stainless steel bond generated using a quaternary braze alloy. Metall Mater Trans A 39:142 7. Lugscheider E, Humm S (2002) High-temperature brazing of superalloys and stainless steels with novel ductile Ni-Hf-based filler metals. Adv Eng Mater 4(3):138–142 8. Kangdao, Tsunoda T et al (2016) Microstructure and electrochemical corrosion behavior of fe-cr system alloys as substitutes for ni-based brazing filler metal. Acta Metall Sin (Engl Lett) 29(8):697–706 9. Shabani-Nooshabadi M, Ghoreishi SM, Jafari Y et al (2014) Electrodeposition of polyanilinemontmorrilonite nanocomposite coatings on 316L stainless steel for corrosion prevention. J Polym Res 21(4):416 10. Yuan XJ, Kang CY, Kim MB et al (2009) Microstructure and XRD analysis of brazing joint for duplex stainless steel using a Ni-Si-B filler metal. Mater Charact 60(9):923–931 11. Sun R, Zhu Y, Guo W et al (2018) Microstructural evolution and thermal stress relaxation of Al2O3/1Cr18Ni9Ti brazed joints with nickel foam. Vacuum 148:18–26 12. Yu XY, Xing WQ, Ding M (2016) Ultrasonic semi-solid coating soldering 6061 aluminum alloys with Sn-Pb-Zn alloys. Ultrason Sonochem 31:216–221 13. Zaharinie T, Yusof F, Hamdi M et al (2014) Effect of brazing temperature on the shear strength of Inconel 600 joint. Int J Adv Manuf Technol 73(5–8):1133–1140 14. Li G, Zhang P, Shi H et al (2018) Microstructure and properties of Cr18-Ni8 steel joints brazed with BNi7 + 3%Cu composite solder. Vacuum 148:303–311 15. Luo Y, Zhang Q, Jiang W et al (2017) The microstructure, mechanical properties and fracture behavior of hastelloy C276-BNi2 brazed joint. Mater Des 115:458–466 16. Zhishui Yu, Kun Shi, Zhi Yan (2011) Microstructural evolution during vacuum brazing of 316L stainless steel using nickel-based filler metal. Rare Metal Mater Eng 40(S2):342–346 17. Furong Chen, Jun Liu, Junhui Dong (2014) Microstructure and mechanical properties of vacuum brazed joints of 1cr18Ni9Ti stainless steel. Weld Join 6:18–21 18. Chen ZJ, Yang CJ, Gu XL et al (2012) Effect of brazing temperature and gap on microstructure and mechanical properties of 316L stainless steel brazed joints. Adv Mater Res 418–420(5):1242–1245 19. Chen YY, Hong UT, Shih HC et al (2005) Electrochemical kinetics of the high entropy alloys in aqueous environments—a comparison with type 304 stainless steel. Corros Sci 47(11):2679–2699 20. James JP, Fitzgerald JM, Scully JR (2008) Localized corrosion of a super-austenitic stainless steel (Fe-24Ni-20Cr-6.3Mo-0.22 N) brazed with Ni-based braze (Ni-22Cr-6.3Si-3.8P): effect of braze gap on corrosion resistance. Naunyn-Schmiedebergs Archiv für experimentelle Pathologie und Pharmakologie 226(3):278–300

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Gongxiang Zhao, born in February 1994, is studying for Master’s degree in Material College of Shanghai Jiao Tong University. He received the Bachelor Degree in engineering from Nanjing University of Aeronautics and Astronautics in June 2016. He studies on welding process and welding numerical simulation.

Hao Lu, Doctor of Engineering, Professor, was born in July 1965. He graduated from the School of Materials Science and Engineering, Shanghai Jiao Tong University in 1987, majoring in welding. In March 2000, he received a doctorate in engineering from Shanghai Jiao Tong University. From December 2001 to November 2002, he was funded by Japan Academic Revitalization Association to conduct postdoctoral research in Japan Generation Equipment Technical Inspection Association. He was engaged in numerical simulation of stress and deformation during welding/local heat treatment of structural parts of nuclear power equipment. He is a member of Welding Mechanics and Structural Design and Manufacturing Committee of China Welding Society, Shanghai Welding Society and Member of Japan Welding Society.

Three-Dimensional Printing: Revolutionary Technology for Academic Use & Prototype Development Bramha Swaroop Tripathi, Ritu Gupta and S. R. N. Reddy

Abstract In current scenario, 3D printing is a revolutionary technology for design and development of 3-dimensional digital model and prototyping objects. There are various 3D printing technologies available today. This paper presents the ‘fused deposition modeling’ technique of 3D printing and its applications in making real world objects. Here, a 3D printer is used for the development and implementation purpose. 3D printing is being used to teach both students and pedagogue about 3D printer and to develop 3D printing skills. This technology helps to develop design skills and methodologies for creativity; to create art effects that can be used as learning aids or as assistive technologies in special learning settings. This paper mainly describes how to design and develop various objects and prototypes with this technology. It also focuses on how this technique is helpful and beneficial to educational projects and engineering-based researches. Keywords 3D printer · FDM · PLA · Additive manufacturing · CAD

1 Introduction 3D printing is additive manufacturing technologies that come under new invention of digital fabrication technologies that are gradually disarray the industrial system. 3D printing is effectively being used for the purpose of rapid prototyping and tooling [1]. In this process created a 3D solid object of any shape or geometry from a digital file. An Active prototyping is an automated method whereby 3D objects are rapidly made on a reasonably sized machine connected to a computer containing prints for the 3D object. This innovative method for creating 3D models with the use of FDM technology saves cost and time by eliminating the requirement to design; print and glue together separate model parts. Then, you can design and create a desired 3D model in a simply method using 3D printing [2]. A 3D printed object is created using additive process. In this mechanism, an object is created by printing subseB. S. Tripathi (B) · R. Gupta · S. R. N. Reddy Indira Gandhi Delhi Technical University for Women, Delhi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 S. Chen et al. (eds.), Transactions on Intelligent Welding Manufacturing, Transactions on Intelligent Welding Manufacturing, https://doi.org/10.1007/978-981-13-8668-8_9

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quent layers of material until the complete object is created. Each layer is an easily sliced horizontal cross-section of the object. This mechanism uses materials more efficiently than popular manufacturing techniques, referred as subtractive process Technique [1]. 3D printing has a direct role in education. Pedagogues and students have been using 3D printers in classroom and Product designing. This Technology allow to students externalize their own ideas in a fast and economically way [2]. In current scenario schools and colleges required to integrate additive manufacturing technologies into their curriculum. These technologies help the students to visualize their imaginations and ideas in real world scenario and thus encourage the creativity and researches [3]. The 3D printing technology is used for both prototyping, product design and distributed manufacturing with various field applications in architecture, aerospace, engineering, dental and medical industries, fashion, jewelry, eyewear, education and many other fields.

2 History of 3D Printing A development of 3D printing take place in late 1980s, at that time it was known as rapid prototyping technology because of its fast and cost-effective product development process. Charles W. Hull in 1984 developed the innovative technology of printing 3D objects from digital data file [4]. In 1986, he obtained a patent on this technique named Stereo lithography. Some other technologies similar to Stereo lithography such as Fused Deposition Modeling and Selective Laser Sintering were introduced in 1993. Some other technology named 3D Printing Techniques was patented by Massachusetts Institute of Technology. It was similar to the 2D printing technology named as Inkjet [5]. In 1996 three major products in this area were introduced named as Genesis from Strtasys, Actual 2100 from 3D systems and Z402 from Z Corporation. In the decade of 2000s, the term “additive manufacturing” became popular. In 2005 Z Corporation launched first high definition 3D colour printer in the market named as Spectrum Z510 [6]. In 2006 another 3D printer named RepRap was introduced with the goal of self-replicating 3D printer. In year 2012, alternative 3D printing processes were introduced in the market. 2013 was a year of significant growth and consolidation in this area [7] (Fig. 1).

1990

early component and Prototype model & Expended Deposition tech

2000

Application & Production run & Increase Investment & growth of Devolopment

Fig. 1 Development history of 3d printing technologies

2020

3d printing embedded in our daily live with eco friendly system

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3 Technology of 3D Printing A different range of 3D printing technologies have been emerged, some of them are commercially available while other is in the development phase. Different type of technologies use various methods of additive manufacturing and can be easily categorize by different type of material they are using for 3d print objects [8]. • Liquid Based Technology: involves the process of solidifying the liquid resin known as photopolymer-based Technology. • Powder Based Technology: create 3D objects by selectively adhere together subsequent layers of a fine powder with high accuracy design. • Paper Based Technology: is based on the process of lamination. In this process, successive thin layers of cut paper, metal or plastic are stuck together to build up a Physical Structure or solid object. • Solid Based Technology: object creation is done by extruding a semi liquid material from a print head nozzle. It involves extruding a molten thermoplastic material like PLA, ABS etc. that very quickly sets after it has left the print head. Fused Deposition Modeling (FDM) In this paper Fused Deposition modeling, a solid based technology is used. The FDM technology uses thermoplastic material as filament or metal wire for the purpose of printing layers of object [9]. The filament is filled to an extrusion nozzle from which it is extruded in molten form. As the nozzle gets heated by hot end, it moves in both axes horizontal and vertical by a numerically controlled mechanism Axis of the system [5] (Fig. 2). At first the design of the printing object is created using Designing Software like Solid Works etc. called CAD Software and then send to the 3D printer [10]. Finally, the object is created by extruding melted thermoplastic material to form layers as the material hardens immediately after extrusion from the nozzle. In this type technology is most popularly filament material for 3D Printers: Acrylonitrile Butadiene Styrene Filament Driver Wheel Filament spool

Head

Extrusion head Temp Heat

Nozzle Printed Part

Build platform Bed plate

Fig. 2 Working of fuse deposition modeling process

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and Polylactic acid. Using this technology, we can build different field application part in standard plastics [11]

4 Working of 3D Printer A 3D printer is a Machine that designs and builds 3D objects and products of devices and components using an additive fabrication process. A 3D printer receives digital data from a computer as input. Instead of printing 2D output on paper, a 3D printer builds three dimensional models out of a custom material [5]. In this paper, Rise 3D N2 printer is used for printing the objects (Fig. 3 and Table 1).

Fig. 3 Used printer rise 3D and it’s working nozzle design [11, 14] Table 1 3D printer specification [4, 14] Printer specification Used print technology

FFF

Build volume (W * D * H) mm

12 × 12 × 24 in./305 × 305 × 610

No. of nozzles

Two

Filament type

PLA/PLA+/ABS/PETG

Printing surface/Speed

Build-tak/10–150 mm/

Nozzle diameter

0.4 mm (0.016 in)

Operating temp.

170–300

Used operating system

Embedded Linux

Memory/On-board flash

1 GB/8 GB

Type of ports

Sdcard*1, Usb2.0*4, Ethernet*1

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A 3D printer consists of following Important Parts • The 3D printer utilizes 3 axes for coordination of the Device. The linear fixed rods are maintained at right angles to x, y, z coordinate axis, this part of printer called Axis of System. • A function of extrusion and retraction is used in this system called Extruders. Extrusion is the process of filling the filament into the hot end in liquid form in the 3D printer. Retraction is the method of inserting the melted filament from the hot end. • Hot End: It is also called nozzle end part which is heated to from 160 to 250 °C, depending on the type of material used in 3D printer. The hot end melts the filament and converts in liquid form after that it pushes liquid of filament through the nozzle. • Print Bed: It is the surface of a 3D printer where a printer nozzle down the liquid filament material that make up a 3D object. The main purpose using of this bed in 3d printer used for improve print quality of 3D printed objects and releasing the 3D object when cooled. • A material is used to 3D print objective in successive layers called filament of device. These are available in different variety according to material quality and color. It can be composed of different material like carbon, metal, biomaterials etc. and two common sizes are 1.75 and 3.0 mm. In the 3D printer basically two filament material is used PLA and ABS. This printer uses PLA, which is a biodegradable thermoplastic material. Comparison of 3D Printer Filament Material Properties See Table 2.

Table 2 Comparison of 3D printer filament material Properties [4, 7, 16] Name

Extrusion temp. (°C)

Benefits

ABS (Acrylonitrile Butadiene Styrene)

210–250

Strong, flexible, smooth surface

PLA (Polylactic Acid)

190–240

Low warping, doesn’t require heated build plate, recyclable

HIPS (High Impact Polystyrene)

230–265

Dissolves only in limonene-D so good for support material

PET(E/T/G) (Polyethylene Terephthalate)

210–250

Recyclable, strong, transparent surface

PVA (Polyvinyl Alcohol)

190–210

Dissolvable in water

Nylon

245

Absorbs color well

Ninja Flex

210–240

Prints are flexible

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5 Software Used for 3D Printer 5.1 Design Software Computer-aided design software has made the design process high accuracy and speedily. 3-D CAD has become common place locating rapid prototyping. Computeraided design process is an engineering technology for creating conceptual designing and Product layout or finalize developing of 3D models [12]. In this paper CAD software named Solid Works. This software enables user to create geometry design directly in 3D form. User can be used product layout, strength and dynamic analysis of assembly and the manufacturing processes [4].

5.2 Print Software 3D printer used here uses idea Maker as printing software. Idea Maker is slicing software which prepares 3D models for printing and turns them into .g code file for your Raise3D printer [4, 13]. Following steps are used for printing a 3D Product Design Model: 1. Firstly, before using 3D Printer. Install the 3D print software idea maker 2. Check 3D File which is created in CAD software and chose repair option and also set the parameter according to 3D printer in the idea maker software. 3. After that set the nozzle and Print bed temperature according to the printer guide & filament material properties.

6 Prototype Development This part describes the how the real world implementation of a 3d object takes place. Here the object is design for the casing of a video based attending system embedded device [11]. – Step 1: Prepare the CAD design for Model according the parameter needed using 3D file designing Software (SolidsWork, Autocad etc.) – Step 2: After design the file, Convert the designed object file in Stl, obj etc. format according to 3D Printer Properties (Fig. 4). – Step 3: After Prepare the design model for printing Using slicing Software Idea Maker and Ultimaker. Then set all required parameter (nozzle temp., Print bed temp and support) and also repair your design using software option. Then convert the file in G code format according the 3D printer (Fig. 5).

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Part: - 1

Part: - 2

157

Part: - 3

Fig. 4 Prototype model part design in Stl format

Part:- 1

Part: - 2

Part: - 3

Fig. 5 Prototype model parts design in G-code format

– Step 4: Finally ON the 3D Printer and load the filament in nozzle wait until filament is loaded and after that give the command print by using 3D Printing Machine (Fig. 6).

6.1 Precaution in Using 3D Printer These are following precaution in using 3d Printer • Mechanical: Do not place limbs inside the build area while the nozzle is in motion. The printer nozzle moves in order to create the object. • High Temperature: Do not touch the printer nozzle—it is heated to a high temperature in order to melt the build material. • Always buy replacement parts from the manufacturer or branded for safety related equipment • Choose an area that has adequate ventilation and exhaust capability

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Fig. 6 Final prototype model after design

6.2 Case Studies These are several prototype design case studies done by 3d printer for various project prototypes. It is helping to protect the product hardware and improve the design and look of the product. So we are giving more case studies for final prototype developed and design by 3d printer. These all project done under the various project

Fig. 7 3D Printing design with final prototype model of Project: FIT-WIT

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Fig. 8 3D Printing design with final prototype model of Project: Pollution Measurement System

Fig. 9 3D Printing design with final prototype model of Project: SWASTH

(DIC-MHRD, MEK-3(Mobile Education Kit-3)) in Indira Gandhi Delhi Technical University for Women in Delhi, India. 1. Product Design: FIT-WIT See Fig. 7. 2. Product Design: Pollution Measurement System See Fig. 8. 3. Product Design: SWASTH See Fig. 9. 4. Product Design: Water Level Controller See Fig. 10.

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Fig. 10 3D Printing design with final prototype model of Project: Water Level Controller

7 Conclusion This paper has summarized the importance and utilization of 3D printing technology in education and research-based prototype development. It provides a platform to the students and researchers where they can easily model their ideas into reality. There are many 3D printing organizations providing support to educational environment. The fused deposition modeling used here provides an easy and affordable way of developing strong 3D printing objects. Using this technology, one can easily build a 3D object by him (Fig. 11). It is a clean simple to use and lab friendly technology. Using this technology, students are better to do experiment with their design concepts, test their engineering 3D Printing popularity in different Areas Art 7%

Hobby 5%

Other 4%

Proof of concept 30%

Marketing Samples 9% Education 6%

Production 19%

Prototype 20%

Fig. 11 Popularity chart of 3D printing in different area [15]

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vision and can convert their projects to a working model easily. Thus, 3D printing is a more sustainable, environment friendly, tool-less technology that saves time, manpower and money. It provides a customizable and less complex way for 3D object & Prototype development.

References 1. Simon Ford E, Minshall T (2016) 3D printing in education: a literature review. Research Gate Publication 2. Pandian A, Belavek C (2016) A review of recent trends and challenges in 3D printing. In: Proceedings of the 2016 ASEE north central section conference, American Society 3. Gokhare VG, Raut DN, Shinde DK (2017) A review paper on 3d-printing aspects and various processes used in the 3D-printing. Int J Eng Res Technol 6(06) 4. D Printing Filaments. https://all3dp.com/1/3d-printer-filament-types-3d-printing-3d-filament/ 5. Nale SB, Kalbande AG (2015) A review on 3D printing technology. Int J Innov Emerg Res Eng 2(9) 6. Ramya A, Vanapalli S (2016) 3D printing technologies in various applications. Int J Mech Eng Technol 7(3):396–409 7. Macdonald E, Salas R, Espalin D et al (2014) 3D printing for the rapid prototyping of structural electronics. IEEE Transl, 2169–3536 8. Prabhu T (2016) Modern rapid 3D printer—a design review. Int J Mech Eng Technol, 29–37 9. D Printing Technologies. https://www.sculpteo.com/en/3d-printing/3d-printing-technologies/ 10. Vaccarazza M, Papa V (2015) 3D printing: a valuable resource in human anatomy education. Anat Sci Int 90:64–65 11. D Printing Lab Manuals. http://mobileeducationkit.net/labmanuals/ 12. Al-maliki JQ, Al-Maliki AJQ (2015) The processes and technologies of 3D printing. Int J Adv Comput Sci Technol, 161–165 13. Hashem A, Dayal Y, Savanah M et al (2015) The application of 3D printing in anatomy education. Med Educ Online 20:29847 14. A 3D Printer. https://www.raise3d.asia/ 15. Market Performance of 3D Printing. https://www.forbes.com/sites/louiscolumbus/2015/03/31/ 2015-roundup-of-3d-printing-market-forecasts-and-estimates/#b86596a1b30f 16. Siddharth B, Regina B (2014) 3D printing and its applications. Int J Comput Sci Inf Technol Res 2(2):378–380

Bramha Swaroop Tripathi is working as a senior Project Associate (project Mobile education kit-3(MEK-3) Sponsored by Microsoft University Relation) in Department of Computer Science in Indira Gandhi Delhi Technical University, Delhi, India. He received Master of Technology in VLSI Design and Bachelor of Engineering in Electronics & Communication from RGPV University Bhopal, MP, India. He has 6-year Experience in R&D and Teaching as an Assistant Professor. His current research work area is wireless sensor network, Embedded IOT System, SOC Design, Product Design techniques (3D Printing, Laser Cutting, PCB Design).

Information for Authors

Aims and Scopes Transactions on Intelligent Welding Manufacturing (TIWM) is authorized by Springer for periodical publication of research papers and monograph on intelligentized welding manufacturing (IWM). The TIWM is a multidisciplinary and interdisciplinary publication series focusing on the development of intelligent modelling, controlling, monitoring, and evaluating and optimizing the welding manufacturing processes related to the following scopes: • • • • • • • • • • • •

Scientific theory of intelligentized welding manufacturing Planning and optimizing of welding techniques Virtual and digital welding/additive manufacturing Sensing technologies for welding process Intelligent control of welding processes and quality Knowledge modeling of welding process Intelligentized robotic welding technologies Intelligentized, digitalized welding equipment Telecontrol and network welding technologies Intelligentized welding technology applications Intelligentized welding workshop implementation Other related intelligent manufacturing topics.

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Information for Authors

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Author Index

C Chen, Chen, Chen, Chen, Chen, Chen, Chen,

Chao, 75 Haiping, 75 Huabin, 93 Jieshi, 137 Junmei, 137 Shanben, 3, 75, 93, 125 Xiaoqi, 57

D Dai, Ruilin, 93 Deng, Junhao, 125 F Feng, Jicai, 29 G Gupta, Ritu, 151 H Hou, Zhen, 93, 125 L Lai, Yanhui, 93 Li, Gang, 75 Li, Junzhao, 29 Liu, Yibo, 29 Lu, Hao, 137 Lv, Na, 75

Ren, Wenjing, 109 Ren, Xukai, 57 S Soulard, Baptiste, 57 Sun, Qingjie, 29 T Tripathi, Bramha Swaroop, 151 W Wang, Junwei, 57 Wang, Qingzhao, 137 Wei, Xiao, 137 Wen, Guangrui, 3, 109 X Xu, Jijin, 137 Xu, Jingyuan, 75 Xu, Yanling, 57, 125 Y Yang, Zhe, 109 Yu, Chun, 137 Z Zhang, Zhifen, 3, 109 Zhao, Gongxiang, 137 Zhou, Hao, 93 Zuo, Zhangchi, 125

R Reddy, S. R. N., 151

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