Iranian Journal of Mechanical Engineering Transactions of ISME

Iranian Journal of Mechanical Engineering Transactions of ISME

Laser Welding Seam Tracking in Fully-welded Heat Exchanger Plates using Image Processing Algorithms

Authors
Yasamin Azmi 1
Mina Nourollahi 1
Mohammad Hossein Farshidianfar 2
Pouria Oliazadeh 3
Seyedeh Ghazal Mahdavian Mashhadi 4
1 Bachelor’s Student, Ferdowsi University of Mashhad, Faculty of Engineering, Department of Mechanical Engineering, Mashhad, Iran
2 Assistant Professor, Ferdowsi University of Mashhad, Faculty of Engineering, Department of Mechanical Engineering, Mashhad, Iran
3 Ph.D., Supervisor of Industrial Research Center of TGT, Mashhad, Iran
4 Master’s degree, Research Engineering of CIR, Mashhad, Iran
10.30506/ijmep.2025.2040236.1990
Abstract
Fully welded heat exchangers, a type of plate heat exchanger, are vital for high-pressure and high-temperature applications, where effective sealing is critical to prevent leaks and ensure operational efficiency. In this study, laser welding is utilized to achieve precise sealing of the plates. A primary factor in achieving high-quality laser welding is the accurate detection of the weld path, which is particularly challenging due to the plates’ thin thickness of approximately 1 mm. Manual visual path detection by an operator is labor-intensive, time-consuming, and often lacks the necessary precision for consistent results. As an alternative, a vision system is employed to capture high-resolution images of the weld path before welding begins. These images are processed using a custom-developed computer program to generate the motion path for the CNC machine’s axes. The path detection error is approximately 0.037 mm, demonstrating the program’s high precision. Visual quality control of welds on multiple samples confirms the superior quality and reliability of the developed system, offering significant improvements over traditional methods.
Keywords
Fully-welded heat exchangers, Laser welding, Path generation, Vision system, Image processing, Spline interpolation

Subjects

[1] O. Levenspiel, “Introduction to Heat Exchangers,” pp. 243–249, 1998, doi: 10.1007/978-1-4899-0104-0_12.
 
[2] J.-P. Vasseur, and A. Dunkels, “Industrial Automation,” Interconnecting Smart Objects with IP, pp. 325–333, 2010, doi: 10.1016/B978-0-12-375165-2.00021-1.
 
[3] K. Xu, K. Qin, H. Wu, and R. Smith, “A New Computer-aided Optimization-based Method for the Design of Single Multi-pass Plate Heat Exchangers,” Processes, Vol. 10, No. 4, pp. 767, 2022, https://doi.org/10.3390/pr10040767.
 
[4] Z. Sun, and J. C. Ion, “Laser Welding of Dissimilar Metal Combinations,” Journal of Materials Science, Vol. 30, No. 17, pp. 4205–4214, 1995, https://doi.org/10.1007/BF00361499.
 
[5] W. J. Shao, X. F. Liu, and Z. J. Wu, “A Robust Weld Seam Detection Method Based on Particle Filter for Laser Welding by using a Passive Vision Sensor,” International Journal of Advanced Manufacturing Technology, Vol. 104, No. 5–8, pp. 2971–2980, 2019, https://doi.org/10.1007/s00170-019-04029-x.
 
[6] A. Rout, B. B. V. L. Deepak, and B. B. Biswal, “Advances in Weld Seam Tracking Techniques for Robotic Welding: A Review,” Robotics and Computer-integrated Manufacturing, Vol. 56, pp. 12–37, 2019, doi: 10.1016/J.RCIM.2018.08.003.
 
[7] M. Kos, E. Arko, H. Kosler, and M. Jezeršek, “Remote-laser Welding System with in-line Adaptive 3D Seam Tracking and Power Control,” Procedia CIRP, Vol. 81, pp. 1189–1194, 2019, doi: 10.1016/J.PROCIR.2019.03.290.
 
[8] N. Wei, X. Yin, Z. Li, J. Xi, R. Yang, and N. Kong, “A Seam Tracking Algorithm Utilizing Spatio-temporal Information of Laser Vision in Thin Plate Butt Welding for Shipbuilding,” Advances in Transdisciplinary Engineering, Vol. 37, pp. 74–81, 2023, doi: 10.3233/ATDE230124.
 
[9] Y. Zou, and W. Zhou, “Automatic Seam Detection and Tracking System for Robots Based on Laser Vision,” Mechatronics, Vol. 63, pp. 102261, 2019, doi: 10.1016/J.MECHATRONICS.2019.102261.
 
[10] Y. Xu, G. Fang, N. Lv, S. Chen, and J. Jia Zou, “Computer Vision Technology for Seam Tracking in Robotic GTAW and GMAW,” Robotics and Computer-Integrated Manufacturing, Vol. 32, pp. 25–36, 2015, doi: 10.1016/J.RCIM.2014.09.002.
                                                               
[11] P. Kiddee, Z. Fang, and M. Tan, “An Automated Weld Seam Tracking System for Thick Pate using Cross Mark Structured Light,” The International Journal of Advanced Manufacturing Technology 2016 87:9, Vol. 87, No. 9, pp. 3589–3603, 2016, doi: 10.1007/S00170-016-8729-7.
 
[12] Y. Xu, G. Fang, N. Lv, S. Chen, and J. Jia Zou, “Computer Vision Technology for Seam Tracking in Robotic GTAW and GMAW,” Robotics and Computer-integrated Manufacturing, Vol. 32, pp. 25–36, 2015, doi: 10.1016/J.RCIM.2014.09.002.
 
[13] Y. Ding, W. Huang, and R. Kovacevic, “An On-line Shape-matching Weld Seam Tracking System,” Robotics and Computer-integrated Manufacturing, Vol. 42, pp. 103–112, 2016, doi: 10.1016/J.RCIM.2016.05.012.
 
[14] J. Muhammad, H. Altun, and E. Abo-Serie, “Welding Seam Profiling Techniques Based on Active Vision Sensing for Intelligent Robotic Welding,” International Journal of Advanced Manufacturing Technology, Vol. 88, No. 1–4, pp. 127–145, 2017, doi: 10.1007/S00170-016-8707-0/METRICS.
 
[15] Y. Zou, X. Wei, and J. Chen, “Conditional Generative Adversarial Network-based Training Image Inpainting for Laser Vision Seam Tracking,” Optics and Lasers in Engineering, Vol. 134, pp. 106140, 2020, https://doi.org/10.1016/j.optlaseng.2020.106140.
                             
[16] J. Fan, S. Deng, F. Jing, C. Zhou, L. Yang, T. Long, and M. Tan, “An Initial Point Alignment and Seam-tracking System for Narrow Weld,” IEEE Transactions on Industrial Informatics, Vol. 16, No. 2, pp. 877–886, 2020, https://doi.org/10.1109/TII.2019.2919658.
 
[17] D. M. Boldrin, L. M. Tosatti, B. Previtali, and A. G. Demir, “Seam Tracking and Gap Bridging during Robotic Laser Beam Welding via Grayscale Imaging and Wobbling,” Robotics and Computer-integrated Manufacturing, Vol. 89, pp. 102774, 2024, doi: 10.1016/J.RCIM.2024.102774.
 
[18] H. Golnabi, and A. Asadpour, “Design and Application of Industrial Machine Vision Systems,” Robotics and Computer-integrated Manufacturing, Vol. 23, No. 6, pp. 630–637, 2007, doi: 10.1016/J.RCIM.2007.02.005.
 
[19] S. H. Dau, T. P. Dang, L. N. A. Khang, N. H. Chi, and L. H. Trang, “A Proposal for an Optical System Utilizing Two Lighting Methods Combined with an Offset Mirror System Applied to an Automated Quality Inspection System for Glass Bottle Bodies,” IFMBE Proc, vol. 122, pp. 1033–1046, 2025, doi: 10.1007/978-3-031-90194-2_75.
 
[20] T. Y. Goh, S. N. Basah, H. Yazid, M. J. Aziz Safar, and F. S. Ahmad Saad, “Performance Analysis of Image Thresholding: Otsu Technique,” Measurement, Vol. 114, pp. 298–307, 2018, doi: 10.1016/J.MEASUREMENT.2017.09.052.
 
[21] L. Lam, and S. W. Lee, “Thinning Methodologies—A Comprehensive Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 9, pp. 869–885, 1992, doi: 10.1109/34.161346.
[22] H. L. Weinert, “A Fast Compact Algorithm for Cubic Spline Smoothing,” Computational Statistics & Data Analysis, Vol. 53, No. 4, pp. 932–940, 2009, https://doi.org/10.1016/j.csda.2008.10.036.

  • Receive Date 03 September 2024
  • Revise Date 28 April 2025
  • Accept Date 21 July 2025