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Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control

Year 2025, Volume: 13 Issue: 4, 1758 - 1781, 30.10.2025
https://doi.org/10.29130/dubited.1724335

Abstract

Today, welding automation is a vital technology that boosts efficiency in manufacturing processes while enhancing weld quality by minimizing human intervention. However, deformations caused by high heat and deviations from programming errors in robotic welding can negatively impact welding quality. Existing camera and laser-based seam tracking systems are insufficient in certain scenarios due to factors such as highly reflective surfaces, intense arc radiation, or uneven surface conditions. This article presents a weld seam tracking system based on an angle sensor, called Weld Guide, developed to address the limitations of existing systems. The proposed system features angle sensors that utilize a Contact-based sensor principle to improve the accuracy of the robotic welding torch. The Weld Guide system was designed using SolidWorks software, simulated in RoboDK simulation software, and validated through experimental tests. The prototype was tested on both linear and curved weld seams, and its performance under different control systems was evaluated using an optical microscope. Results from the experiments showed that the Weld Guide system successfully tracked weld seams with a deviation of less than 0.6 mm. The comparison between the trajectories obtained from simulated and actual field tests exhibited a similarity exceeding 97%, demonstrating a high level of accuracy in trajectory-tracking performance. Furthermore, a hybrid approach combining Fuzzy C-Means (FCM) clustering with Proportional Integral Derivative (PID) control was implemented to enable automatic tuning of the PID parameters. By incorporating oscillation levels into the fuzzy logic rules, the optimization was enhanced against sudden changes, thereby preventing error accumulation and excessive oscillations. These findings indicate that the proposed system provides a reliable and cost-effective alternative when optical-based tracking methods fall short.

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Thanks

The authors would like to express their sincere thanks to the editor and the anonymous reviewers for their helpful comments and suggestions. The authors also acknowledge the support of ZETEST Quality Control Laboratory in Ankara for providing facilities for microstructural examinations. This study was conducted as part of a doctoral thesis entitled “Mechanical Feedback and Artificial Intelligence-Based Optimization in Robotic Welding

References

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Bulanık C-Means Kümeleme ve PID Kontrolü Kullanılarak Robotik Kaynak Dikişi Takibinin Simülasyon Tabanlı Optimizasyonu

Year 2025, Volume: 13 Issue: 4, 1758 - 1781, 30.10.2025
https://doi.org/10.29130/dubited.1724335

Abstract

Günümüzde kaynak otomasyonu, imalat süreçlerinde verimliliği artırırken, insan müdahalesini en aza indirerek kaynak kalitesini iyileştiren hayati bir teknolojidir. Ancak, robotik kaynaktaki yüksek ısı kaynaklı deformasyonlar ve programlama hatalarından kaynaklanan sapmalar, kaynak kalitesini olumsuz etkileyebilir. Mevcut kamera ve lazer tabanlı dikiş takip sistemleri, yüksek yansıtıcı yüzeyler, yoğun ark ışınımı veya düzensiz yüzey koşulları gibi faktörler nedeniyle bazı senaryolarda yetersiz kalmaktadır. Bu makalede, mevcut sistemlerin sınırlamalarını aşmak amacıyla geliştirilen, açı sensörüne dayalı bir kaynak dikişi takip sistemi olan Weld Guide sunulmaktadır. Önerilen sistem, robotik kaynak meşalesinin doğruluğunu artırmak için temas bazlı ölçüm prensibini kullanan açı sensörleri içermektedir. Weld Guide sistemi SolidWorks yazılımı ile tasarlanmış, RoboDK simülasyon yazılımında modellenmiş ve deneysel testlerle doğrulanmıştır. Prototip, hem doğrusal hem de eğrisel kaynak dikişlerinde test edilerek çeşitli kaynak koşulları altında performansı değerlendirilmiştir. Deney sonuçları, Weld Guide sisteminin kaynak dikişlerini 0,6 mm’den daha az sapma ile başarılı bir şekilde takip ettiğini göstermiştir. Simülasyon analizlerinin gerçek saha testleriyle karşılaştırılması ise %97’den fazla benzerlik oranı ortaya koymuştur. Ayrıca, Fuzzy C-Means (FCM) kümeleme ve PID kontrolünü birleştiren hibrit bir yöntem uygulanarak PID parametrelerinin otomatik ayarlanması sağlanmış ve ani hata değişikliklerine karşı optimizasyon iyileştirilmiş, böylece hata birikimi ve salınımlar önlenmiştir. Bu bulgular, önerilen sistemin optik tabanlı takip yöntemlerinin yetersiz kaldığı durumlarda güvenilir ve ekonomik bir alternatif sunduğunu göstermektedir.

References

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  • Cibicik, A., Tingelstad, L., & Egeland, O. (2021). Laser scanning and parametrization of weld grooves with reflective surfaces. Sensors, 21(14), Article 4791. https://doi.org/10.3390/s21144791
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  • Haeri, H., Talehjerdi, A. G., Sarfarazi, V., Moayer, A., & Marji, M. F. (2024). A finite element analysis of the effects of TADAS dampers on the frame members’ performances in concrete structures under Cyclic and monotonic loadings. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 49(2), 1133-1156. https://doi.org/10.1007/s40996-024-01540-4
  • Han, J., Shan, X., Liu, H., Xiao, J., & Huang, T. (2023). Fuzzy gain scheduling PID control of a hybrid robot based on dynamic characteristics. Mechanism and Machine Theory, 184, Article 105283. https://doi.org/10.1016/j.mechmachtheory.2023.105283
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  • Hong, L., Wang, B., Xu, Z., & Lv, D. (2018, August). Algorithm and application of inverse kinematics for 6-DOF welding robot based on screw theory. In 2018 International Conference on Information Technology and Management Engineering (ICITME 2018) (pp. 159-163). Atlantis Press. https://doi.org/10.2991/icitme-18.2018.32
  • Huang, Y., Abu-Dakka, F. J., Silvério, J., & Caldwell, D. G. (2020). Toward orientation learning and adaptation in cartesian space. Transactions on Robotics, 37(1), 82-98. https://doi.org/10.1109/TRO.2020.3010633
  • Huynh, C. T., & Phung, T. C. (2024). Research on welding seam tracking algorithm for automatic welding process of X-shaped tip of concrete piles using laser distance sensor. Robotica, 42(6), 1796-1815. https://doi.org/10.1017/S0263574724000535
  • Ibrahim, I. N., & Al Akkad, M. A. (2017). Studying the disturbances of robotic arm movement in space using the compound-pendulum method. Bulletin of IzhSTU named after MT Kalashnikov, 20(2), 156-159. https://doi.org/10.22213/2413-1172-2017-2-156-159
  • Keles, A. E., Haznedar, B., Kaya Keles, M., & Arslan, M. T. (2023). The effect of adaptive neuro-fuzzy inference system (ANFIS) on determining the leadership perceptions of construction employees. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 47(6), 4145-4157. https://doi.org/10.1007/s40996-023-01146-2
  • Li, G., Hong, Y., Gao, J., Hong, B., & Li, X. (2020). Welding seam trajectory recognition for automated skip welding guidance of a spatially intermittent welding seam based on laser vision sensor. Sensors, 20(13), Article 3657. https://doi.org/10.3390/s20133657
  • Li Sr, B., Qin, X., Lei, J., Zeng, Y., Zhang, J., Jia, B., Li, H., & Li, Z. (2021, November). Detection method of power line under uneven lightness for flying-walking power line inspection robot. In Proceedings of the 2021 International Conference on Image, Video Processing, and Artificial Intelligence (Vol. 12076, pp. 46-52). SPIE. https://doi.org/10.1117/12.2611653
  • Lin, C. H., Wang, K. J., Tadesse, A. A., & Woldegiorgis, B. H. (2022). Human-robot collaboration empowered by hidden semi-Markov model for operator behaviour prediction in a smart assembly system. Journal of Manufacturing Systems, 62, 317-333. https://doi.org/10.1016/j.jmsy.2021.12.001
  • Liu, F., Wang, Z., & Ji, Y. (2018). Precise initial weld position identification of a fillet weld seam using laser vision technology. The International Journal of Advanced Manufacturing Technology, 99(5), 2059-2068. https://doi.org/10.1007/s00170-018-2574-9
  • Lu, S., Zhou, J., & Zhang, J. (2015). Optimization of welding thickness on casting-steel surface for production of forging die. The International Journal of Advanced Manufacturing Technology, 76 (5), 1411-1419. https://doi.org/10.1007/s00170-014-6371-9
  • Luo, J., Zhu, L., Wu, N., Chen, M., Liu, D., Zhang, Z., & Liu, J. (2022). Adaptive neural-PID visual servoing tracking control via extreme learning machine. Machines, 10(9), Article 782. https://doi.org/10.3390/machines10090782
  • Morsi, N. M. (2024). Feasibility testing of robotics inspection by advanced simulation for aerospace structures (Doctoral thesis, Glasgow Caledonian University).
  • Nguyen, H. C., & Lee, B. R. (2014). Laser-vision-based quality inspection system for small-bead laser welding. International Journal of Precision Engineering and Manufacturing, 15(3), 415-423. https://doi.org/10.1007/s12541-014-0352-7
  • Petschnigg, C., Breitenhuber, G., Breiling, B., Dieber, B., & Brandstötter, M. (2018, March). Online simulation for flexible robotic manufacturing. In Proceedings of the International Conference on Industrial Technology and Management (pp. 88-92). IEEE. https://doi.org/10.1109/ICITM.2018.8333925
  • Ramalingam, S., Rasool Mohideen, S., Manigandan, S., & Prem Anand, T. P. (2021). Hybrid polymer composite material for robotic manipulator subject to single link flexibility. International Journal of Ambient Energy, 42(5), 514-521. https://doi.org/10.1080/01430750.2018.1557551
  • Reddy, J., Dutta, A., Mukherjee, A., & Pal, S. K. (2024). A low-cost vision-based weld-line detection and measurement technique for robotic welding. International Journal of Computer Integrated Manufacturing, 37(12), 1538-1558. https://doi.org/10.1080/0951192X.2024.2314784
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There are 46 citations in total.

Details

Primary Language English
Subjects Electronics, Sensors and Digital Hardware (Other), Resource Technologies
Journal Section Articles
Authors

Adem Dilbaz 0000-0002-3135-7032

İlker Ali Ozkan 0000-0002-5715-1040

Ziya Özçelik 0000-0002-6567-2671

Publication Date October 30, 2025
Submission Date June 24, 2025
Acceptance Date September 29, 2025
Published in Issue Year 2025 Volume: 13 Issue: 4

Cite

APA Dilbaz, A., Ozkan, İ. A., & Özçelik, Z. (2025). Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control. Duzce University Journal of Science and Technology, 13(4), 1758-1781. https://doi.org/10.29130/dubited.1724335
AMA Dilbaz A, Ozkan İA, Özçelik Z. Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control. DUBİTED. October 2025;13(4):1758-1781. doi:10.29130/dubited.1724335
Chicago Dilbaz, Adem, İlker Ali Ozkan, and Ziya Özçelik. “Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control”. Duzce University Journal of Science and Technology 13, no. 4 (October 2025): 1758-81. https://doi.org/10.29130/dubited.1724335.
EndNote Dilbaz A, Ozkan İA, Özçelik Z (October 1, 2025) Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control. Duzce University Journal of Science and Technology 13 4 1758–1781.
IEEE A. Dilbaz, İ. A. Ozkan, and Z. Özçelik, “Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control”, DUBİTED, vol. 13, no. 4, pp. 1758–1781, 2025, doi: 10.29130/dubited.1724335.
ISNAD Dilbaz, Adem et al. “Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control”. Duzce University Journal of Science and Technology 13/4 (October2025), 1758-1781. https://doi.org/10.29130/dubited.1724335.
JAMA Dilbaz A, Ozkan İA, Özçelik Z. Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control. DUBİTED. 2025;13:1758–1781.
MLA Dilbaz, Adem et al. “Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control”. Duzce University Journal of Science and Technology, vol. 13, no. 4, 2025, pp. 1758-81, doi:10.29130/dubited.1724335.
Vancouver Dilbaz A, Ozkan İA, Özçelik Z. Simulation-Based Optimization of Robotic Weld Seam Tracking Using Fuzzy C-Means Clustering and PID Control. DUBİTED. 2025;13(4):1758-81.