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The Use of Artificial Intelligence for Quality Assessment of Refill Friction Stir Spot Welded Thin Joints

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Języki publikacji
EN
Abstrakty
EN
This paper presents a machine learning and image segmentation based advanced quality assessment technique for thin Refill Friction Stir Spot Welded (RFSSW) joints. In particular, the research focuses on developing a predictive support vector machines (SVM) model. The purpose of this model is to facilitate the selection of RFSSW process parameters in order to increase the shear load capacity of joints. In addition, an improved weld quality assessment algorithm based on optical analysis was developed. The research methodology includes specimen preparation stages, mechanical tests, and algorithmic analysis, culminating in a machine learning model trained on experimental data. The results demonstrate the effectiveness of the model in selecting welding process parameters and assessing weld quality, offering significant improvements compared to standard techniques. This research not only proposes a novel approach to optimizing welding parameters but also facilitates automatic quality assessment, potentially revolutionizing and spreading the application of the RFSSW technique in various industries
Słowa kluczowe
Twórcy
  • Department of Manufacturing and Production Engineering, Rzeszow University of Technology, ul. Powstańców Warszawy 8, 35-959 Rzeszów, Poland
  • Faculty of Management, Lublin University of Technology, ul. Nadbystrzycka 38 D, 20-618 Lublin, Poland
  • Department of Mechanics and Machine Building, State Academy of Applied Sciences in Krosno, ul. Żwirki i Wigury 9A, 38‐400 Krosno, Poland
Bibliografia
  • 1. Zhang Z, Yang X, Zhang J, Zhou G, Xu X, Zou B. Effect of welding parameters on microstructure and mechanical properties of friction stir spot welded 5052 aluminum alloy. Materials & Design 2011; 3, 2. doi:10.1016/j.matdes.2011.03.058.
  • 2. Kubit A., Kluz R., Trzepieciński T., Wydrzyński D., Bochnowski W. Analysis of the mechanical properties and of micrographs of refill friction stir spot welded 7075-T6 aluminium sheets. Archives of Civil and Mechanical Engineering 2018; 18. doi:10.1016/j.acme.2017.07.005.
  • 3. Nhan PT. Effect of the welding parameters on mechanical properties of AA5083 friction stir welding. Proceedings of 5th International Conference on Green Technology and Sustainable Development, GTSD 2020. doi:10.1109/GTSD50082.2020.9303056.
  • 4. Xiao-Jie Y., Bin W., Xiao-Yan S., Yu-Xin L. Research on adaptive control of medium frequency DC resistance spot welding. International Conference on Artificial Intelligence and Electromechanical Automation (AIEA). IEEE 2021; 30–34. doi:10.1109/ AIEA53260.2021.00014.
  • 5. Shen Z, Yang X, Zhang Z, Cui L, Li T. Microstructure and failure mechanisms of refill friction stir spot welded 7075-T6 aluminum alloy joints. Materials & Design 2013; 44: 476–486. doi:10.1016/J. MATDES.2012.08.026.
  • 6. Kubit A., Trzepiecinski T., Bochnowski W., Drabczyk M., Faes K. Analysis of the mechanism of fatigue failure of the refill friction stir spot welded overlap joints. Archives of Civil and Mechanical Engineering 2019; 19: 1419–1430. doi:10.1016/J. ACME.2019.09.004.
  • 7. Engineering MW-A of civil and M, undefined. Friction stir processing – state of the art. Springer MS Węglowski Archives of civil and Mechanical Engineering, 2018•Springer 2017; 18: 114–129. doi:10.1016/j.acme.2017.06.002
  • 8. Wang X., Mu R., Wang C. numerical simulation and experimental study on self-piercing riveting of DC03/6016 dissimilar sheets. 3rd International Conference on Electron Device and Mechanical Engineering (ICEDME). IEEE 2020; 463–466. doi:10.1109/ICEDME50972.2020.00111.
  • 9. Oh S., Kim H.K., Jeong T.E., Kam D.H., Ki H. Deeplearning-based predictive architectures for self-piercing riveting process. IEEE Access 2020; 8: 116254–116267. doi:10.1109/ACCESS.2020.3004337.
  • 10. Mubiayi M.P., Akinlabi E. Titilayo, Makhatha M.E. Effect of process parameters on tensile strength and morphology of friction stir spot welds of aluminium and copper. 8th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT). IEEE 2017; 48–53. doi:10.1109/ ICMIMT.2017.7917433.
  • 11. Mahgoub A., Merah N., Bazoune A., Al-Badour F. Effect of pin tool profile on mechanical and metallurgical properties in friction stir spot welding of pure copper. 8th International Conference on Mechanical and Aerospace Engineering (ICMAE). IEEE 2017; 381–384. doi:10.1109/ICMAE.2017.8038676.
  • 12. Pizoń J., Gola A. The meaning and directions of development of personalized production in the era of industry 4.0 and Industry 5.0. Lecture Notes in Mechanical Engineering 2023; 1–13. doi:10.1007/978-3-031-09360-9_1/COVER.
  • 13. Kozłowski E., Antosz K., Sęp J., Prucnal S. Integrating sensor systems and signal processing for sustainable production: analysis of cutting tool condition. Electronics 2024; 13, 185. 2023; 13: 185. doi:10.3390/ELECTRONICS13010185.
  • 14. Lonkwic P., Tofil A. Supporting welding work in the aspect of increasing production process efficiency. Advances in Science and Technology Research Journal 2023; 17: 8–14. doi:10.12913/22998624/157418.
  • 15. Kulisz M., Kłosowski G., Rymarczyk T., Słoniec J., Gauda K., Cwynar W. Optimizing the neural network loss function in electrical tomography to increase energy efficiency in industrial reactors. Energies (Basel) 2024; 17: 681. doi:10.3390/EN17030681.
  • 16. Kulisz M., Kujawska J., Aubakirova Z., Zhairbaeva G., Warowny T. Prediction of the compressive strength of environmentally friendly concrete using artificial neural network. Applied Computer Science 2022; 18. doi:10.35784/acs-2022-29.
  • 17. Kłosowski G., Rymarczyk T., Niderla K., Rzemieniak M., Dmowski A., Maj M. Comparison of machine learning methods for image reconstruction using the LSTM classifier in industrial electrical tomography. Energies 2021; 14, 7269. doi:10.3390/EN14217269.
  • 18. Rymarczyk T., Kłosowski G., Cieplak T., Nid- erla K. The use of dual machine learning in industrial electrical tomography. Journal of Physics: Conference Series 2022; 2408: 012023. doi:10.1088/1742-6596/2408/1/012023.
  • 19. Chen K., Yao H., Han Z. Arithmetic optimization algorithm to optimize support vector machine for chip defect Identification. 28th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE 2022; 1–5. doi:10.1109/ M2VIP55626.2022.10041106.
  • 20. Sánchez VDA. Advanced support vector machines and kernel methods. Neurocomputing 2003; 55. doi:10.1016/S0925-2312(03)00373-4
  • 21. Olaseni K, Aliyu S, Bakare K. A fuzzy c-means news article clustering based on an improved sqrtcosine similarity measurement. Science Forum (Journal of Pure and Applied Sciences) 2022; 22. doi:10.5455/sf.olasamkay.
  • 22. Ghanbari T., Mehraban A. Data threshold setting using a new approach based on otsu’s image thresholding. International Conference on Protection and Automation of Power Systems (IPAPS). IEEE 2022; 1–5. doi:10.1109/IPAPS55380.2022.9763224
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-dd9f5d5d-3cc0-4117-b9f3-99d8bfa74859
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