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

• Autorzy: Kubit Andrzej , Kłosowski Grzegorz , Berezowski Wojciech

Identyfikatory

DOI

10.12913/22998624/185618

• 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

EN

friction welding; prediction; SVM; image classification

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 3
• Strony: 45--57
• Opis fizyczny: Bibliogr. 22 poz., fig., tab.

Twórcy

autor

Kubit Andrzej

• Department of Manufacturing and Production Engineering, Rzeszow University of Technology, ul. Powstańców Warszawy 8, 35-959 Rzeszów, Poland

• https://orcid.org/0000-0002-6179-5359

autor

Kłosowski Grzegorz

• Faculty of Management, Lublin University of Technology, ul. Nadbystrzycka 38 D, 20-618 Lublin, Poland

• https://orcid.org/0000-0001-7927-3674

autor

Berezowski Wojciech

• Department of Mechanics and Machine Building, State Academy of Applied Sciences in Krosno, ul. Żwirki i Wigury 9A, 38‐400 Krosno, Poland

Bibliografia

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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).