Resistance spot welding is used to join two or more metal sheets by overlapping them and producing a localized spot weld through the simultaneous application of pressure and heat to the weld area by two electrodes. In case of unsuitable choices taken when setting the welding process, some issues might arise on the assembled workpiece, and even cause some damage to the structure of the welded materials. The presence of expulsion, for instance, is frequently used as a welding quality indicator. The present work aims to address the challenge of automatically detecting the presence of expulsions during the welding process. By implementing an automated expulsion detection system, manufacturers can proactively monitor electrode condition and schedule maintenance or replacement before weld quality begins to deteriorate. To achieve this goal, a deep learning algorithm, specifically a Convolutional Neural Network model, is proposed. The implementation of such an algorithm offers significant advantages in the quality control process of resistance spot welding operations.

Convolutional Neural Network for Quality Monitoring and Predictive Maintenance in Resistance Spot Welding / Bruno, Giulia; Antal, Gabriel; Traini, Emiliano; De Maddis, Manuela (ADVANCES IN COMPUTATIONAL INTELLIGENCE AND ROBOTICS BOOK SERIES). - In: Real-World Applications of AI InnovationELETTRONICO. - [s.l] : IGI Global, 2024. - ISBN 9798369342527. - pp. 307-330 [10.4018/979-8-3693-4252-7.ch015]

Convolutional Neural Network for Quality Monitoring and Predictive Maintenance in Resistance Spot Welding

Bruno, Giulia;Antal, Gabriel;Traini, Emiliano;De Maddis, Manuela
2024

Abstract

Resistance spot welding is used to join two or more metal sheets by overlapping them and producing a localized spot weld through the simultaneous application of pressure and heat to the weld area by two electrodes. In case of unsuitable choices taken when setting the welding process, some issues might arise on the assembled workpiece, and even cause some damage to the structure of the welded materials. The presence of expulsion, for instance, is frequently used as a welding quality indicator. The present work aims to address the challenge of automatically detecting the presence of expulsions during the welding process. By implementing an automated expulsion detection system, manufacturers can proactively monitor electrode condition and schedule maintenance or replacement before weld quality begins to deteriorate. To achieve this goal, a deep learning algorithm, specifically a Convolutional Neural Network model, is proposed. The implementation of such an algorithm offers significant advantages in the quality control process of resistance spot welding operations.
2024
9798369342527
9798369342534
Real-World Applications of AI Innovation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2995562
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