[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121998-en":3,"doc-seo-121998-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121998,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds - read online free","Resistance spot welding (RSW) joint quality is heavily influenced by electrode condition. This research develops a machine-learning tool that automatically evaluates how electrode wear affects weld quality by using failure-load classes derived from two experimental campaigns. Sensor signals from electrode displacement and electrode force embedded in the welding machine are processed to extract predictors, and several algorithms are evaluated. The neural-network model reaches 90% accuracy, enabling manufacturers to detect low-quality welds, reduce redundant compensation welds, and optimize electrode redressing or replacement timing.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMachine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds  \nOriginal  \nMachine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds / Panza, Luigi; Bruno, Giulia; Antal, Gabriel; DE MADDIS, Manuela; RUSSO SPENA, Pasquale. -In: IJIDEM. -ISSN 1955-2505. -ELETTRONICO. - (2024) . [10 . 1007/s12008-023-01733-7]  \nAvailability:  \nThis version is available at: 11583/2986123 since: 2024-02-20T08:30:05Z  \nPublisher:  \nSpringer  \nPublished  \nDOI:10.1007/s12008-023-01733-7  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \nInternational Journal on Interactive Design and Manufacturing (IJIDeM) [https://doi.org/10.1007/s12008-023-01733-7](https://doi.org/10.1007/s12008-023-01733-7)  \nMachine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds  \nLuigi Panza1 · Giulia Bruno1 · Gabriel Antal1 · Manuela De Maddis1 · Pasquale Russo Spena1  \nReceived: 5 October 2023 / Accepted: 15 December 2023 © The Author(s) 2024  \nAbstract  \nThe quality of resistance spot welding (RSW) joints is strongly affected by the condition of the electrodes. This work developsa machine learning-based tool to automatically assess the influence of electrode wear on the quality of RSW welds. Two different experimental campaigns were performed to evaluate the effect of electrode wear on the mechanical strength of spot welds. The resulting failure load of the joints has been used to define the weld quality classes of the machine learning tool, while data from electrode displacement and electrode force sensors, embedded in the welding machine, have been processed to identify the predictorsofthe tool. Some machine learning algorithms have been tested. The most performing algorithm, i.e., the neural network, achieved an accuracy of 90% . This work provides important theoretical and practical contributions. First, the decreasing thermal expansion of the weld nugget as the electrode degradation advances results in a strong correlation between the difference of the maximum displacement value and the last value recorded during the welding and the relative failure load. Then, this work offers a practical decision support tool for manufacturers. In fact, the automatic detection of low-quality welds allows to reduce or eliminate unnecessary redundant welds, which are performed to compensate for the uncertainty of electrode wear. This leads to savings in time, energy, and resources for manufacturers. Finally, general recommendations for the timing of redressing or replacing the electrode are provided in the manuscript based on the company willingness to accept some non-compliant welds or not.  \nKeywords Resistance spot welding · Electrode degradation · Electrode wear · Machine learning · Artificial intelligence · Predictive maintenance  \n1 Introduction  \nResistance spot welding (RSW) is the leading technique for joining metal sheets in many industrial fields, especially in the automotive industry, because of its automatability, easy implementation, and cost-effectiveness [1] . The process  \nB Luigi Panza  \nluigi.panza@polito.it  \nGiulia Bruno  \ngiulia.bruno@polito.it  \nGabriel Antal  \ngabriel.antal@polito.it  \nManuela De Maddis  \nmanuela.demaddis@polito.it  \nPasquale Russo Spena  \npasquale.russospena@polito.it  \n1 Department of Management and Production Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Turin, Italy  \ninvolves the simultaneous action of electric and mechanical energies. At the beginning of welding, two or more overlapped sheets are pressed together by two Cu-based electrodes. Then, a high current flows through the sheets for a short time (hundreds of ms) . The heat generated by the Joule effect locally melts t","cbCaifA6QoouKEcg","https://ap.wps.com/l/cbCaifA6QoouKEcg","pdf",2799410,1,19,"English","en",105,"# Abstract\n# Introduction\n## Resistance spot welding fundamentals\n## Causes of non-compliant spot welds\n## Weld quality assessment methods\n## Industry 4.0 and AI-based quality prediction","[{\"question\":\"How does electrode wear affect the quality of resistance spot welds?\",\"answer\":\"Electrode degradation changes the welding thermal and mechanical behavior, leading to measurable differences in displacement behavior and reduced mechanical strength, which the tool links to weld failure load and quality classes.\"},{\"question\":\"What data and sensors are used by the machine learning tool?\",\"answer\":\"The tool processes signals from electrode displacement and electrode force sensors embedded in the welding machine, converting them into predictors for weld quality classification.\"},{\"question\":\"Which machine learning algorithm performs best and what is its accuracy?\",\"answer\":\"Among tested models, the neural network achieves the highest performance with an accuracy of 90%.\"}]","Machine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds - 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