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IFIP International Conference on Product Lifecycle Management

PLM 2021: Product Lifecycle Management. Green and Blue Technologies to Support Smart and Sustainable Organizations pp 239–252Cite as

Data-Driven Framework for Electrode Wear Prediction in Resistance Spot Welding

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Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 639))

Abstract

Smart Manufacturing (SM) can be defined as the extensive application of computer-integrated manufacturing and advanced intelligence systems to enable rapid manufacturing of new products, dynamic response to product demand, and real-time optimization of manufacturing production and supply-chain networks. For this reason, SM is now attracting a huge interest in both academic and industrial communities and will probably drive the manufacturing evolution in the next decade. In SM, data play a key role. They can support decisional systems and human operators by helping them to improve production and process control, to monitor continuous production flows, to prevent or detect equipment failures at an early stage, to minimize inefficiencies through the overall supply chain, and so on. In fact, data can be exploited by combining a wide variety of advanced technologies to give machines the ability to learn, adapt, make decisions, and display new behaviours. In this regard, the aim of the study concerns the proposal of a data-driven framework to predict the electrode wear in Resistance Spot Welding process. Electrode wear is the most important factor that introduce high variability and uncertainty in the quality of spot welds. Using an equipped medium-frequency welding machine, various data such as thermal maps of the spot surfaces by passive thermography, electrode surface diameters, electrodes-workpiece contact conditions, process variables, and electrode displacement curves can be collected. These data can be provided as input to a Machine Learning algorithm to predict electrode wear over time, thus ensuring a reliable spot weld process and joint quality.

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Acknowledgments

This work has been funded by the Ministero dell’Istruzione, dell’Università e della Ricerca, Grant/Award Number: TESUN-83486178370409, finanziamento dipartimenti di eccellenza CAP. 1694 TIT. 232 ART. 6.

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Authors and Affiliations

  1. Department of Management and Production Engineering, Politecnico di Torino, Turin, Italy

    Luigi Panza, Giulia Bruno, Manuela De Maddis, Franco Lombardi, Pasquale Russo Spena & Emiliano Traini

Authors
  1. Luigi Panza
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  2. Giulia Bruno
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  3. Manuela De Maddis
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  4. Franco Lombardi
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  5. Pasquale Russo Spena
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  6. Emiliano Traini
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Corresponding author

Correspondence to Luigi Panza .

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Editors and Affiliations

  1. Pontifícia Universidade Católica do Paraná, Curitiba, Brazil

    Osiris Canciglieri Junior

  2. University Grenoble Alpes, Grenoble, France

    Frédéric Noël

  3. École de Technologie Supérieure, Montreal, QC, Canada

    Louis Rivest

  4. Qatar University, Doha, Qatar

    Abdelaziz Bouras

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Panza, L., Bruno, G., De Maddis, M., Lombardi, F., Spena, P.R., Traini, E. (2022). Data-Driven Framework for Electrode Wear Prediction in Resistance Spot Welding. In: Canciglieri Junior, O., Noël, F., Rivest, L., Bouras, A. (eds) Product Lifecycle Management. Green and Blue Technologies to Support Smart and Sustainable Organizations. PLM 2021. IFIP Advances in Information and Communication Technology, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-030-94335-6_17

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  • DOI: https://doi.org/10.1007/978-3-030-94335-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94334-9

  • Online ISBN: 978-3-030-94335-6

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