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Abstract

Artificial Intelligence (AI) has proven effective in assisting manufacturing companies to achieve Zero Defect Manufacturing. However, certain products may have quality characteristics that are challenging to verify in a manufacturing facility. This could be due to several factors, including the product’s complexity, a lack of available data or information, or the need for specialized testing or analysis. Prior research on using AI for challenging quality detection is limited. Therefore, the purpose of this article is to identify the enablers that contributed to the development of an AI-based defect detection approach in an industrial setting. A case study was conducted at a transmission axle assembly factory where an end-of-line defect detection test was being developed with the help of vibration sensors. This study demonstrates that it was possible to rapidly acquire domain expertise by experimenting, which contributed to the identification of important features to characterize defects. A regression model simulating the normal vibration behavior of transmission axles was created and could be used to detect anomalies by evaluating the deviation of new products compared to the model. The approach could be validated by creating an axle with a built-in defect. Five enablers were considered key to this development.

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Acknowledgements

This work was partially supported by the Industrial Technology (IndTech) Graduate School founded by the Knowledge Foundation (KKS, Stockholm, Sweden) and the XPRES project funded by Vinnova (Stockholm, Sweden). The authors express gratitude for the reviewers’ constructive feedback.

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Correspondence to Nicolas Leberruyer .

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Leberruyer, N., Bruch, J., Ahlskog, M., Afshar, S. (2023). Enabling an AI-Based Defect Detection Approach to Facilitate Zero Defect Manufacturing. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-031-43666-6_43

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  • DOI: https://doi.org/10.1007/978-3-031-43666-6_43

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