Abstract
Developments in Machine Learning (ML) in the last years resulted in taking as granted their usage and their necessity clear in areas such as manufacturing and quality control. Such areas include case specific requirements and restrictions that require the human expert’s knowledge and effort to apply the ML algorithms efficiently. This paper proposes a framework architecture that utilizes Automated Machine Learning (AutoML) to minimize human intervention while constructing and maintaining ML models for quality control. The data analyst gives the setting for multiple configurations while designing predictive quality models which are automatically optimized and maintained. Moreover, experiments are conducted to test the framework in both the performance of the prediction models and the time needed to construct the models.
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Acknowledgements
This work is partly funded by the European Union's Horizon 2020 project COALA “COgnitive Assisted agile manufacturing for a LAbor force supported by trustworthy Artificial Intelligence” (Grant agreement No 957296). The work presented here reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains.
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Fikardos, M., Lepenioti, K., Bousdekis, A., Bosani, E., Apostolou, D., Mentzas, G. (2022). An Automated Machine Learning Framework for Predictive Analytics in Quality Control. In: Kim, D.Y., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action. APMS 2022. IFIP Advances in Information and Communication Technology, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-031-16407-1_3
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