Machine learning based optimization of a ceramic bushing manufacturing process

  • Machine learning (ML) has shown great promise in a variety of domains in recent years. ML models are known to require large amounts of labeled training data, keeping small to medium-sized business from utilizing them. This paper presents ML based approach to optimize a ceramic bushing manufac-turing process, by predicting the employed press-fit process as a function of press punch position. Accurate predictions would ensure optimal process configuration, guaranteeing quality and reducing waste. Models are trained in a supervised manner to predict the press-fit process and the ceramic defect probabilities as functions of press punch position. We were able to predict the press-fit process with a mean correlation of 0.996 and assess whether the process would damage the ceramic with a mean precision of 96.7%. Our results exemplify how ML can be used to predict and optimize highly specialised processes even with small datasets.

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Metadaten
Author:Thomas H. Schmitt, Maximilian Bundscherer, Ralf Drechsel, Tobias BockletORCiD
DOI:https://doi.org/10.1109/sensors52175.2022.9967124
ISBN:978-1-6654-8464-0
Parent Title (English):2022 IEEE Sensors
Publisher:IEEE
Document Type:conference proceeding (article)
Language:English
Reviewed:Begutachtet/Reviewed
Release Date:2024/07/02
Tag:Manufacturing; ceramic bushing; machine learning; optimization
Pagenumber:4
Konferenzangabe:2022 IEEE Sensors, 30 October 2022 - 02 November 2022, Dallas, TX, USA
institutes:Fakultät Informatik
Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Digitalisierung & Künstliche Intelligenz
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