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.
| 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 |
