@inproceedings{SchmittBundschererDrechseletal.2022, author = {Schmitt, Thomas H. and Bundscherer, Maximilian and Drechsel, Ralf and Bocklet, Tobias}, title = {Machine learning based optimization of a ceramic bushing manufacturing process}, series = {2022 IEEE Sensors}, booktitle = {2022 IEEE Sensors}, publisher = {IEEE}, isbn = {978-1-6654-8464-0}, doi = {10.1109/sensors52175.2022.9967124}, pages = {4}, year = {2022}, abstract = {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.}, language = {en} }