AutoML for Industrial Process Control

Due to the growing complexity of modern manufacturing, industrial process control systems generate vast amounts of data with significant potential for machine learning applications. While ML offers immense benefits, the lack of data science expertise poses challenges for adoption. AutoML frameworks tackle these barriers by automating key ML tasks, enhancing accessibility and efficiency. This study investigates their effectiveness in a ceramic industry use case, comparing preprocessing strategies and analyzing explainability with SHAP values validated by domain experts. The findings highlight AutoML's potential to streamline ML model development but also its reliance on domain expertise for effective feature selection and explainability.