Enhancing Product Quality in High-Variant Manufacturing: Combining Physics-Based Simulations and Data Science for Target Variable Estimation in an IoT- and Machine Learning-Driven Context

Due to growing demands for quality, sustainability, and digitalization, data science and artificial intelligence are gaining importance across industries. The extensive product range in many sectors often poses considerable challenges. For example, ma-chine learning (ML) models may struggle with limited data per product type. This paper presents a method that combines data science and physics-based simula-tions for target variable estimation, with the aim of creating a comparable database for effective statistical analysis and ML models — based on a use case from the aluminum production industry. A key advantage of this approach is that it can effectively model even production variants with very low quantities. The following discussion will pre-sent how this method can be used to enhance production processes, specifically to identify parameters that directly influence product quality. Furthermore, the work ex-plores the potential for precisely controlling these parameters using ML models and discusses some major challenges. The article demonstrates that integrating data science, technological knowledge and physics-based simulations is an effective methodology for estimating target variables, facilitating precise modelling even for production variants with minimal quantities and improving production processes.