# 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

## TL;DR

This paper introduces a method that combines physics-based simulations and data science to improve product quality in manufacturing with many variants.

## Contribution

The novelty lies in integrating FEM simulations with ML to enable cross-variant analysis and better quality control in low-volume production.

## Key findings

- FEM simulations help transform process data for cross-variant comparison and ML use.
- The method effectively models low-volume production variants.
- ML models can precisely control parameters influencing product quality.

## Abstract

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, machine learning (ML) models may struggle with limited data per production variant. The present paper proposes a methodology that integrates the fields of data science and physical simulations. The results from finite element method (FEM) simulations are utilized to transform the process data in such a manner that it can be compared across processes for different production variants and employed for machine learning (ML) methods and statistical analyses. The method is illustrated using an example of aluminum production. A key advantage of this approach is that it can effectively model even production variants with very low quantities. The following discussion will present how this method can be used to enhance production processes, specifically to identify parameters that directly influence product quality, which would not be evident using alternative approaches. Furthermore, the work explores the potential for precisely controlling these parameters using ML models and discusses some major challenges.