Material Quality Filter Model: Machine Learning Integrated with Expert Experience for Process Optimization

Abstract

In the process of material production, the mismatch between raw material parameters and manufacturing processing parameters may lead to fluctuations in product properties and ultimately to unstable or unqualified product quality. In this paper, we propose the concept of the Quality Filter model for process optimization. The Quality Filter model uses the property prediction model as a surrogate model and integrates expert experience and process window constraints to construct a loss function. When raw material parameters are supplied, the suitable processing parameters can be automatically matched, and the processing fluctuation can be used to hedge the fluctuations in raw material, thus stabilizing the product quality and improving overall product properties. A trial production data set of 128 samples of wind power steel from a steel plant was used to test the model. We selected the ellipsoid discriminant analysis model with a classification accuracy rate of 82.81% as the surrogate model, which gives a highly interpretable visualization result. Finally, the results show that the properties of the samples that underwent the optimized process are improved.

Keywords

• quality control
• big data
• discriminant analysis
• dynamic control
• steel production