Model-Guided Local Bayesian Optimization for Tuning of Interpretable Controllers in Injection Molding

IEEE Conference on Control Technology and Applications (CCTA), pp. 372–378

Abstract

Advanced control methods have proven effective for controlling cavity pressure, a key determinant of part-quality attributes, in the plastics injection molding process. However, the abstract nature of the resulting control laws makes them difficult to interpret in a production environment, thereby limiting adoption in industrial applications. Additionally, controller optimization poses a severe challenge due to the diversity of mold geometries and materials. We propose a method to automatically optimize interpretable controllers during manufacturing while being cycle-efficient and risk-aware. The approach uses a Physics-Inspired Neural Mixture-of-Local-Experts model of the injection molding dynamics and augments its simulated closed-loop costs with a residual Gaussian Process, enabling Local Bayesian Optimization of controller parameters. We benchmark the algorithm against Vanilla Bayesian Optimization (BO) in simulation, using three controllers with parameter counts ranging from 1 to 30. Using the local method, we identify controller parameters that yield costs comparable to or lower than those of global BO over 20 optimization iterations, while mitigating high-cost excursions during tuning.

Authors 5

1. Jens Ahlers Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Robert Göllinger Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Xu Chen Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Heike Vallery Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. Sebastian Stemmler Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University