Model-Guided Local Bayesian Optimization for Tuning of Interpretable Controllers in Injection Molding
For industrial injection molding, this work addresses the challenge of tuning interpretable controllers efficiently and safely, which is critical for adoption in production environments.
The paper proposes a method for automatically optimizing interpretable controllers in injection molding that is cycle-efficient and risk-aware, using a Physics-Inspired Neural Mixture-of-Local-Experts model and Local Bayesian Optimization. In simulation benchmarks, the method achieves comparable or lower costs than global Bayesian Optimization over 20 iterations while reducing high-cost excursions.
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.