Self-Optimizing Grinding Machines using Gaussian Process Models and Constrained Bayesian Optimization

TL;DR

This paper presents a method for self-optimizing grinding machines by applying constrained Bayesian optimization with Gaussian process models to minimize costs while satisfying quality and safety constraints, using real-time temperature measurements.

Contribution

It introduces a novel approach combining Gaussian process models and constrained Bayesian optimization for real-time grinding machine parameter tuning.

Findings

Optimized grinding parameters after few trials

Effectively incorporated constraint uncertainty

Reduced production costs while maintaining quality

Abstract

In this study, self-optimization of a grinding machine is demonstrated with respect to production costs, while fulfilling quality and safety constraints. The quality requirements of the final workpiece are defined with respect to grinding burn and surface roughness, and the safety constraints are defined with respect to the temperature at the grinding surface. Grinding temperature is measured at the contact zone between the grinding wheel and workpiece using a pyrometer and an optical fiber, which is embedded inside the rotating grinding wheel. Constrained Bayesian optimization combined with Gaussian process models is applied to determine the optimal feed rate and cutting speed of a cup wheel grinding machine manufacturing tungsten carbide cutting inserts. The approach results in the determination of optimal parameters for unknown workpiece and tool combinations after only a few grinding trials. It also incorporates the uncertainty of the constraints in the prediction of optimal parameters by using stochastic process models.