Multi-objective simulation optimization of the adhesive bonding process of materials

TL;DR

This paper presents a Bayesian optimization approach using Gaussian and Logistic Regression to efficiently identify Pareto-optimal parameters in adhesive bonding processes, aiding automotive lightweighting efforts.

Contribution

It introduces a novel application of Bayesian optimization with specific regression models for multi-objective process parameter tuning in adhesive bonding.

Findings

Efficient identification of Pareto-optimal process parameters

Reduced number of experiments needed for optimization

Effective guidance for adhesive bonding process design

Abstract

Automotive companies are increasingly looking for ways to make their products lighter, using novel materials and novel bonding processes to join these materials together. Finding the optimal process parameters for such adhesive bonding process is challenging. In this research, we successfully applied Bayesian optimization using Gaussian Process Regression and Logistic Regression, to efficiently (i.e., requiring few experiments) guide the design of experiments to the Pareto-optimal process parameter settings.