Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding

TL;DR

This paper presents a Bayesian optimization approach using Gaussian Process Regression to efficiently identify Pareto-optimal process parameters for adhesive bonding, handling multiple objectives, constraints, and data scarcity.

Contribution

It introduces a novel multi-objective Bayesian optimization method tailored for scarce data scenarios in process design, specifically applied to adhesive bonding.

Findings

Efficiently finds Pareto-optimal solutions with limited experiments.

Successfully models objectives and constraints with Gaussian Processes.

Reduces experimental costs in process optimization.

Abstract

Adhesive joints are increasingly used in industry for a wide variety of applications because of their favorable characteristics such as high strength-to-weight ratio, design flexibility, limited stress concentrations, planar force transfer, good damage tolerance, and fatigue resistance. Finding the optimal process parameters for an adhesive bonding process is challenging: the optimization is inherently multi-objective (aiming to maximize break strength while minimizing cost), constrained (the process should not result in any visual damage to the materials, and stress tests should not result in failures that are adhesion-related), and uncertain (testing the same process parameters several times may lead to different break strengths). Real-life physical experiments in the lab are expensive to perform. Traditional evolutionary approaches (such as genetic algorithms) are then ill-suited to solve the problem, due to the prohibitive amount of experiments required for evaluation. Although Bayesian optimization-based algorithms are preferred to solve such expensive problems, few methods consider the optimization of more than one (noisy) objective and several constraints at the same time. In this research, we successfully applied specific machine learning techniques (Gaussian Process Regression) to emulate the objective and constraint functions based on a limited amount of experimental data. The techniques are embedded in a Bayesian optimization algorithm, which succeeds in detecting Pareto-optimal process settings in a highly efficient way (i.e., requiring a limited number of physical experiments).