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

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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-04
Main Authors: Morales-Hernández, Alejandro, Sebastian Rojas Gonzalez, Inneke Van Nieuwenhuyse, Couckuyt, Ivo, Jordens, Jeroen, Witters, Maarten, Bart Van Doninck
Format: Article
Language:English
Subjects:
Adhesive bonding
Adhesive joints
Bonded joints
Constraints
Damage tolerance
Design optimization
Design parameters
Evolutionary algorithms
Experiments
Fatigue failure
Fatigue strength
Gaussian process
Genetic algorithms
Machine learning
Multiple objective analysis
Optimization
Pareto optimization
Process parameters
Strength to weight ratio
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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).
ISSN:2331-8422