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Title: | Bayesian multi-objective optimization of process design parameters in constrained settings with noise: an engineering design application | Authors: | MORALES HERNANDEZ, Alejandro ROJAS GONZALEZ, Sebastian VAN NIEUWENHUYSE, Inneke Couckuyt, Ivo Jordens, Jeroen Witters, Maarten Van Doninck, Bart |
Issue Date: | 2024 | Publisher: | SPRINGER | Source: | ENGINEERING WITH COMPUTERS, | Status: | Early view | Abstract: | The use of adhesive joints in various industrial applications has become increasingly popular due to their beneficial characteristics , including their high strength-to-weight ratio, design flexibility, limited stress concentrations, planar force transfer, good damage tolerance, and fatigue resistance. However, finding the best process parameters for adhesive bonding can be challenging. This optimization problem is inherently multi-objective, aiming to maximize break strength while minimizing cost and constrained to avoid any visual damage to the materials and ensure that stress tests do not result in adhesion-related failures. Additionally, testing the same process parameters several times may yield different break strengths, making the optimization process uncertain. Conducting physical experiments in a laboratory setting is costly, and traditional evolutionary approaches like genetic algorithms are not suitable due to the large number of experiments required for evaluation. Bayesian optimization is suitable in this context, but few methods simultaneously consider the optimization of multiple noisy objectives and constraints. This study successfully applies advanced learning techniques to emulate the objective and constraint functions based on limited experimental data. These are incorporated into a Bayesian optimization framework, which efficiently detects Pareto-optimal process configurations under strict budget constraints. | Notes: | Morales-Hernández, A (corresponding author), Hasselt Univ, Flanders Make UHasselt & Data Sci Inst, B-3590 Diepenbeek, Belgium.; Morales-Hernández, A (corresponding author), Hasselt Univ, Fac Sci, Computat Math, B-3590 Diepenbeek, Belgium. alejandro.moraleshernandez@uhasselt.be; sebastian.rojasgonzalez@ugent.be; inneke.vannieuwenhuyse@uhasselt.be; ivo.couckuyt@ugent.be; jeroen.jordens@flandersmake.be; maarten.witters@flandersmake.be; bart.vandoninck@flandersmake.be |
Keywords: | Bayesian optimization;Multi-objective optimization;Constrained optimization;Machine learning;Adhesive bonding | Document URI: | http://hdl.handle.net/1942/42094 | ISSN: | 0177-0667 | e-ISSN: | 1435-5663 | DOI: | 10.1007/s00366-023-01922-8 | ISI #: | 001139313500002 | Rights: | The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024 | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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s00366-023-01922-8.pdf Restricted Access | Published version | 2.12 MB | Adobe PDF | View/Open Request a copy |
Constrained_MOO_adhesives_EwC_author_version.pdf | Peer-reviewed author version | 13.1 MB | Adobe PDF | View/Open |
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