Work

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

Year: 2021

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2112.06769

Authors: Morales-Hernández, Alejandro, Van Nieuwenhuyse, Inneke, Gonzalez, Sebastian Rojas, Jordens, Jeroen, Witters, Maarten, Van Doninck, Bart

Institutions: Hasselt University, Flanders Make (Belgium)

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: Flanders Make, Fonds Wetenschappelijk Onderzoek, Vlaamse regering