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

Autor:	Morales-Hern��ndez, Alejandro, Van Nieuwenhuyse, Inneke, Gonzalez, Sebastian Rojas, Jordens, Jeroen, Witters, Maarten, Van Doninck, Bart
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	FOS: Computer and information sciences multi-objective Computer Science - Machine Learning machine learning process optimization bayesian optimization Machine Learning (cs.LG)
Popis:	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. Accepted on Winter Simulation Conference (WSC21)
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c84312116bff2430c7a4f2b56458d4e http://arxiv.org/abs/2112.06769 Zobrazit plný text záznamu