Critical analysis of the suitability of surrogate models for finite element method application in catalog-based suspension bushing design.

Cernuda, Carlos; Llavori, I�igo; Zavoianu, Alexandru-Ciprian; Aguirre, Aitor; Zabala, Alaitz; Plaza, Jon

Abstract

This work presents a critical analysis of the suitability of surrogate models for finite element method application. A case study of a finite element method (FEM) structural problem was selected in order to test the performance of surrogate algorithms. A simple design of experiments (DoE) approach, based on 1D kernel density estimations, is employed to construct a representative pool of real FEM simulations, which becomes the dataset for five different surrogate models, two linear and three non-linear, whose most relevant hyperparameters were tuned (model selection). Results in a real bushing case study show that surrogate models can accurately mimic FEM simulations outcomes, in this case four types of stiffnesses (axial, radial, torsion, and cardanic).

Citation

CERNUDA, C., LLAVORI, I., ZAVOIANU, A.-C., AGUIRRE, A., ZABALA, A. and PLAZA, J. 2020. Critical analysis of the suitability of surrogate models for finite element method application in catalog-based suspension bushing design. In Proceedings of 25th Institute of Electrical and Electronics Engineers (IEEE) Emerging technologies and factory automation international conference 2020 (ETFA 2020), 8-11 September 2020, Vienna, Austria. Piscataway: IEEE [online], article ID 9212166, pages 829-836. Available from: https://doi.org/10.1109/ETFA46521.2020.9212166

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 25th Institute of Electrical and Electronics Engineers (IEEE) Emerging technologies and factory automation international conference 2020 (ETFA 2020)
• Start Date: Sep 8, 2020
• End Date: Sep 11, 2020
• Acceptance Date: Jun 4, 2020
• Online Publication Date: Sep 8, 2020
• Publication Date: Oct 5, 2020
• Deposit Date: Oct 13, 2020
• Publicly Available Date: Oct 13, 2020
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 829-836
• Series ISSN: 1946-0759
• DOI: https://doi.org/10.1109/ETFA46521.2020.9212166
• Keywords: Design of experiments; Surrogate model; Finite element method; Bushing; Support vector regression; Random forest
• Public URL: https://rgu-repository.worktribe.com/output/976017