2026-04-27T05:30:37Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/60922024-02-29T15:02:30Zcom_20.500.11984_1143col_20.500.11984_1148

ARRAZOLA, PEDRO JOSE 2023-06-07T07:22:48Z 2023-06-07T07:22:48Z 2023 2212-8271 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=172750 https://hdl.handle.net/20.500.11984/6092 The identification of input parameters for funite element modelling of the cutting process is still a complex task as the experimental testing equipment cannot reach its combined levels of strains, strain rates and temperatures. Inverse identification using Artificial Intelligence method provides a relevant alternative. In this paper, material constitutive and friction models parameters identified with an Efficient Global Optimization algorithm and an ALE orthogonal cutting model are introduced in a CEL model. Assessment of the differences in the results due to the formulation and dependence of parameters identification to the finite element model are then performed. eng Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2023 The Authors. Published by Elsevier B.V. Cutting Finite element method (FEM) Predictive model Application of material constitutive and friction models parameters identified with AI and ALE to a CEL orthogonal cutting model http://purl.org/coar/resource_type/c_c94f