Article (Scientific journals)
Identification of the Constitutive and Friction Models Parameters via a Multi-Objective Surrogate-Assisted Algorithm for the Modeling of Machining—Application to Arbitrary Lagrangian Eulerian Orthogonal Cutting of Ti6Al4V
Ducobu, François; Kugalur-Palanisamy, N.; Briffoteaux, Guillaume et al.
2024In Journal of Manufacturing Science and Engineering, 146 (6)
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Keywords :
artificial intelligence; finite element modeling; machining processes; modeling; multi-objective identification; orthogonal cutting; simulation; surrogate evolutionary algorithm; Element models; Finite element modeling; Machining Process; Modeling; Multi objective; Multi-objective identification; Objective identification; Orthogonal cutting; Simulation; Surrogate evolutionary algorithm; Control and Systems Engineering; Mechanical Engineering; Computer Science Applications; Industrial and Manufacturing Engineering
Abstract :
[en] The evolution of high-performance computing facilitates the simulation of manufacturing processes. The prediction accuracy of a numerical model of the cutting process is closely associated with the selection of constitutive and friction models. The reliability and the accuracy of these models highly depend on the value of the parameters involved in the definition of the cutting process. Direct of inverse methods are used to determine these model parameters. However, these identification procedures often neglect the link between the parameters of the material and the friction models. This article introduces a novel approach to inversely identify the best parameters value for both models at the same time and by taking into account multiple cutting conditions in the optimization routine. An artificial intelligence (AI) framework that combines the finite element modeling with an adaptive Bayesian multi-objective evolutionary algorithm (AB-MOEA) is developed, where the objective is to minimize the deviation between the experimental and the numerical results. The arbitrary Lagrangian–Eulerian (ALE) formulation and the Ti6Al4V alloy are selected to demonstrate its applicability. The investigation shows that the developed AI platform can identify the best parameters values with low computational time and resources. The identified parameters values predicted the cutting and feed forces within a deviation of less than 4% from the experiments for all the cutting conditions considered in this work.
Disciplines :
Mechanical engineering
Author, co-author :
Ducobu, François  ;  Université de Mons - UMONS > Faculté Polytechnique > Service de Génie Mécanique
Kugalur-Palanisamy, N.;  Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, University of Mons, Mons, Belgium
Briffoteaux, Guillaume  ;  Université de Mons - UMONS > Faculté Polytechnique > Service de Mathématique et Recherche opérationnelle
Gobert, Maxime  ;  Université de Mons - UMONS > Faculté Polytechnique > Service de Mathématique et Recherche opérationnelle
Tuyttens, Daniel ;  Université de Mons - UMONS > Faculté Polytechnique > Service de Mathématique et Recherche opérationnelle
Arrazola, Pedro-José
Rivière, Edouard  ;  Université de Mons - UMONS > Faculté Polytechnique > Service de Génie Mécanique
Language :
English
Title :
Identification of the Constitutive and Friction Models Parameters via a Multi-Objective Surrogate-Assisted Algorithm for the Modeling of Machining—Application to Arbitrary Lagrangian Eulerian Orthogonal Cutting of Ti6Al4V
Publication date :
16 April 2024
Journal title :
Journal of Manufacturing Science and Engineering
ISSN :
1087-1357
Publisher :
American Society of Mechanical Engineers (ASME)
Volume :
146
Issue :
6
Peer reviewed :
Peer Reviewed verified by ORBi
Research unit :
F707 - Génie Mécanique
F151 - Mathématique et Recherche opérationnelle
Research institute :
R400 - Institut de Recherche en Science et Ingénierie des Matériaux
Infortech
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique
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