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
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
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
Altintas, Y., and Ber, A., 2001, “Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and Cnc Design,” ASME Appl. Mech. Rev., 54(5), pp. B84–B84.
Merchant, M. E., 1945, “Mechanics of the Metal Cutting Process. I. Orthogonal Cutting and a Type 2 Chip,” J. Appl. Phys., 16, pp. 267–275.
Wang, S., Tao, Z., Wenping, D., Zhanwen, S., and To, S., 2022, “Analytical Modeling and Prediction of Cutting Forces in Orthogonal Turning: A Review,” Int. J. Adv. Manuf. Technol., 119, pp. 1407–1434.
Tsekhanov, J., and Storchak, M., 2015, “Development of Analytical Model for Orthogonal Cutting,” Prod. Eng., 9(2), pp. 247–255.
Markopoulos, A., 2012, Finite Element Method in Machining Processes, ASM, Materials Park, OH.
Arrazola, P., Özel, T., Umbrello, D., Davies, M., and Jawahir, I., 2013, “Recent Advances in Modelling of Metal Machining Processes,” CIRP Ann. Manuf. Technol., 62(2), pp. 695–718.
Arrazola, P., Ugarte, D., Montoya, J., Villar, A., and Marya, S., 2005, “Finite Element Modeling of Chip Formation Process with Abaqus/Explicit 6.3,” Proceedings of VII International Conference on Computational Plasticity, Barcelona, Spain.
Furrer, D., and Semiatin, S., 2010, Metals Process Simulation, ASM International, Materials Park, OH.
Fang, N., and Jawahir, I., 2002, “Analytical Predictions and Experimental Validation of Cutting Force Ratio, Chip Thickness, and Chip Back-Flow Angle in Restricted Contact Machining Using the Universal Slip-Line Model,” Int. J. Mach. Tools. Manuf., 42(6), pp. 681–694.
Komvopoulos, K., and Erpenbeck, S., 1991, “Finite Element Modeling of Orthogonal Metal Cutting,” J. Eng. Ind., 113(3), pp. 253–267.
Melkote, S., Grzesik, W., Outeiro, J., Rech, J., Schulze, V., Attia, H., Arrazola, P., M’Saoubi, R., and Saldana, C., 2017, “Advances in Material and Friction Data for Modelling of Metal Machining,” CIRP Ann. - Manuf. Technol., 66(2), pp. 731–754.
Ducobu, F., Rivière-Lorphèvre, E., and Filippi, E., 2016, “Application of the Coupled Eulerian-Lagrangian (CEL) Method to the Modeling of Orthogonal Cutting,” Eur. J. Mech. - A/Solids, 59, pp. 58–66.
Ducobu, F., Arrazola, P.-J., Rivière-Lorphèvre, E., de Zarate, G. O., Madariaga, A., and Filippi, E., 2017, “The CEL Method as an Alternative to the Current Modelling Approaches for Ti6al4v Orthogonal Cutting Simulation,” Procedia CIRP, 58, pp. 245–250.
Childs, T. H. C., 1998, “Material Property Needs in Modeling Metal Machining,” Mach. Sci. Technol., 2(2), pp. 303–316.
Kugalur-Palanisamy, N., Rivière-Lorphèvre, E., Arrazola, P. J., and Ducobu, F., 2019, “Comparison of Johnson-Cook and Modified Johnson-Cook Material Constitutive Models and Their Influence on Finite Element Modelling of Ti6Al4V Orthogonal Cutting Process,” 22nd International ESAFORM Conference on Material Forming, ESAFORM 2019, Vitoria-Gasteiz, Spain.
Kugalur-Palanisamy, N., Rivière-Lorphèvre, E., Arrazola, P. J., and Ducobu, F., 2022, “Influence of Coulomb’s Friction Coefficient in Finite Element Modeling of Orthogonal Cutting of Ti6Al4V,” Key. Eng. Mater., 926(2), p. 1619.
Childs, T., 2006, “Friction Modelling in Metal Cutting,” Wear, 260(3), pp. 310–318.
Arrazola, P. J., and Özel, T., 2010, “Investigations on the Effects of Friction Modeling in Finite Element Simulation of Machining,” Int. J. Mech. Sci., 52(1), pp. 31–42.
Ducobu, F., Rivière-Lorphèvre, E., and Filippi, E., 2017, “On the Importance of the Choice of the Parameters of the Johnson-Cook Constitutive Model and Their Influence on the Results of a Ti6Al4v Orthogonal Cutting Model,” Int. J. Mech. Sci., 122, pp. 143–155.
Kugalur Palanisamy, N., Rivière Lorphèvre, E., Arrazola, P.-J., and Ducobu, F., 2021, “Influence of Constitutive Models and the Choice of the Parameters on Fe Simulation of Ti6al4v Orthogonal Cutting Process for Different Uncut Chip Thicknesses,” J. Manuf. Mater. Process., 5(2), p. 56.
Chandrasekaran, H., M’Saoubi, R., and Chazal, H., 2005, “Modelling of Material Flow Stress in Chip Formation Process From Orthogonal Milling and Split Hopkinson Bar Tests,” Mach. Sci. Technol., 9(1), pp. 131–145.
Özel, T., and Altan, T., 2000, “Determination of Workpiece Flow Stress and Friction at the Chip-Tool Contact for High-Speed Cutting,” Int. J. Mach. Tools. Manuf., 40(1), pp. 133–152.
Sterle, L., Pušavec, F., and Kalin, M., 2019, “Determination of Friction Coefficient in Cutting Processes: Comparison Between Open and Closed Tribometers,” Procedia CIRP, 82, pp. 101–106.
Malakizadi, A., Hosseinkhani, K., Mariano, E., Ng, E., Prete, A. D., and Nyborg, L., 2017, “Influence of Friction Models on Fe Simulation Results of Orthogonal Cutting Process,” Int. J. Adv. Manuf. Technol., 88, pp. 3217–3232.
Globocki Lakic, G., Kramar, D., and Kopac, J., 2014, Metal Cutting—Theory and Application, University of Banjaluka, Faculty of Mechanical Engineering; University of Ljubljana, Faculty of Mechanical Engineering, p. 12.
de Zarate, G. O., Madariaga, A., Arrazola, P. J., and Childs, T. H., 2021, “A Novel Methodology to Characterize Tool-Chip Contact in Metal Cutting Using Partially Restricted Contact Length Tools,” CIRP. Ann., 70(1), pp. 61–64.
Özel, T., and Karpat, Y., 2007, “Identification of Constitutive Material Model Parameters for High-Strain Rate Metal Cutting Conditions Using Evolutionary Computational Algorithms,” Mater. Manuf. Processes., 22(5), pp. 659–667.
Chaparro, B., Thuillier, S., Menezes, L., Manach, P., and Fernandes, J., 2008, “Material Parameters Identification: Gradient-Based, Genetic and Hybrid Optimization Algorithms,” Comput. Mater. Sci., 44(2), pp. 339–346.
Milani, A., Dabboussi, W., Nemes, J., and Abeyaratne, R., 2009, “An Improved Multi-objective Identification of Johnson-Cook Material Parameters,” Int. J. Impact Eng., 36(2), pp. 294–302.
Klocke, F., Lung, D., Buchkremer, S., and Jawahir, I. S., 2013, “From Orthogonal Cutting Experiments Towards Easy-to-Implement and Accurate Flow Stress Data,” Mater. Manuf. Processes., 28(11), pp. 1222–1227.
Bäker, M., 2015, “A New Method to Determine Material Parameters From Machining Simulations Using Inverse Identification,” Procedia CIRP, 31, pp. 399–404.
Denkena, B., Grove, T., Dittrich, M., Niederwestberg, D., and Lahres, M., 2015, “Inverse Determination of Constitutive Equations and Cutting Force Modelling for Complex Tools Using Oxley’s Predictive Machining Theory,” Procedia CIRP, 31, pp. 405–410.
Shatla, M., Kerk, C., and Altan, T., 2001, “Process Modeling in Machining. Part I: Determination of Flow Stress Data,” Int. J. Mach. Tools. Manuf., 41(10), pp. 1511–1534.
Nguyen, N., and Hosseini, A., 2023, “Direct Calculation of Johnson-Cook Constitutive Material Parameters for Oblique Cutting Operations,” J. Manuf. Process., 92, pp. 226–237.
Shrot, A., and Baeker, M., 2011, “Inverse Identification of Johnson-Cook Material Parameters From Machining Simulations,” Adv. Mater. Res., 223, pp. 277–285.
Shrot, A., and Bäker, M., 2012, “Determination of Johnson-Cook Parameters From Machining Simulations,” Comput. Mater. Sci., 52(1), pp. 298–304.
Bosetti, P., Maximiliano Giorgio Bort, C., and Bruschi, S., 2013, “Identification of Johnson–Cook and Tresca’s Parameters for Numerical Modeling of AISI-304 Machining Processes,” ASME J. Manuf. Sci. Eng., 135(5), p. 051021.
Franchi, R., and Mariano, E., 2016, “Inverse Analysis Procedure to Determine Flow Stress and Friction Data for Metal Cutting Finite Element Modeling,” Key. Eng. Mater., 651–653, pp. 1345–1350.
Bergs, T., Hardt, M., and Schraknepper, D., 2019, “Inverse Material Model Parameter Identification for Metal Cutting Simulations by Optimization Strategies,” MM Sci. J., 2019(80), pp. 3172–3178.
Bergs, T., Hardt, M., and Schraknepper, D., 2020, “Determination of Johnson-Cook Material Model Parameters for AISI 1045 From Orthogonal Cutting Tests Using the Downhill-Simplex Algorithm,” Procedia Manuf., 48, pp. 541–552.
Hardt, M., Schraknepper, D., and Bergs, T., 2021, “Investigations on the Application of the Downhill-Simplex-Algorithm to the Inverse Determination of Material Model Parameters for Fe-Machining Simulations,” Simul. Modell. Practice Theory, 107, p. 102214.
Hardt, M., Jayaramaiah, D., and Bergs, T., 2021, “On the Application of the Particle Swarm Optimization to the Inverse Determination of Material Model Parameters for Cutting Simulations,” Modelling, 2(1), pp. 129–148.
Hardt, M., and Bergs, T., 2021, “Considering Multiple Process Observables to Determine Materialmodel Parameters for Fe-Cutting Simulations,” Int. J. Adv. Manuf. Technol., 113, pp. 3419–3431.
Arrazola, P., Özel, T., Umbrello, D., Davies, M., and Jawahir, I., 2013, “Recent Advances in Modelling of Metal Machining Processes,” CIRP. Ann., 62(2), pp. 695–718.
Kugalur Palanisamy, N., Rivière Lorphèvre, E., Gobert, M., Briffoteaux, G., Tuyttens, D., Arrazola, P.-J., and Ducobu, F., 2022, “Identification of the Parameter Values of the Constitutive and Friction Models in Machining Using Ego Algorithm: Application to Ti6Al4V,” Metals, 12(6), p. 976.
Ducobu, F., Palanisamy, N. K., Arrazola, P.-J., and Rivière-Lorphèvre, E., 2023, “Application of Material Constitutive and Friction Models Parameters Identified With AI and ALE to a CEL Orthogonal Cutting Model,” Procedia CIRP, 117, pp. 311–316.
Johnson, G. R., and Cook, W. H., 1983, “A Constitutive Model and Data for Metals Subjected to Large Strains, High Strain Rates and High Temperatures,” Eng. Fract. Mech., 21(1), pp. 31–48.
Calamaz, M., Coupard, D., and Girot, F., 2008, “A New Material Model for 2d Numerical Simulation of Serrated Chip Formation When Machining Titanium Alloy Ti6Al4V,” Int. J. Mach. Tools. Manuf., 48(3), pp. 275–288.
Markopoulos, A., Vaxevanidis, N., and Manolakos, D., 2015, “Friction and Material Modelling in Finite Element Simulation of Orthogonal Cutting,” Tribol. Ind., 37(4), pp. 440–448.
Ducobu, F., Rivière-Lorphèvre, E., and Filippi, E., 2014, “Numerical Contribution to the Comprehension of Saw-Toothed Ti6Al4V Chip Formation in Orthogonal Cutting,” Int. J. Mech. Sci., 81, pp. 77–87.
Movahhedy, M. R., Gadala, M. S., and Altintas, Y., 2000, “Simulation of Chip Formation in Orthogonal Metal Cutting Process: An Ale Finite Element Approach,” Mach. Sci. Technol., 4(1), pp. 15–42.
Ducobu, F., Rivière-Lorphèvre, E., and Filippi, E., 2017, “Mesh Influence in Orthogonal Cutting Modelling With the Coupled Eulerian-Lagrangian (CEL) Method,” Eur. J. Mech., A/Solids, 65, pp. 324–335.
Ducobu, F., Rivière-Lorphèvre, E., and Filippi, E., 2015, “On the Introduction of Adaptive Mass Scaling in a Finite Element Model of Ti6al4v Orthogonal Cutting,” Simul. Modell. Practice Theory, 53, pp. 1–14.
Leseur, D., 1999, Experimental Investigations of Material Models for Ti-6A1-4V and 2024-T3.
Boivineau, M., Cagran, C., Doytier, D., Eyraud, V., Nadal, M. H., Wilthan, B., and Pottlacher, G., 2006, “Thermophysical Properties of Solid and Liquid Ti6Al4V (TA6v) Alloy,” Int. J. Thermophys., 27(2), pp. 507–529.
Seo, S., Min, O., and Yang, H., 2005, “Constitutive Equation for Ti–6al–4v at High Temperatures Measured Using the Shpb Technique,” Int. J. Impact Eng. - INT J IMPACT ENG, 31(6), pp. 735–754.
Emmerich, M., and Deutz, A., 2018, “A Tutorial on Multiobjective Optimization: Fundamentals and Evolutionary Methods,” Natural Comput., 17, pp. 585–609.
Wang, X., Jin, Y., Schmitt, S., and Olhofer, M., 2020, “An Adaptive Bayesian Approach to Surrogate-Assisted Evolutionary Multi-objective Optimization,” Inf. Sci., 519, pp. 317–331.
Cheng, R., Jin, Y., Olhofer, M., and Sendhoff, B., 2016, “A Reference Vector Guided Evolutionary Algorithm for Many-objective Optimization,” IEEE Trans. Evol. Comput., 20(5), pp. 773–791.
Talbi, E. G., 2009, Metaheuristics: From Design to Implementation, Wiley, Hoboken, NJ.
Cornell, J. A., 2002, Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data, John Wiley & Sons, Hoboken, NJ.
Bonilla, E. V., Chai, K., and Williams, C., 2008, “Multi-Task Gaussian Process Prediction,” Advances in Neural Information Processing Systems 20, Vancouver, British Columbia, Canada.
Xia, W., Yang, H., Liao, X., and Zeng, J., 2014, “A Multi-objective Optimization Method Based on Gaussian Process Simultaneous Modeling for Quality Control in Sheet Metal Forming,” Int. J. Adv. Manuf. Technol., 72, pp. 1333–1346.
Rasmussen, C. E., 2006, Gaussian Processes for Machine Learning, MIT Press, Cambridge, MA.
Briffoteaux, G., Tomenko, P., and Geremie, F., 2021, “pysbo, a Python Platform for Surrogate-Based Optimization,” CeCILL Licence.
Ducobu, F., Rivière-Lorphèvre, E., and Filippi, E., 2015, “Experimental Contribution to the Study of the Ti6Al4V Chip Formation in Orthogonal Cutting on a Milling Machine,” Int. J. Mater. Forming, 8(3), pp. 455–468.
Kang, G., Kim, J., Choi, Y., and Lee, D., 2022, “In-Process Identification of the Cutting Force Coefficients in Milling Based on a Virtual Machining Model,” Int. J. Precis. Eng. Manuf., 23, pp. 839–851.
Zitzler, E., and Thiele, L., 1999, “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach,” IEEE Trans. Evol. Comput., 3(4), pp. 257–271.
Rech, J., Arrazola, P. J., Claudin, C., Courbon, C., Pusavec, F., and Kopac, J., 2013, “Characterisation of Friction and Heat Partition Coefficients at the Tool-Work Material Interface in Cutting,” CIRP. Ann., 62(1), pp. 79–82.
Marler, R., and Arora, J., 2010, “The Weighted Sum Method for Multi-objective Optimization: New Insights,” Struct. Multidiscipl. Optim., 41, pp. 853–862.