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Tytuł artykułu
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Abstrakty
The prediction of machining processes is a challenging task and usually requires a large experimental basis. These experiments are time-consuming and require manufacturing and testing of different tool geometries at various process conditions to find optimum machining settings. In this paper, a machine learning model of the orthogonal cutting process of Ti6Al4V is proposed to predict the cutting and feed forces for a wide range of process conditions with regards to rake angle, clearance angle, cutting edge radius, feed and cutting speed. The model uses training data generated by virtual experiments, which are conducted using physical based simulations of the orthogonal cutting process with the smoothed particle hydrodynamics (SPH). The ML training set is composed of input parameters, and output process forces from 2500 instances of GPU accelerated SPH simulations. The resulting model provides fast process force predictions and can consider the cutter geometry in comparison to classical analytical approaches.
Czasopismo
Rocznik
Tom
Strony
111--123
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
- IWF, ETH Zürich, Switzerland
autor
- IWF, ETH Zürich, Switzerland
autor
- IWF/inspire, ETH Zürich, Switzerland
autor
- Federal Office of Meteorology & Climatology, MeteoSwiss, Switzerland
autor
- IWF/inspire, ETH Zürich, Switzerland
autor
- IWF, ETH Zürich, Switzerland
autor
- IWF, ETH Zürich, Switzerland
Bibliografia
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- [8] SRIDHAR P., PRIETO J.M.R., DE PAYREBRUNE K.M., 2020, Discretization Approaches to Model Orthogonal Cutting with Lagrangian, Arbitrary Lagrangian Eulerian, Particle Finite Element Method and Smooth Particle Hydrodynamics Formulations, Procedia CIRP, 93, 1496–1501.
- [9] RÖTHLIN M., KLIPPEL H., AFRASIABI M., WEGENER K., 2019, Metal Cutting Simulations Using Smoothed Particle Hydrodynamics on the GPU, The International Journal of Advanced Manufacturing Technology, 102/ 9–12, 3445–3457.
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- [18] AFRASIABI M., RÖTHLIN M., KLIPPEL H., WEGENER K., 2019, Meshfree Simulation of Metal Cutting: an Updated Lagrangian Approach with Dynamic Refinement, International Journal of Mechanical Sciences, 160, 451–466.
- [19] RÖTHLIN M., KLIPPEL H., AFRASIABI M., WEGENER K., 2019, Meshless Single Grain Cutting Simulations on the GPU, International Journal of Mechatronics and Manufacturing Systems, 12/3–4, 272–297.
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- [22] JUMPER J., EVANS R., PRITZEL A., GREEN T., FIGURNOV M., RONNEBERGER O., TUNYASUVUNAKOOL K., BATES R., ŽÍDEK A., POTAPENKO A., BRIDGLAND A., 2021. Highly Accurate Protein Structure Prediction with AlphaFold, Nature, 596, 583–589.
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- [25] JAKOB J., GROSS M., GÜNTHER T., 2020, A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation, IEEE Transactions on Visualization and Computer Graphics, 27/2, 1279–1289.
- [26] LANGER M.F., GOESSMANN A., RUPP M., 2020, Representations of Molecules and Materials for Interpolation of Quantum-Mechanical Simulations via Machine Learning, arXiv preprint arXiv:2003.12081.
- [27] WOODWARD M., TIAN Y., HYETT C., FRYER C., LIVESCU D., STEPANOV M., CHERTKOV M., 2021, Physics Informed Machine Learning of SPH: Machine Learning Lagrangian Turbulence, arXiv Preprint arXiv:2110.13311.
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- [29] WYEN C.F., 2011, Rounded Cutting Edges and Their Influence In Machining Titanium, Dissertation, ETH Zürich.
- [30] RÜTTIMANN, 2012, Simulation of Metal Cutting Processes Using Meshfree Methods, Dissertation, ETH Zürich.
- [31] DUCOBU F., ARRAZOLA P.J., RIVIÈRE-LORPHÈVRE E., FILIPPI E., 2021, On the Selection of an Empirical Material Constitutive Model for the Finite Element Modelling of Ti6Al4V Orthogonal Cutting, Including the Segmented Chip Formation, International Journal of Material Forming, 3.
- [32] CYBENKO G., 1989, Approximation by Superpositions of a Sigmoidal Function, Math. Control Signal Systems, 2, 303–314.
- [33] ALBRECHT P., 1960, New Developments in the Theory of the Metal-Cutting Process: Part I. The Ploughing Process in Metal Cutting, Journal of Engineering for Industry, 82/4, 348–357.
- [34] BABU B., LINDGREN L.E., 2013, Dislocation Density Based Model for Plastic Deformation and Globularization of Ti-6Al-4V, International Journal of Plasticity, 50, 94–108.
- [35] RODRIGUEZ J.M., LARSSON S., CARBONELL J.M., JONSÉN P., 2020, Dislocation Density Based Flow Stress Model Applied to the PFEM Simulation of Orthogonal Cutting Processes of Ti-6Al-4V, Materials, 13/8, 1979, https://doi.org/10.3390/ma13081979.
- [36] PAN Z., LIANG S.Y., GARMESTANI H., SHIH D.S., 2016, Prediction of Machining-Induced Phase Transformation and Grain Growth of Ti-6Al-4 V Alloy, The International Journal of Advanced Manufacturing Technology, 87/1, 859–866.
- [37] MOUFKI A., MOLINARI A., DUDZINSKI D., 1998, Modelling of Orthogonal Cutting with a Temperature Dependent Friction Law, Journal of the Mechanics and Physics of Solids, 46/10, 2103–2138.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2cc251f9-29bf-4520-8d06-938f05ee028d