Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations
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 mach...
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| Format: | Article |
| Language: | English |
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Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT
2022-03-01
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| Series: | Journal of Machine Engineering |
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| Online Access: | http://jmacheng.not.pl/Cutting-Force-Prediction-of-Ti6Al4V-using-a-Machine-Learning-Model-of-SPH-Orthogonal,147201,0,2.html |
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| author | Hagen Klippel Eduardo Gonzalez Sanchez Margolis Isabel Matthias Röthlin Mohamadreza Afrasiabi Kuffa Michal Konrad Wegener |
| author_facet | Hagen Klippel Eduardo Gonzalez Sanchez Margolis Isabel Matthias Röthlin Mohamadreza Afrasiabi Kuffa Michal Konrad Wegener |
| author_sort | Hagen Klippel |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-69f97244e0db42ec9ea24409c93c6ca5 |
| institution | DOAJ |
| issn | 1895-7595 2391-8071 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT |
| record_format | Article |
| series | Journal of Machine Engineering |
| spelling | doaj-art-69f97244e0db42ec9ea24409c93c6ca52025-08-20T03:01:58ZengPublishing House of Wrocław Board of Scientific Technical Societies Federation NOTJournal of Machine Engineering1895-75952391-80712022-03-0122111112310.36897/jme/147201147201Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process SimulationsHagen Klippel0Eduardo Gonzalez Sanchez1Margolis Isabel2Matthias Röthlin3Mohamadreza Afrasiabi4Kuffa Michal5Konrad Wegener6IWF, ETH Zürich, SwitzerlandIWF, ETH Zürich, SwitzerlandIWF/inspire, ETH Zürich, SwitzerlandFederal Office of Meteorology & Climatology, MeteoSwiss, Switzerland, SwitzerlandIWF/inspire, ETH Zürich, SwitzerlandIWF, ETH Zürich, SwitzerlandIWF, ETH Zürich, SwitzerlandThe 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.http://jmacheng.not.pl/Cutting-Force-Prediction-of-Ti6Al4V-using-a-Machine-Learning-Model-of-SPH-Orthogonal,147201,0,2.htmlti6al4vsphmachiningmachine learning |
| spellingShingle | Hagen Klippel Eduardo Gonzalez Sanchez Margolis Isabel Matthias Röthlin Mohamadreza Afrasiabi Kuffa Michal Konrad Wegener Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations Journal of Machine Engineering ti6al4v sph machining machine learning |
| title | Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations |
| title_full | Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations |
| title_fullStr | Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations |
| title_full_unstemmed | Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations |
| title_short | Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations |
| title_sort | cutting force prediction of ti6al4v using a machine learning model of sph orthogonal cutting process simulations |
| topic | ti6al4v sph machining machine learning |
| url | http://jmacheng.not.pl/Cutting-Force-Prediction-of-Ti6Al4V-using-a-Machine-Learning-Model-of-SPH-Orthogonal,147201,0,2.html |
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