Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints
On-the-fly virtual end-point constraints consists in moving all five axes of the machine tool while nominally maintaining the coincidence of a sensing head centre point with a master ball centre attached to the workpiece table. The sensing head detects the deviations from the nominal coincidence as...
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Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT
2025-05-01
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| Series: | Journal of Machine Engineering |
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| author | Min Zeng J. R. R. Mayer Miao Feng Elie Bitar-Nehme Xuan Truong Duong |
| author_facet | Min Zeng J. R. R. Mayer Miao Feng Elie Bitar-Nehme Xuan Truong Duong |
| author_sort | Min Zeng |
| collection | DOAJ |
| description | On-the-fly virtual end-point constraints consists in moving all five axes of the machine tool while nominally maintaining the coincidence of a sensing head centre point with a master ball centre attached to the workpiece table. The sensing head detects the deviations from the nominal coincidence as a 3D volumetric error vector. More than one ball can be so measured and a fixed length ball bar is also measured for detecting isotropic scaling effects. Initial processing of data using the SAMBA (scale and master ball artefact) method eliminates setup errors and provides estimates of inter- and intra-axis errors as well as volumetric errors vectors. Two ML models are trained and compared, Neural Network (NN) and eXtreme Gradient Boosting (XGBoost), to find the most suitable model and the required amount of training data to predict volumetric errors of a five-axis machine tool with wCBXfZY(S)t topology based on axis commands. The results show that NN marginally outperforms XGBoost and a kinematic model with ratios of prediction error over volumetric error norms of 0.12, 0.13 and 0.14, respectively. |
| format | Article |
| id | doaj-art-427ec99a464c40afa96a8fce9032e952 |
| institution | Kabale University |
| issn | 1895-7595 2391-8071 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT |
| record_format | Article |
| series | Journal of Machine Engineering |
| spelling | doaj-art-427ec99a464c40afa96a8fce9032e9522025-08-20T03:45:04ZengPublishing House of Wrocław Board of Scientific Technical Societies Federation NOTJournal of Machine Engineering1895-75952391-80712025-05-01252132710.36897/jme/203805203805Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point ConstraintsMin Zeng0J. R. R. Mayer1Miao Feng2Elie Bitar-Nehme3Xuan Truong Duong4Department of Mechanical Engineering, Polytechnique Montréal, CanadaDepartment of Mechanical Engineering, Polytechnique Montréal, CanadaDepartment of Computer Science and Operations Research, Université de Montréal, CanadaDepartment of Mechanical Engineering, Polytechnique Montréal, CanadaDepartment of Mechanical Engineering, Dawson College, CanadaOn-the-fly virtual end-point constraints consists in moving all five axes of the machine tool while nominally maintaining the coincidence of a sensing head centre point with a master ball centre attached to the workpiece table. The sensing head detects the deviations from the nominal coincidence as a 3D volumetric error vector. More than one ball can be so measured and a fixed length ball bar is also measured for detecting isotropic scaling effects. Initial processing of data using the SAMBA (scale and master ball artefact) method eliminates setup errors and provides estimates of inter- and intra-axis errors as well as volumetric errors vectors. Two ML models are trained and compared, Neural Network (NN) and eXtreme Gradient Boosting (XGBoost), to find the most suitable model and the required amount of training data to predict volumetric errors of a five-axis machine tool with wCBXfZY(S)t topology based on axis commands. The results show that NN marginally outperforms XGBoost and a kinematic model with ratios of prediction error over volumetric error norms of 0.12, 0.13 and 0.14, respectively.http://jmacheng.not.pl/Comparison-of-Two-Machine-Learning-Models-for-Predicting-Volumetric-Errors-From-On,203805,0,2.htmlmachine learningfive-axis machine toolvolumetric errorsr-test |
| spellingShingle | Min Zeng J. R. R. Mayer Miao Feng Elie Bitar-Nehme Xuan Truong Duong Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints Journal of Machine Engineering machine learning five-axis machine tool volumetric errors r-test |
| title | Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints |
| title_full | Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints |
| title_fullStr | Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints |
| title_full_unstemmed | Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints |
| title_short | Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints |
| title_sort | comparison of two machine learning models for predicting volumetric errors from on the fly r test type device data and virtual end point constraints |
| topic | machine learning five-axis machine tool volumetric errors r-test |
| url | http://jmacheng.not.pl/Comparison-of-Two-Machine-Learning-Models-for-Predicting-Volumetric-Errors-From-On,203805,0,2.html |
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