An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques
Workpiece surface quality is a critical metric for assessing machining quality. However, due to the complex coupling characteristics of cutting factors, accurately predicting surface roughness remains challenging. Typically, roughness is measured post-machining using specialized instruments, which d...
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MDPI AG
2024-11-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/11/827 |
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| author | Taoming Chen Chun Li Zhexiang Zou Qi Han Bing Li Fengshou Gu Andrew D. Ball |
| author_facet | Taoming Chen Chun Li Zhexiang Zou Qi Han Bing Li Fengshou Gu Andrew D. Ball |
| author_sort | Taoming Chen |
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| description | Workpiece surface quality is a critical metric for assessing machining quality. However, due to the complex coupling characteristics of cutting factors, accurately predicting surface roughness remains challenging. Typically, roughness is measured post-machining using specialized instruments, which delays feedback and hampers timely problem detection, ultimately resulting in cutting resource wastage. To address this issue, this paper introduces a predictive model for workpiece surface roughness based on the finite element (FE) method and advanced image processing techniques. Initially, an orthogonal turning experiment was designed, and an FE cutting model was constructed to assess the distribution of cutting forces and temperatures under varying cutting parameters. Image processing methods (including mesh calibration, edge extraction, and contour fitting) were then applied to extract surface characteristics from the FE simulation outputs, yielding preliminary estimates of surface roughness. By employing range and regression analyses methods, this study quantitatively evaluates the interdependencies among cutting parameters, forces, temperatures, and roughness, subsequently formulating a multivariate regression model to predict surface roughness. Finally, a turning experiment under actual working conditions was conducted, confirming the model’s capacity to predict the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math></inline-formula> trend with an accuracy of 85.07%. Thus, the proposed model provides a precise predictive tool for surface roughness, offering valuable guidance for optimizing machining parameters and supporting proactive control in the turning process, ultimately enhancing machining efficiency and quality. |
| format | Article |
| id | doaj-art-16fdc5b418894345ae7457e337ecfaf8 |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Machines |
| spelling | doaj-art-16fdc5b418894345ae7457e337ecfaf82025-08-20T01:54:07ZengMDPI AGMachines2075-17022024-11-01121182710.3390/machines12110827An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing TechniquesTaoming Chen0Chun Li1Zhexiang Zou2Qi Han3Bing Li4Fengshou Gu5Andrew D. Ball6School of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, ChinaSchool of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, ChinaSchool of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, ChinaSchool of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, ChinaSchool of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, ChinaCentre for Efficiency and Performance Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKCentre for Efficiency and Performance Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKWorkpiece surface quality is a critical metric for assessing machining quality. However, due to the complex coupling characteristics of cutting factors, accurately predicting surface roughness remains challenging. Typically, roughness is measured post-machining using specialized instruments, which delays feedback and hampers timely problem detection, ultimately resulting in cutting resource wastage. To address this issue, this paper introduces a predictive model for workpiece surface roughness based on the finite element (FE) method and advanced image processing techniques. Initially, an orthogonal turning experiment was designed, and an FE cutting model was constructed to assess the distribution of cutting forces and temperatures under varying cutting parameters. Image processing methods (including mesh calibration, edge extraction, and contour fitting) were then applied to extract surface characteristics from the FE simulation outputs, yielding preliminary estimates of surface roughness. By employing range and regression analyses methods, this study quantitatively evaluates the interdependencies among cutting parameters, forces, temperatures, and roughness, subsequently formulating a multivariate regression model to predict surface roughness. Finally, a turning experiment under actual working conditions was conducted, confirming the model’s capacity to predict the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math></inline-formula> trend with an accuracy of 85.07%. Thus, the proposed model provides a precise predictive tool for surface roughness, offering valuable guidance for optimizing machining parameters and supporting proactive control in the turning process, ultimately enhancing machining efficiency and quality.https://www.mdpi.com/2075-1702/12/11/827finite element method (FEM)turning processmultivariate linear regression analysis (MLRA)image processingsurface roughness |
| spellingShingle | Taoming Chen Chun Li Zhexiang Zou Qi Han Bing Li Fengshou Gu Andrew D. Ball An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques Machines finite element method (FEM) turning process multivariate linear regression analysis (MLRA) image processing surface roughness |
| title | An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques |
| title_full | An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques |
| title_fullStr | An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques |
| title_full_unstemmed | An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques |
| title_short | An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques |
| title_sort | advanced approach for predicting workpiece surface roughness using finite element method and image processing techniques |
| topic | finite element method (FEM) turning process multivariate linear regression analysis (MLRA) image processing surface roughness |
| url | https://www.mdpi.com/2075-1702/12/11/827 |
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