Local corner smoothing based on deep learning for CNC machine tools
Abstract Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essen...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84577-9 |
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| author | Bai Jiang Rong Sun Ze-long Li Liang Xu Huang Liao Xiao-yan Teng Bing Li |
| author_facet | Bai Jiang Rong Sun Ze-long Li Liang Xu Huang Liao Xiao-yan Teng Bing Li |
| author_sort | Bai Jiang |
| collection | DOAJ |
| description | Abstract Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essential components of optimization are introduced, and the local corner smoothness is converted into an optimization problem. The optimization challenge is then resolved by an intelligent optimization algorithm. Considering the influence of population size and computational resources on intelligent optimization algorithms, a deep learning algorithm (the Double-ResNet Local Smoothing (DRLS) algorithm) is proposed to further improve optimization efficiency. The First-Double-Local Smoothing (FDLS) algorithm is used to optimize the positions of NURBS (Non-Uniform Rational B-Spline) control points, and the Second-Double-Local Smoothing (SDLS) algorithm is employed to optimize the NURBS weights to generate a smoother toolpath, thus allowing the cutting tool to pass through each local corner at a higher feedrate during the machining process. In order to ensure machining quality, geometric constraints, drive condition constraints, and contour error constraints are taken into account during the feedrate planning process. Finally, three simulations are presented to verify the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-4a25811c268d46e5a0c268c45cb3f150 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4a25811c268d46e5a0c268c45cb3f1502025-01-05T12:16:03ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-024-84577-9Local corner smoothing based on deep learning for CNC machine toolsBai Jiang0Rong Sun1Ze-long Li2Liang Xu3Huang Liao4Xiao-yan Teng5Bing Li6College of Mechanical and Electrical Engineering, Harbin Engineering UniversityChina Ordnance Industry Group Aviation Ammunition Research InstituteCollege of Intelligent systems Science and Engineering, Harbin Engineering UniversityCollege of Intelligent systems Science and Engineering, Harbin Engineering UniversityCollege of Mechanical and Electrical Engineering, Harbin Engineering UniversityCollege of Mechanical and Electrical Engineering, Harbin Engineering UniversityCollege of Intelligent systems Science and Engineering, Harbin Engineering UniversityAbstract Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essential components of optimization are introduced, and the local corner smoothness is converted into an optimization problem. The optimization challenge is then resolved by an intelligent optimization algorithm. Considering the influence of population size and computational resources on intelligent optimization algorithms, a deep learning algorithm (the Double-ResNet Local Smoothing (DRLS) algorithm) is proposed to further improve optimization efficiency. The First-Double-Local Smoothing (FDLS) algorithm is used to optimize the positions of NURBS (Non-Uniform Rational B-Spline) control points, and the Second-Double-Local Smoothing (SDLS) algorithm is employed to optimize the NURBS weights to generate a smoother toolpath, thus allowing the cutting tool to pass through each local corner at a higher feedrate during the machining process. In order to ensure machining quality, geometric constraints, drive condition constraints, and contour error constraints are taken into account during the feedrate planning process. Finally, three simulations are presented to verify the effectiveness of the proposed method.https://doi.org/10.1038/s41598-024-84577-9Local corner smoothingIntelligent optimization algorithmDeep learningFeedrate planning |
| spellingShingle | Bai Jiang Rong Sun Ze-long Li Liang Xu Huang Liao Xiao-yan Teng Bing Li Local corner smoothing based on deep learning for CNC machine tools Scientific Reports Local corner smoothing Intelligent optimization algorithm Deep learning Feedrate planning |
| title | Local corner smoothing based on deep learning for CNC machine tools |
| title_full | Local corner smoothing based on deep learning for CNC machine tools |
| title_fullStr | Local corner smoothing based on deep learning for CNC machine tools |
| title_full_unstemmed | Local corner smoothing based on deep learning for CNC machine tools |
| title_short | Local corner smoothing based on deep learning for CNC machine tools |
| title_sort | local corner smoothing based on deep learning for cnc machine tools |
| topic | Local corner smoothing Intelligent optimization algorithm Deep learning Feedrate planning |
| url | https://doi.org/10.1038/s41598-024-84577-9 |
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