Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method
It is well understood that burr size and shape, as well as surface quality attributes like surface roughness in milling parts, vary according to several factors. These include cutting tool orientation, cutting profile, cutting parameters, tool shape and size, coating, and the interaction between the...
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MDPI AG
2025-06-01
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| author | Ali Khosrozadeh Seyed Ali Niknam Fatemeh Hajizadeh |
| author_facet | Ali Khosrozadeh Seyed Ali Niknam Fatemeh Hajizadeh |
| author_sort | Ali Khosrozadeh |
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| description | It is well understood that burr size and shape, as well as surface quality attributes like surface roughness in milling parts, vary according to several factors. These include cutting tool orientation, cutting profile, cutting parameters, tool shape and size, coating, and the interaction between the workpiece and the cutting tool. Therefore, burr size cannot be formulated simply as a function of direct parameters. This study proposes an ensemble learning regression model to accurately predict burr size and surface roughness during the slot milling of aluminum alloy (AA) 6061. The model was trained using cutting parameters as inputs and evaluated with performance metrics such as mean absolute error (<i>MAE</i>), mean squared error (<i>MSE</i>), and the coefficient of determination (<i>R</i><sup>2</sup>). The model demonstrated strong generalization capability when tested on unseen data. Specifically, it achieved an <i>R</i><sup>2</sup> of 0.97 for surface roughness (<i>Ra</i>) and <i>R</i><sup>2</sup> values of 0.93 (<i>B</i>5, <i>B</i>8), 0.92 (<i>B</i>2), 0.86 (<i>B</i>1), and 0.65 (<i>B</i>4) for various burr types. These results validate the model’s effectiveness despite the nonlinear and complex nature of burr formation. Additionally, feature importance analysis via the <i>F-</i>test indicated that feed per tooth and depth of cut were the most influential parameters across several burr types and surface roughness outcomes. This work represents a novel and accurate approach for predicting key surface quality indicators, with significant implications for process optimization and cost reduction in precision machining. |
| format | Article |
| id | doaj-art-afa11276db8247dbb548d9dd16ee05a5 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Machines |
| spelling | doaj-art-afa11276db8247dbb548d9dd16ee05a52025-08-20T03:27:17ZengMDPI AGMachines2075-17022025-06-0113649410.3390/machines13060494Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression MethodAli Khosrozadeh0Seyed Ali Niknam1Fatemeh Hajizadeh2Sustainable Manufacturing Systems Research Laboratory (SMSRL), School of Mechanical Engineering, Iran University of Science and Technology, Tehran 13114-16846, IranSustainable Manufacturing Systems Research Laboratory (SMSRL), School of Mechanical Engineering, Iran University of Science and Technology, Tehran 13114-16846, IranSustainable Manufacturing Systems Research Laboratory (SMSRL), School of Mechanical Engineering, Iran University of Science and Technology, Tehran 13114-16846, IranIt is well understood that burr size and shape, as well as surface quality attributes like surface roughness in milling parts, vary according to several factors. These include cutting tool orientation, cutting profile, cutting parameters, tool shape and size, coating, and the interaction between the workpiece and the cutting tool. Therefore, burr size cannot be formulated simply as a function of direct parameters. This study proposes an ensemble learning regression model to accurately predict burr size and surface roughness during the slot milling of aluminum alloy (AA) 6061. The model was trained using cutting parameters as inputs and evaluated with performance metrics such as mean absolute error (<i>MAE</i>), mean squared error (<i>MSE</i>), and the coefficient of determination (<i>R</i><sup>2</sup>). The model demonstrated strong generalization capability when tested on unseen data. Specifically, it achieved an <i>R</i><sup>2</sup> of 0.97 for surface roughness (<i>Ra</i>) and <i>R</i><sup>2</sup> values of 0.93 (<i>B</i>5, <i>B</i>8), 0.92 (<i>B</i>2), 0.86 (<i>B</i>1), and 0.65 (<i>B</i>4) for various burr types. These results validate the model’s effectiveness despite the nonlinear and complex nature of burr formation. Additionally, feature importance analysis via the <i>F-</i>test indicated that feed per tooth and depth of cut were the most influential parameters across several burr types and surface roughness outcomes. This work represents a novel and accurate approach for predicting key surface quality indicators, with significant implications for process optimization and cost reduction in precision machining.https://www.mdpi.com/2075-1702/13/6/494ensemble learning regression methodsurface roughnessburrmillingaluminum alloy |
| spellingShingle | Ali Khosrozadeh Seyed Ali Niknam Fatemeh Hajizadeh Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method Machines ensemble learning regression method surface roughness burr milling aluminum alloy |
| title | Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method |
| title_full | Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method |
| title_fullStr | Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method |
| title_full_unstemmed | Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method |
| title_short | Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method |
| title_sort | construction of a surface roughness and burr size prediction model through the ensemble learning regression method |
| topic | ensemble learning regression method surface roughness burr milling aluminum alloy |
| url | https://www.mdpi.com/2075-1702/13/6/494 |
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