Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid
Surface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined surface. The u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/10/5/81 |
| _version_ | 1797498380403867648 |
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| author | Vineet Dubey Anuj Kumar Sharma Danil Yurievich Pimenov |
| author_facet | Vineet Dubey Anuj Kumar Sharma Danil Yurievich Pimenov |
| author_sort | Vineet Dubey |
| collection | DOAJ |
| description | Surface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined surface. The use of cutting fluid in the machining zone helps to minimize the heat generation. In this paper, minimum quantity lubrication is used in turning of AISI 304 steel for determining the surface roughness. The cutting fluid is enriched with alumina nanoparticles of two different average particle sizes of 30 and 40 nm. Among the input parameters chosen for investigation are cutting speed, depth of cut, feed rate, and nanoparticle concentration. The response surface approach is used in the design of the experiment (RSM). For the purpose of estimating the surface roughness and comparing the experimental value to the predicted values, three machine learning-based models, including linear regression (LR), random forest (RF), and support vector machine (SVM), are utilized in addition. For the purpose of evaluating the accuracy of the predicted values, the coefficient of determination (R2), mean absolute percentage error (MAPE), and mean square error (MSE) were all used. Random forest outperformed the other two models in both the particle sizes of 30 and 40 nm, with R-squared of 0.8176 and 0.7231, respectively. Thus, this study provides a novel approach in predicting the surface roughness by varying the particle size in the cutting fluid using machine learning, which can save time and wastage of material and energy. |
| first_indexed | 2024-03-10T03:32:38Z |
| format | Article |
| id | doaj.art-37864cd34a0e4c57bd4392c745b0d50e |
| institution | Directory Open Access Journal |
| issn | 2075-4442 |
| language | English |
| last_indexed | 2024-03-10T03:32:38Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj.art-37864cd34a0e4c57bd4392c745b0d50e2023-11-23T11:51:21ZengMDPI AGLubricants2075-44422022-05-011058110.3390/lubricants10050081Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting FluidVineet Dubey0Anuj Kumar Sharma1Danil Yurievich Pimenov2Center for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow 226031, IndiaCenter for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow 226031, IndiaDepartment of Automated Mechanical Engineering, South Ural State University, Lenin Prosp, 76, 454080 Chelyabinsk, RussiaSurface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined surface. The use of cutting fluid in the machining zone helps to minimize the heat generation. In this paper, minimum quantity lubrication is used in turning of AISI 304 steel for determining the surface roughness. The cutting fluid is enriched with alumina nanoparticles of two different average particle sizes of 30 and 40 nm. Among the input parameters chosen for investigation are cutting speed, depth of cut, feed rate, and nanoparticle concentration. The response surface approach is used in the design of the experiment (RSM). For the purpose of estimating the surface roughness and comparing the experimental value to the predicted values, three machine learning-based models, including linear regression (LR), random forest (RF), and support vector machine (SVM), are utilized in addition. For the purpose of evaluating the accuracy of the predicted values, the coefficient of determination (R2), mean absolute percentage error (MAPE), and mean square error (MSE) were all used. Random forest outperformed the other two models in both the particle sizes of 30 and 40 nm, with R-squared of 0.8176 and 0.7231, respectively. Thus, this study provides a novel approach in predicting the surface roughness by varying the particle size in the cutting fluid using machine learning, which can save time and wastage of material and energy.https://www.mdpi.com/2075-4442/10/5/81turninglubricationmachiningcutting fluidnanofluidmachine learning |
| spellingShingle | Vineet Dubey Anuj Kumar Sharma Danil Yurievich Pimenov Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid Lubricants turning lubrication machining cutting fluid nanofluid machine learning |
| title | Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid |
| title_full | Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid |
| title_fullStr | Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid |
| title_full_unstemmed | Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid |
| title_short | Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid |
| title_sort | prediction of surface roughness using machine learning approach in mql turning of aisi 304 steel by varying nanoparticle size in the cutting fluid |
| topic | turning lubrication machining cutting fluid nanofluid machine learning |
| url | https://www.mdpi.com/2075-4442/10/5/81 |
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