Estimation of Tool Life in the Milling Process—Testing Regression Models
The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R<sup>2</sup> parameters of these models. The work is based on our own experiments and the accumulated database (whi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/23/9346 |
| _version_ | 1797399596367872000 |
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| author | Andrzej Paszkiewicz Grzegorz Piecuch Tomasz Żabiński Marek Bolanowski Mateusz Salach Dariusz Rączka |
| author_facet | Andrzej Paszkiewicz Grzegorz Piecuch Tomasz Żabiński Marek Bolanowski Mateusz Salach Dariusz Rączka |
| author_sort | Andrzej Paszkiewicz |
| collection | DOAJ |
| description | The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R<sup>2</sup> parameters of these models. The work is based on our own experiments and the accumulated database (which we make available for further use). The study uses a Haas VF-1 milling machine equipped with vibration sensors and based on a Beckhoff PLC data collector. As the acquired sensor data are continuous, and in order to account for dependencies between them, regression models were used. Support Vector Regression (SVR), decision trees and neural networks were tested during the work. The results obtained show that the best prediction results with the lowest error values were obtained for two-dimensional neural networks using the LBFGS solver (93.9%). Very similar results were also obtained for SVR (93.4%). The research carried out is related to the realisation of intelligent manufacturing dedicated to Industry 4.0 in the field of monitoring production processes, planning service downtime and reducing the level of losses resulting from damage to materials, semi-finished products and tools. |
| first_indexed | 2024-03-09T01:43:24Z |
| format | Article |
| id | doaj.art-c73f9a266b2e4f96aeecba4ee3625be4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:43:24Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c73f9a266b2e4f96aeecba4ee3625be42023-12-08T15:25:33ZengMDPI AGSensors1424-82202023-11-012323934610.3390/s23239346Estimation of Tool Life in the Milling Process—Testing Regression ModelsAndrzej Paszkiewicz0Grzegorz Piecuch1Tomasz Żabiński2Marek Bolanowski3Mateusz Salach4Dariusz Rączka5Department of Complex Systems, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, PolandDepartment of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, PolandDepartment of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, PolandDepartment of Complex Systems, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, PolandDepartment of Complex Systems, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, PolandFaculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, PolandThe article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R<sup>2</sup> parameters of these models. The work is based on our own experiments and the accumulated database (which we make available for further use). The study uses a Haas VF-1 milling machine equipped with vibration sensors and based on a Beckhoff PLC data collector. As the acquired sensor data are continuous, and in order to account for dependencies between them, regression models were used. Support Vector Regression (SVR), decision trees and neural networks were tested during the work. The results obtained show that the best prediction results with the lowest error values were obtained for two-dimensional neural networks using the LBFGS solver (93.9%). Very similar results were also obtained for SVR (93.4%). The research carried out is related to the realisation of intelligent manufacturing dedicated to Industry 4.0 in the field of monitoring production processes, planning service downtime and reducing the level of losses resulting from damage to materials, semi-finished products and tools.https://www.mdpi.com/1424-8220/23/23/9346IIoTCNC machinemachine learningsmart manufacturingtool condition monitoring |
| spellingShingle | Andrzej Paszkiewicz Grzegorz Piecuch Tomasz Żabiński Marek Bolanowski Mateusz Salach Dariusz Rączka Estimation of Tool Life in the Milling Process—Testing Regression Models Sensors IIoT CNC machine machine learning smart manufacturing tool condition monitoring |
| title | Estimation of Tool Life in the Milling Process—Testing Regression Models |
| title_full | Estimation of Tool Life in the Milling Process—Testing Regression Models |
| title_fullStr | Estimation of Tool Life in the Milling Process—Testing Regression Models |
| title_full_unstemmed | Estimation of Tool Life in the Milling Process—Testing Regression Models |
| title_short | Estimation of Tool Life in the Milling Process—Testing Regression Models |
| title_sort | estimation of tool life in the milling process testing regression models |
| topic | IIoT CNC machine machine learning smart manufacturing tool condition monitoring |
| url | https://www.mdpi.com/1424-8220/23/23/9346 |
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