Application of Artificial Neural Networks in Predicting Surface Quality and Machining Time
This paper explores the use of artificial neural networks to optimize metal machining processes. It has two main components. The first component focuses on developing a neural network in EasyNN to predict quality errors of metal surfaces machined with toroidal milling. The input data are extracted f...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2075-1702/13/7/561 |
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| author | Andrei Raul Osan Raul Florentin Drenţa |
| author_facet | Andrei Raul Osan Raul Florentin Drenţa |
| author_sort | Andrei Raul Osan |
| collection | DOAJ |
| description | This paper explores the use of artificial neural networks to optimize metal machining processes. It has two main components. The first component focuses on developing a neural network in EasyNN to predict quality errors of metal surfaces machined with toroidal milling. The input data are extracted from the experimental values of the machining operations, and the output data are the measured surface roughness values. The network created not only predicts the quality errors but also identifies the key parameters influencing their quality, such as cutting speed, feed rate, and tool geometry, which are crucial for process optimization. The second component underscores the accuracy of the neural network′s predictions by using MatLab to develop a neural network that estimates the machining time, a key factor in cost calculation and efficient order planning. The experimental data used to train the network come from a restricted set of machining jobs. The obtained results demonstrate an adequate estimation of the machining time, thus facilitating the optimization of the entire metal machining process, positively impacting the cost and efficiency of industrial operations. |
| format | Article |
| id | doaj-art-1bed806bd3c2433684f04787b7992dd7 |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-1bed806bd3c2433684f04787b7992dd72025-08-20T02:45:42ZengMDPI AGMachines2075-17022025-06-0113756110.3390/machines13070561Application of Artificial Neural Networks in Predicting Surface Quality and Machining TimeAndrei Raul Osan0Raul Florentin Drenţa1Department of Engineering and Management of Technology, Technical University of Cluj-Napoca, North University Center of Baia Mare, 430122 Baia Mare, RomaniaDepartment of Engineering and Management of Technology, Technical University of Cluj-Napoca, North University Center of Baia Mare, 430122 Baia Mare, RomaniaThis paper explores the use of artificial neural networks to optimize metal machining processes. It has two main components. The first component focuses on developing a neural network in EasyNN to predict quality errors of metal surfaces machined with toroidal milling. The input data are extracted from the experimental values of the machining operations, and the output data are the measured surface roughness values. The network created not only predicts the quality errors but also identifies the key parameters influencing their quality, such as cutting speed, feed rate, and tool geometry, which are crucial for process optimization. The second component underscores the accuracy of the neural network′s predictions by using MatLab to develop a neural network that estimates the machining time, a key factor in cost calculation and efficient order planning. The experimental data used to train the network come from a restricted set of machining jobs. The obtained results demonstrate an adequate estimation of the machining time, thus facilitating the optimization of the entire metal machining process, positively impacting the cost and efficiency of industrial operations.https://www.mdpi.com/2075-1702/13/7/561neural networkinput neuronsoutput neuronshidden neuronsEasyNNMatLab |
| spellingShingle | Andrei Raul Osan Raul Florentin Drenţa Application of Artificial Neural Networks in Predicting Surface Quality and Machining Time Machines neural network input neurons output neurons hidden neurons EasyNN MatLab |
| title | Application of Artificial Neural Networks in Predicting Surface Quality and Machining Time |
| title_full | Application of Artificial Neural Networks in Predicting Surface Quality and Machining Time |
| title_fullStr | Application of Artificial Neural Networks in Predicting Surface Quality and Machining Time |
| title_full_unstemmed | Application of Artificial Neural Networks in Predicting Surface Quality and Machining Time |
| title_short | Application of Artificial Neural Networks in Predicting Surface Quality and Machining Time |
| title_sort | application of artificial neural networks in predicting surface quality and machining time |
| topic | neural network input neurons output neurons hidden neurons EasyNN MatLab |
| url | https://www.mdpi.com/2075-1702/13/7/561 |
| work_keys_str_mv | AT andreiraulosan applicationofartificialneuralnetworksinpredictingsurfacequalityandmachiningtime AT raulflorentindrenta applicationofartificialneuralnetworksinpredictingsurfacequalityandmachiningtime |