Modeling and Prediction of Surface Roughness in the End Milling Process using Multiple Regression Analysis and Artificial Neural Network
In recent years, trends have been towards modeling machine processing using artificial intelligence. Artificial neural network (ANN) and multiple regression analysis are methods used to model and optimize the performance of manufacturing technologies. ANN and multiple regression analysis show high r...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Kragujevac
2022-09-01
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| Series: | Tribology in Industry |
| Subjects: | |
| Online Access: | http://www.tribology.rs/journals/2022/2022-3/2022-3-15.html |
| Summary: | In recent years, trends have been towards modeling machine processing using artificial intelligence. Artificial neural network (ANN) and multiple regression analysis are methods used to model and optimize the performance of manufacturing technologies. ANN and multiple regression analysis show high reliability in the prediction and optimization of machining processes. In this paper, machining parameters such as spindle speed, feed rate and depth of cut were used in end milling process to minimize surface roughness. The influence of the parameters on the surface roughness was investigated using an artificial neural network and multiple regression analysis, and results are compared with the measured results. |
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| ISSN: | 0354-8996 2217-7965 |