Surface roughness and cutting force estimation in the CNC turning using artificial neural networks
Surface roughness and cutting forces are considered as important factors to determine machinability rate and the quality of product. A number of factors like cutting speed, feed rate, depth of cutting and tool noise radius influence the surface roughness and cutting forces in turning process. In thi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2015-04-01
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| Series: | Management Science Letters |
| Subjects: | |
| Online Access: | http://www.growingscience.com/msl/Vol5/msl_2015_27.pdf |
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| Summary: | Surface roughness and cutting forces are considered as important factors to determine machinability rate and the quality of product. A number of factors like cutting speed, feed rate, depth of cutting and tool noise radius influence the surface roughness and cutting forces in turning process. In this paper, an Artificial Neural Network (ANN) model was used to forecast surface roughness and cutting forces with related inputs, including cutting speed, feed rate, depth of cut and tool noise radius. The machined surface roughness and cutting force parameters related to input parameters are the outputs of the ANN model. In this work, 24 samples of experimental data were used to train the network. Moreover, eight other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation. |
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| ISSN: | 1923-2934 1923-9343 |