Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6
The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated...
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| Format: | Article |
| Language: | English |
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Al-Khwarizmi College of Engineering – University of Baghdad
2018-04-01
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| Series: | Al-Khawarizmi Engineering Journal |
| Online Access: | http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/115 |
| _version_ | 1818904017017765888 |
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| author | Mohanned Mohammed H. AL-Khafaji |
| author_facet | Mohanned Mohammed H. AL-Khafaji |
| author_sort | Mohanned Mohammed H. AL-Khafaji |
| collection | DOAJ |
| description | The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. The inputs to all networks are cutting speed, depth of cut, and feed rate. All networks performances (outputs) for all machining force components (cutting force, passive force and feed force) showed perfect match with the experimental data and the calculated correlation coefficients were equal to one. The built network for the chip thickness ratio is giving correlation coefficient equal one too, when its output compared with the experimental results. These networks (models) are used to optimize the cutting parameters that produce the lowest machining force and chip thickness ratio. The models showed that the optimum machining force was (240.46 N) which can be produced when the cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.27 mm/rev). The proposed network for the chip thickness ratio showed that the minimum chip thickness is (1.21), which is at cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.17 mm/rev). |
| first_indexed | 2024-12-19T21:00:45Z |
| format | Article |
| id | doaj.art-91213c09529946299790c9607ac46af3 |
| institution | Directory Open Access Journal |
| issn | 1818-1171 2312-0789 |
| language | English |
| last_indexed | 2024-12-19T21:00:45Z |
| publishDate | 2018-04-01 |
| publisher | Al-Khwarizmi College of Engineering – University of Baghdad |
| record_format | Article |
| series | Al-Khawarizmi Engineering Journal |
| spelling | doaj.art-91213c09529946299790c9607ac46af32022-12-21T20:05:49ZengAl-Khwarizmi College of Engineering – University of BaghdadAl-Khawarizmi Engineering Journal1818-11712312-07892018-04-0114110.22153/kej.2018.10.004Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6Mohanned Mohammed H. AL-Khafaji0Department of Production Engineering and Metallurgy/ University of Technology/ Baghdad/ IraqThe turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. The inputs to all networks are cutting speed, depth of cut, and feed rate. All networks performances (outputs) for all machining force components (cutting force, passive force and feed force) showed perfect match with the experimental data and the calculated correlation coefficients were equal to one. The built network for the chip thickness ratio is giving correlation coefficient equal one too, when its output compared with the experimental results. These networks (models) are used to optimize the cutting parameters that produce the lowest machining force and chip thickness ratio. The models showed that the optimum machining force was (240.46 N) which can be produced when the cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.27 mm/rev). The proposed network for the chip thickness ratio showed that the minimum chip thickness is (1.21), which is at cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.17 mm/rev).http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/115 |
| spellingShingle | Mohanned Mohammed H. AL-Khafaji Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6 Al-Khawarizmi Engineering Journal |
| title | Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6 |
| title_full | Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6 |
| title_fullStr | Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6 |
| title_full_unstemmed | Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6 |
| title_short | Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6 |
| title_sort | neural network modeling of cutting force and chip thickness ratio for turning aluminum alloy 7075 t6 |
| url | http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/115 |
| work_keys_str_mv | AT mohannedmohammedhalkhafaji neuralnetworkmodelingofcuttingforceandchipthicknessratioforturningaluminumalloy7075t6 |