Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks
The paper presents an approach to solving the problem of modelling and prediction of surface roughness in CNC turning process. In order to solve this problem an experiment was designed. Samples for experimental part of investigation were of dimensions 30 × 350 mm, and the sample material was GJS 50...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2020-01-01
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| Series: | Tehnički Vjesnik |
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| Online Access: | https://hrcak.srce.hr/file/361331 |
| _version_ | 1797207103552618496 |
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| author | Tomislav Šarić* Đorđe Vukelić Katica Šimunović Ilija Svalina Branko Tadić Miljana Prica Goran Šimunović |
| author_facet | Tomislav Šarić* Đorđe Vukelić Katica Šimunović Ilija Svalina Branko Tadić Miljana Prica Goran Šimunović |
| author_sort | Tomislav Šarić* |
| collection | DOAJ |
| description | The paper presents an approach to solving the problem of modelling and prediction of surface roughness in CNC turning process. In order to solve this problem an experiment was designed. Samples for experimental part of investigation were of dimensions 30 × 350 mm, and the sample material was GJS 500 - 7. Six cutting inserts were used for the designed experiment as well as variations of cutting speed, feed and depth of cut on CNC lathe DMG Moriseiki-CTX 310 Ecoline. After the conducted experiment, surface roughness of each sample was measured and a data set of 750 instances was formed. For data analysis, the Back-Propagation Neural Network (BPNN) algorithm was used. In modelling different BPNN architectures with characteristic features the results of RMS (Root Mean Square) error were controlled. Specially analysed were the RMS errors realised by different number of neurons in hidden layers. For the BPNN architecture with one hidden layer the architecture (4 – 8 - 1) was adopted with RMS error of 3,37%. In modelling the BPNN architecture with two hidden layers, a considerable amount of architectures was investigated. The adopted architecture with two hidden layers (4 - 2 - 10 - 1) generated the RMS error of 2,26%. The investigation was also directed at the size of the data set and controlling the level of RMS error. |
| first_indexed | 2024-04-24T09:17:35Z |
| format | Article |
| id | doaj.art-e370567aaac04e29959aee9f329ff902 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:17:35Z |
| publishDate | 2020-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-e370567aaac04e29959aee9f329ff9022024-04-15T16:40:40ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392020-01-012761923193010.17559/TV-20200818114207Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural NetworksTomislav Šarić*0Đorđe Vukelić1Katica Šimunović2Ilija Svalina3Branko Tadić4Miljana Prica5Goran Šimunović6Mechanical Engineering Faculty in Slavonski Brod, University of Slavonski Brod, Trg Ivane Brlic Mazuranic 2, HR-35000 Slavonski Brod, CroatiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, SerbiaMechanical Engineering Faculty in Slavonski Brod, University of Slavonski Brod, Trg Ivane Brlic Mazuranic 2, HR-35000 Slavonski Brod, CroatiaMechanical Engineering Faculty in Slavonski Brod, University of Slavonski Brod, Trg Ivane Brlic Mazuranic 2, HR-35000 Slavonski Brod, CroatiaFaculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000 Kragujevac, SerbiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, SerbiaMechanical Engineering Faculty in Slavonski Brod, University of Slavonski Brod, Trg Ivane Brlic Mazuranic 2, HR-35000 Slavonski Brod, CroatiaThe paper presents an approach to solving the problem of modelling and prediction of surface roughness in CNC turning process. In order to solve this problem an experiment was designed. Samples for experimental part of investigation were of dimensions 30 × 350 mm, and the sample material was GJS 500 - 7. Six cutting inserts were used for the designed experiment as well as variations of cutting speed, feed and depth of cut on CNC lathe DMG Moriseiki-CTX 310 Ecoline. After the conducted experiment, surface roughness of each sample was measured and a data set of 750 instances was formed. For data analysis, the Back-Propagation Neural Network (BPNN) algorithm was used. In modelling different BPNN architectures with characteristic features the results of RMS (Root Mean Square) error were controlled. Specially analysed were the RMS errors realised by different number of neurons in hidden layers. For the BPNN architecture with one hidden layer the architecture (4 – 8 - 1) was adopted with RMS error of 3,37%. In modelling the BPNN architecture with two hidden layers, a considerable amount of architectures was investigated. The adopted architecture with two hidden layers (4 - 2 - 10 - 1) generated the RMS error of 2,26%. The investigation was also directed at the size of the data set and controlling the level of RMS error.https://hrcak.srce.hr/file/361331CNC turningNeural Networkspredictionsurface roughness |
| spellingShingle | Tomislav Šarić* Đorđe Vukelić Katica Šimunović Ilija Svalina Branko Tadić Miljana Prica Goran Šimunović Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks Tehnički Vjesnik CNC turning Neural Networks prediction surface roughness |
| title | Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks |
| title_full | Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks |
| title_fullStr | Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks |
| title_full_unstemmed | Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks |
| title_short | Modelling and Prediction of Surface Roughness in CNC Turning Process using Neural Networks |
| title_sort | modelling and prediction of surface roughness in cnc turning process using neural networks |
| topic | CNC turning Neural Networks prediction surface roughness |
| url | https://hrcak.srce.hr/file/361331 |
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