Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks
The current study focusses on developing a back propagation neural network model for depth of cut during the abrasive water jet machining of a Ti-6AL-4V aluminum alloy. The study analyzed depth of cut for five different water jet abrasive parameters namely, water pressure, transverse speed, abrasive...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025005973 |
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| author | Yakub Iqbal Mogul Ibtisam Mogul Jaimon Dennis Quadros Ma Mohin Abdul Aabid Muneer Baig Mohammad Abdul Malik |
| author_facet | Yakub Iqbal Mogul Ibtisam Mogul Jaimon Dennis Quadros Ma Mohin Abdul Aabid Muneer Baig Mohammad Abdul Malik |
| author_sort | Yakub Iqbal Mogul |
| collection | DOAJ |
| description | The current study focusses on developing a back propagation neural network model for depth of cut during the abrasive water jet machining of a Ti-6AL-4V aluminum alloy. The study analyzed depth of cut for five different water jet abrasive parameters namely, water pressure, transverse speed, abrasive mass flow rate, abrasive orifice size, and nozzle to orifice diameter. Experiments were conducted as per the L27 Taguchi-design of experiments (DoE). The back propagation neural network model comprising of one input layer, one hidden layer and an output layer with an architecture of 1–5–6 was chosen for conducting the analysis. The algorithm predicted the Taguchi based output values for the experimental depth of cut with an accuracy of up to 95 %. The neural network algorithm further automated itself, generating 50 new data sets for K-cross validation, out of which 70 %, 20 %, and 10 % of the data were used for training, testing, and validation, respectively. Confirmatory experiments were conducted for depth of cut and assessed against the data set used for validation (10 %). The results showed that as the depth of cut was small, i.e., ranging from 3 mm to 5 mm, the algorithm was unable to predict the optimized parameters, however, the prediction improved as the depth of cut increased. Overall, the consistency between the neural network predicted and the experimental depth of cut throughout the algorithm confirmed the validity of the procedure and the appropriateness of the algorithm. |
| format | Article |
| id | doaj-art-a79c99be03d241eea52d9a7ba34b1bc0 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Results in Engineering |
| spelling | doaj-art-a79c99be03d241eea52d9a7ba34b1bc02025-08-20T03:02:07ZengElsevierResults in Engineering2590-12302025-03-012510452010.1016/j.rineng.2025.104520Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networksYakub Iqbal Mogul0Ibtisam Mogul1Jaimon Dennis Quadros2Ma Mohin3Abdul Aabid4Muneer Baig5Mohammad Abdul Malik6National Center for Motorsport Engineering, University of Greater Manchester, Deane Road, Bolton, BL3 5AB, UKCentre for Applied Computer Science, School of Creative Technologies, University of Greater Manchester, Deane Road, Bolton, BL3 5AB, UKDepartment of Mechanical Engineering, University of Greater Manchester, RAK Academic Center, 16038, Ras Al Khaimah, UAEDepartment of Mechanical Engineering, School of Engineering, University of Greater Manchester, Deane Road, Bolton BL3 5AB, UKDepartment of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh 11586, Saudi Arabia; Corresponding author.Department of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh 11586, Saudi ArabiaDepartment of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh 11586, Saudi ArabiaThe current study focusses on developing a back propagation neural network model for depth of cut during the abrasive water jet machining of a Ti-6AL-4V aluminum alloy. The study analyzed depth of cut for five different water jet abrasive parameters namely, water pressure, transverse speed, abrasive mass flow rate, abrasive orifice size, and nozzle to orifice diameter. Experiments were conducted as per the L27 Taguchi-design of experiments (DoE). The back propagation neural network model comprising of one input layer, one hidden layer and an output layer with an architecture of 1–5–6 was chosen for conducting the analysis. The algorithm predicted the Taguchi based output values for the experimental depth of cut with an accuracy of up to 95 %. The neural network algorithm further automated itself, generating 50 new data sets for K-cross validation, out of which 70 %, 20 %, and 10 % of the data were used for training, testing, and validation, respectively. Confirmatory experiments were conducted for depth of cut and assessed against the data set used for validation (10 %). The results showed that as the depth of cut was small, i.e., ranging from 3 mm to 5 mm, the algorithm was unable to predict the optimized parameters, however, the prediction improved as the depth of cut increased. Overall, the consistency between the neural network predicted and the experimental depth of cut throughout the algorithm confirmed the validity of the procedure and the appropriateness of the algorithm.http://www.sciencedirect.com/science/article/pii/S2590123025005973Back propagation neural networkAbrasive water jet machiningDepth of cutTi-6AL-4V aluminum alloyTaguchi-design of experiments |
| spellingShingle | Yakub Iqbal Mogul Ibtisam Mogul Jaimon Dennis Quadros Ma Mohin Abdul Aabid Muneer Baig Mohammad Abdul Malik Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks Results in Engineering Back propagation neural network Abrasive water jet machining Depth of cut Ti-6AL-4V aluminum alloy Taguchi-design of experiments |
| title | Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks |
| title_full | Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks |
| title_fullStr | Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks |
| title_full_unstemmed | Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks |
| title_short | Prediction of cutting depth in abrasive water jet machining of Ti-6AL-4V alloy using back propagation neural networks |
| title_sort | prediction of cutting depth in abrasive water jet machining of ti 6al 4v alloy using back propagation neural networks |
| topic | Back propagation neural network Abrasive water jet machining Depth of cut Ti-6AL-4V aluminum alloy Taguchi-design of experiments |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025005973 |
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