Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718
Inconel 718 is a hard-to-machine alloy with very poor machinability and low thermal conductivity. Machining of such alloy is a critical manufacturing issue that should be carefully controlled to obtain machined components with acceptable accuracy and surface integrity. In this paper, hybrid machine...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785421011078 |
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| author | Ammar H. Elsheikh T. Muthuramalingam S. Shanmugan Ahmed Mohamed Mahmoud Ibrahim B. Ramesh Ahmed B. Khoshaim Essam B. Moustafa Badr Bedairi Hitesh Panchal Ravishankar Sathyamurthy |
| author_facet | Ammar H. Elsheikh T. Muthuramalingam S. Shanmugan Ahmed Mohamed Mahmoud Ibrahim B. Ramesh Ahmed B. Khoshaim Essam B. Moustafa Badr Bedairi Hitesh Panchal Ravishankar Sathyamurthy |
| author_sort | Ammar H. Elsheikh |
| collection | DOAJ |
| description | Inconel 718 is a hard-to-machine alloy with very poor machinability and low thermal conductivity. Machining of such alloy is a critical manufacturing issue that should be carefully controlled to obtain machined components with acceptable accuracy and surface integrity. In this paper, hybrid machine learning (ML) models are developed to predict the induced residual stresses (RSes) during turning of Inconel 718 alloy. The developed models are composed of a traditional artificial neural network (ANN) incorporated with bio-inspired optimizers, namely pigeon optimization algorithm (POA) and particle swarm optimization (PSO). These optimizers are used to fine-tune the ANN parameters to enhance its prediction accuracy. The models were trained using measured RSes at different cutting conditions. The effects of the cutting conditions, such as cutting speed, cutting depth, and feed rate on the induced RSes are also investigated. The predicted RSes obtained by the developed models were compared with the measured ones as well as with those predicted by traditional ANN. The prediction accuracy of the models was statistically evaluated using seven statistical measures. The ANN–POA and ANN–PSO outperformed the traditional ANN. The coefficient of determination of ANN–POA, ANN–PSO, and ANN was 0.991, 0.938, and 0.585, respectively, while root mean square error was 11.870, 31.487, and 119.437, respectively. |
| first_indexed | 2024-12-20T14:18:08Z |
| format | Article |
| id | doaj.art-9bf250beb5e947c7a0c71d53129fa07f |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-12-20T14:18:08Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-9bf250beb5e947c7a0c71d53129fa07f2022-12-21T19:37:59ZengElsevierJournal of Materials Research and Technology2238-78542021-11-011536223634Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718Ammar H. Elsheikh0T. Muthuramalingam1S. Shanmugan2Ahmed Mohamed Mahmoud Ibrahim3B. Ramesh4Ahmed B. Khoshaim5Essam B. Moustafa6Badr Bedairi7Hitesh Panchal8Ravishankar Sathyamurthy9Department of Production Engineering and Mechanical Design, Tanta University, Tanta, 31527, Egypt; Corresponding author.Department of Mechatronics Engineering, Kattankulathur Campus, SRM Institute of Science and Technology, Chennai, 603203, IndiaResearch Centre for Solar Energy, Department of Physics, Koneru Lakshmaiah Education Foundation, Green Fields, Guntur District, Vaddeswaram, Andhra Pradesh, 522502, IndiaProduction Engineering and Mechanical Design Department, Faculty of Engineering, Minia University, 61519, EgyptInstitute of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602 105, Tamil Nadu, IndiaMechanical Engineering Department, Faculty of Engineering, King Abdulaziz University (KAU), P.O. Box 80204, Jeddah, Saudi ArabiaMechanical Engineering Department, Faculty of Engineering, King Abdulaziz University (KAU), P.O. Box 80204, Jeddah, Saudi ArabiaCollege of Engineering, Taibah University, Madina Al Munawara, Saudi ArabiaGovernment Engineering College Patan, Gujarat, IndiaDepartment of Mechanical Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, 641407, Tamil Nadu, IndiaInconel 718 is a hard-to-machine alloy with very poor machinability and low thermal conductivity. Machining of such alloy is a critical manufacturing issue that should be carefully controlled to obtain machined components with acceptable accuracy and surface integrity. In this paper, hybrid machine learning (ML) models are developed to predict the induced residual stresses (RSes) during turning of Inconel 718 alloy. The developed models are composed of a traditional artificial neural network (ANN) incorporated with bio-inspired optimizers, namely pigeon optimization algorithm (POA) and particle swarm optimization (PSO). These optimizers are used to fine-tune the ANN parameters to enhance its prediction accuracy. The models were trained using measured RSes at different cutting conditions. The effects of the cutting conditions, such as cutting speed, cutting depth, and feed rate on the induced RSes are also investigated. The predicted RSes obtained by the developed models were compared with the measured ones as well as with those predicted by traditional ANN. The prediction accuracy of the models was statistically evaluated using seven statistical measures. The ANN–POA and ANN–PSO outperformed the traditional ANN. The coefficient of determination of ANN–POA, ANN–PSO, and ANN was 0.991, 0.938, and 0.585, respectively, while root mean square error was 11.870, 31.487, and 119.437, respectively.http://www.sciencedirect.com/science/article/pii/S2238785421011078MachiningInconel 718Machine learningParticle swarm optimizationPigeon optimization algorithm |
| spellingShingle | Ammar H. Elsheikh T. Muthuramalingam S. Shanmugan Ahmed Mohamed Mahmoud Ibrahim B. Ramesh Ahmed B. Khoshaim Essam B. Moustafa Badr Bedairi Hitesh Panchal Ravishankar Sathyamurthy Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718 Journal of Materials Research and Technology Machining Inconel 718 Machine learning Particle swarm optimization Pigeon optimization algorithm |
| title | Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718 |
| title_full | Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718 |
| title_fullStr | Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718 |
| title_full_unstemmed | Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718 |
| title_short | Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718 |
| title_sort | fine tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of inconel 718 |
| topic | Machining Inconel 718 Machine learning Particle swarm optimization Pigeon optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2238785421011078 |
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