Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS
Soft computing techniques, with their self-learning capabilities, fuzzy principles, and evolutionary computational philosophy, are being increasingly utilized in modeling complex machining processes. This study develops artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFI...
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2024-10-01
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| Series: | Fracture and Structural Integrity |
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| Online Access: | https://www.fracturae.com/index.php/fis/article/view/5061/4072 |
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| author | Paresh Kulkarni Satish Chinchanikar |
| author_facet | Paresh Kulkarni Satish Chinchanikar |
| author_sort | Paresh Kulkarni |
| collection | DOAJ |
| description | Soft computing techniques, with their self-learning capabilities, fuzzy principles, and evolutionary computational philosophy, are being increasingly utilized in modeling complex machining processes. This study develops artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models to predict cutting force, surface roughness, and tool life during Inconel 718 turning with a hybrid nanofluid under minimum quantity lubrication. The hybrid nanofluid was created by combining 50�50% multi-walled carbon nanotubes and aluminum oxide nanoparticles with vegetable-based palm oil. ANFIS and ANN models were constructed with data from well-designed machining trials. The ANFIS model predicted machining performance using fuzzy logic, whereas the ANN model employed a feedforward neural network design. The results showed that both models were able to accurately predict the machining performance. However, ANFIS outperforms ANN in terms of accuracy, with prediction errors of 4.47% and 10.97% for surface roughness, and 6.05% and 9.86% for tool life, respectively. However, the accuracy of cutting force prediction was slightly higher with the ANN. This shows that ANFIS could be a better option for forecasting the machining performance while turning Inconel 718. However, this study suggests further investigation into ANFIS modeling, with a focus on membership function parameter optimization through hybrid optimization techniques. |
| format | Article |
| id | doaj-art-01bfcd85914e4e41973ef3bb72b85058 |
| institution | DOAJ |
| issn | 1971-8993 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj-art-01bfcd85914e4e41973ef3bb72b850582025-08-20T03:22:30ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932024-10-011870719010.3221/IGF-ESIS.70.0410.3221/IGF-ESIS.70.04Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFISParesh KulkarniSatish ChinchanikarSoft computing techniques, with their self-learning capabilities, fuzzy principles, and evolutionary computational philosophy, are being increasingly utilized in modeling complex machining processes. This study develops artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models to predict cutting force, surface roughness, and tool life during Inconel 718 turning with a hybrid nanofluid under minimum quantity lubrication. The hybrid nanofluid was created by combining 50�50% multi-walled carbon nanotubes and aluminum oxide nanoparticles with vegetable-based palm oil. ANFIS and ANN models were constructed with data from well-designed machining trials. The ANFIS model predicted machining performance using fuzzy logic, whereas the ANN model employed a feedforward neural network design. The results showed that both models were able to accurately predict the machining performance. However, ANFIS outperforms ANN in terms of accuracy, with prediction errors of 4.47% and 10.97% for surface roughness, and 6.05% and 9.86% for tool life, respectively. However, the accuracy of cutting force prediction was slightly higher with the ANN. This shows that ANFIS could be a better option for forecasting the machining performance while turning Inconel 718. However, this study suggests further investigation into ANFIS modeling, with a focus on membership function parameter optimization through hybrid optimization techniques.https://www.fracturae.com/index.php/fis/article/view/5061/4072annanfisinconel 718subtractive manufacturingtool wearfracturemodeling |
| spellingShingle | Paresh Kulkarni Satish Chinchanikar Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS Fracture and Structural Integrity ann anfis inconel 718 subtractive manufacturing tool wear fracture modeling |
| title | Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS |
| title_full | Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS |
| title_fullStr | Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS |
| title_full_unstemmed | Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS |
| title_short | Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS |
| title_sort | modeling turning performance of inconel 718 with hybrid nanofluid under mql using ann and anfis |
| topic | ann anfis inconel 718 subtractive manufacturing tool wear fracture modeling |
| url | https://www.fracturae.com/index.php/fis/article/view/5061/4072 |
| work_keys_str_mv | AT pareshkulkarni modelingturningperformanceofinconel718withhybridnanofluidundermqlusingannandanfis AT satishchinchanikar modelingturningperformanceofinconel718withhybridnanofluidundermqlusingannandanfis |