Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model
In hard milling, there has been a significant surge in demand for sustainable machining techniques. Research indicates that the Minimum Quantity Lubrication (MQL) method is a promising approach to achieving sustainability in milling processes due to its eco-friendly characteristics, as well as its c...
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MDPI AG
2025-03-01
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| author | Arumugam Balasuadhakar Sundaresan Thirumalai Kumaran Saood Ali |
| author_facet | Arumugam Balasuadhakar Sundaresan Thirumalai Kumaran Saood Ali |
| author_sort | Arumugam Balasuadhakar |
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| description | In hard milling, there has been a significant surge in demand for sustainable machining techniques. Research indicates that the Minimum Quantity Lubrication (MQL) method is a promising approach to achieving sustainability in milling processes due to its eco-friendly characteristics, as well as its cost-effectiveness and improved cooling efficiency compared to conventional flood cooling. This study investigates the end milling of AISI H11 die steel, utilizing a cooling system that involves a mixture of graphene nanoparticles (Gnps) and sesame oil for MQL. The experimental framework is based on a Taguchi L36 orthogonal array, with key parameters including feed rate, cutting speed, cooling condition, and air pressure. The resulting outcomes for cutting zone temperature and surface roughness were analyzed using the Taguchi Signal-to-Noise ratio and Analysis of Variance (ANOVA). Additionally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction model was developed to assess the impact of process parameters on cutting temperature and surface quality. The optimal cutting parameters were found to be a cutting speed of 40 m/min, a feed rate of 0.01 mm/rev, a jet pressure of 4 bar, and a nano-based MQL cooling environment. The adoption of these optimal parameters resulted in a substantial 62.5% reduction in cutting temperature and a 68.6% decrease in surface roughness. Furthermore, the ANFIS models demonstrated high accuracy, with 97.4% accuracy in predicting cutting temperature and 92.6% accuracy in predicting surface roughness, highlighting their effectiveness in providing precise forecasts for the machining process. |
| format | Article |
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| spelling | doaj-art-6f038b44a3cf4c65b9bb64c8e1c57cc22025-08-20T01:48:48ZengMDPI AGMachines2075-17022025-03-0113323710.3390/machines13030237Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS ModelArumugam Balasuadhakar0Sundaresan Thirumalai Kumaran1Saood Ali2Department of Nautical Science, AMET University, Kanathur, Chennai 603112, Tamil Nadu, IndiaDepartment of Mechanical Engineering, PSG Institute of Technology and Applied Research, Coimbatore 641062, Tamil Nadu, IndiaSchool of Mechanical Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Gyeongbuk, Republic of KoreaIn hard milling, there has been a significant surge in demand for sustainable machining techniques. Research indicates that the Minimum Quantity Lubrication (MQL) method is a promising approach to achieving sustainability in milling processes due to its eco-friendly characteristics, as well as its cost-effectiveness and improved cooling efficiency compared to conventional flood cooling. This study investigates the end milling of AISI H11 die steel, utilizing a cooling system that involves a mixture of graphene nanoparticles (Gnps) and sesame oil for MQL. The experimental framework is based on a Taguchi L36 orthogonal array, with key parameters including feed rate, cutting speed, cooling condition, and air pressure. The resulting outcomes for cutting zone temperature and surface roughness were analyzed using the Taguchi Signal-to-Noise ratio and Analysis of Variance (ANOVA). Additionally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) prediction model was developed to assess the impact of process parameters on cutting temperature and surface quality. The optimal cutting parameters were found to be a cutting speed of 40 m/min, a feed rate of 0.01 mm/rev, a jet pressure of 4 bar, and a nano-based MQL cooling environment. The adoption of these optimal parameters resulted in a substantial 62.5% reduction in cutting temperature and a 68.6% decrease in surface roughness. Furthermore, the ANFIS models demonstrated high accuracy, with 97.4% accuracy in predicting cutting temperature and 92.6% accuracy in predicting surface roughness, highlighting their effectiveness in providing precise forecasts for the machining process.https://www.mdpi.com/2075-1702/13/3/237sustainable machiningminimum quantity lubricationANFISgraphene nanoparticles |
| spellingShingle | Arumugam Balasuadhakar Sundaresan Thirumalai Kumaran Saood Ali Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model Machines sustainable machining minimum quantity lubrication ANFIS graphene nanoparticles |
| title | Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model |
| title_full | Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model |
| title_fullStr | Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model |
| title_full_unstemmed | Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model |
| title_short | Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model |
| title_sort | sustainable cooling strategies in end milling of aisi h11 steel based on anfis model |
| topic | sustainable machining minimum quantity lubrication ANFIS graphene nanoparticles |
| url | https://www.mdpi.com/2075-1702/13/3/237 |
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