A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy
This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed ba...
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| author | Mahalingam Siva Kumar Devaraj Rajamani Emad Abouel Nasr Esakki Balasubramanian Hussein Mohamed Antonello Astarita |
| author_facet | Mahalingam Siva Kumar Devaraj Rajamani Emad Abouel Nasr Esakki Balasubramanian Hussein Mohamed Antonello Astarita |
| author_sort | Mahalingam Siva Kumar |
| collection | DOAJ |
| description | This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as <i>R<sub>a</sub></i> of 1.5387 µm, <i>kw</i> of 1.2034 mm, and <i>mh</i> of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes. |
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| language | English |
| last_indexed | 2024-03-10T05:59:10Z |
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| series | Materials |
| spelling | doaj.art-dabce3d480a0455ab7fca02c5adfefee2023-11-22T21:11:10ZengMDPI AGMaterials1996-19442021-10-011421637310.3390/ma14216373A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 AlloyMahalingam Siva Kumar0Devaraj Rajamani1Emad Abouel Nasr2Esakki Balasubramanian3Hussein Mohamed4Antonello Astarita5Centre for Autonomous System Research, Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, IndiaCentre for Autonomous System Research, Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, IndiaDepartment of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi ArabiaCentre for Autonomous System Research, Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, IndiaDepartment of Mechanical Engineering, Faculty of Engineering, Helwan University, Cairo 11732, EgyptDepartment of Chemical, Materials, and Industrial Production Engineering, University of Naples Federico II, 80138 Naples, ItalyThis paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as <i>R<sub>a</sub></i> of 1.5387 µm, <i>kw</i> of 1.2034 mm, and <i>mh</i> of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes.https://www.mdpi.com/1996-1944/14/21/6373modelinggenetic algorithmadaptive neuro-fuzzy inference systemoptimizationartificial bee colony algorithmbox-behnken design |
| spellingShingle | Mahalingam Siva Kumar Devaraj Rajamani Emad Abouel Nasr Esakki Balasubramanian Hussein Mohamed Antonello Astarita A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy Materials modeling genetic algorithm adaptive neuro-fuzzy inference system optimization artificial bee colony algorithm box-behnken design |
| title | A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy |
| title_full | A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy |
| title_fullStr | A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy |
| title_full_unstemmed | A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy |
| title_short | A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy |
| title_sort | hybrid approach of anfis artificial bee colony algorithm for intelligent modeling and optimization of plasma arc cutting on monel™ 400 alloy |
| topic | modeling genetic algorithm adaptive neuro-fuzzy inference system optimization artificial bee colony algorithm box-behnken design |
| url | https://www.mdpi.com/1996-1944/14/21/6373 |
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