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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Autores principales:	Mahalingam Siva Kumar, Devaraj Rajamani, Emad Abouel Nasr, Esakki Balasubramanian, Hussein Mohamed, Antonello Astarita
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	modeling genetic algorithm adaptive neuro-fuzzy inference system optimization artificial bee colony algorithm box-behnken design Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/dabce3d480a0455ab7fca02c5adfefee
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spelling	oai:doaj.org-article:dabce3d480a0455ab7fca02c5adfefee2021-11-11T17:56:45ZA Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy10.3390/ma142163731996-1944https://doaj.org/article/dabce3d480a0455ab7fca02c5adfefee2021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6373https://doaj.org/toc/1996-1944This 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.Mahalingam Siva KumarDevaraj RajamaniEmad Abouel NasrEsakki BalasubramanianHussein MohamedAntonello AstaritaMDPI AGarticlemodelinggenetic algorithmadaptive neuro-fuzzy inference systemoptimizationartificial bee colony algorithmbox-behnken designTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6373, p 6373 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	modeling genetic algorithm adaptive neuro-fuzzy inference system optimization artificial bee colony algorithm box-behnken design Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	modeling genetic algorithm adaptive neuro-fuzzy inference system optimization artificial bee colony algorithm box-behnken design Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 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
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.
format	article
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
title	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_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_sort	hybrid approach of anfis—artificial bee colony algorithm for intelligent modeling and optimization of plasma arc cutting on monel™ 400 alloy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/dabce3d480a0455ab7fca02c5adfefee
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A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

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