A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

Kumar, M. Siva; Rajamani, D.; Nasr, Emad Abouel; Esakki, Balasubramanian; Mohamed, Hamdy M.; Astarita, Antonello

doi:10.3390/ma14216373

Cited by 16 publications

(7 citation statements)

References 65 publications

(78 reference statements)

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“…Out of these 30 solutions, the best one is selected again using the TOPSIS method for all of the responses. It is confirmed from Figure 4 that the highest closeness values are obtained for the MFO algorithm as compared to the DFO [26] and PSO [27] algorithms. The convergence plot for three different performance measures is shown in Figure 5.…”

Section: Resultsmentioning

confidence: 52%

MFO Tunned SVR Models for Analyzing Dimensional Characteristics of Cracks Developed on Steam Generator Tubes

et al. 2022

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Accurate prediction of material defects from the given images will avoid the major cause in industrial applications. In this work, a Support Vector Regression (SVR) model has been developed from the given Gray Level Co-occurrence Matrix (GLCM) features extracted from Magnetic Flux Leakage (MFL) images wherein the length, depth, and width of the images are considered response values from the given features data set, and a percentage of data has been considered for testing the SVR model. Four parameters like Kernel function, solver type, and validation scheme, and its value and % of testing data that affect the SVR model’s performance are considered to select the best SVR model. Six different kernel functions, and three different kinds of solvers are considered as two validation schemes, and 10% to 30% of the testing data set of different levels of the above parameters. The prediction accuracy of the SVR model is considered by simultaneously minimizing prediction measures of both Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) and maximizing values. The Moth Flame Optimization (MFO) algorithm has been implemented to select the best SVR model and its four parameters based on the above conflict three prediction measures by converting multi-objectives into a single object using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The performance of the MFO algorithm is compared statistically with the Dragon Fly Optimization Algorithm (DFO) and Particle Swarm Optimization Algorithm (PSO).

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Section: Resultsmentioning

confidence: 52%

MFO Tunned SVR Models for Analyzing Dimensional Characteristics of Cracks Developed on Steam Generator Tubes

et al. 2022

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“…The general ANFIS network architecture consists of five major steps, as follows: input fuzzification, implication, normalization, defuzzification, and output layers. The detailed step-by-step explanation of how the ANFIS network has been effectively utilized for intelligent modeling of the advanced manufacturing process is explained by the previous works [ 21 , 22 ].…”

Section: Methodologiesmentioning

confidence: 99%

Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm

et al. 2022

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The attainment of intricate part profiles for composite laminates for end-use applications is one of the tedious tasks carried out through conventional machining processes. Therefore, the present work emphasized hybrid intelligent modeling and multi-response optimization of abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL) fabricated through carbon/aramid fiber, reinforced with varying wt% of reduced graphene oxide (r-GO) filled epoxy resin and Nitinol shape memory alloy as the skin material. The AWJC experiments were performed by varying the wt% of r-GO (0, 1, and 2%), traverse speed (400, 500, and 600 mm/min), waterjet pressure (200, 250, and 300 MPa), and stand-off distance (2, 3, and 4 mm) as the input parameters, whereas kerf taper (Kt) and surface roughness (Ra) were considered as the quality responses. A hybrid approach of a parametric optimized adaptive neuro-fuzzy inference system (ANFIS) was adopted through three different metaheuristic algorithms such as particle swarm optimization, moth flame optimization, and dragonfly optimization. The prediction efficiency of the ANFIS network has been found to be significantly improved through the moth flame optimization algorithms in terms of minimized prediction errors, such as mean absolute percentage error and root mean square error. Further, multi-response optimization has been performed for optimized ANFIS response models through the salp swarm optimization (SSO) algorithm to identify the optimal AWJC parameters. The optimal set of parameters, such as 1.004 wt% of r-GO, 600 mm/min of traverse speed, 214 MPa of waterjet pressure, and 4 mm of stand-off distance, were obtained for improved quality characteristics. Moreover, the confirmation experiment results show that an average prediction error of 3.38% for Kt and 3.77% for Ra, respectively, were obtained for SSO, which demonstrates the prediction capability of the proposed optimization algorithm.

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“…The larger ramifications of integrating the methods of mathematical programming and machine learning with EDM are highlighted in this section. It is possible to lessen EDM's impact on the environment by optimising machining settings and increasing productivity [24]. The benefits of the suggested technique have been quantified by measuring sustainability parameters including material utilisation, energy efficiency, and environmental impact.…”

Section: Theme 3: Sustainability and Environmental Impact Of Enhanced...mentioning

confidence: 99%

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Ritu Gupta

2023

IJRITCC

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This study explores the combination of machine guidance and several developing approaches to enhance both precision and effectiveness during Electricity-discharged Machining (EDM) business operations. The studies on quality control, energy efficiency, sustainable development, mathematical modelling within EDM optimization, and machine learning applications in EDM optimisation are all examined in this study. It highlights significant gaps in scientific knowledge, providing a pathway for the development of state-of-the-art EDM methods. The outcomes show that material decrease, energy efficiency, along EDM technique optimisation can all be enhanced. This study offers valuable information for future research within the field and contributes to the ongoing conversation about advanced manufacturing techniques. This project intends to revolutionise EDM by merging mathematical programming and machine learning. Three primary topics are investigated machining parameter optimisation, efficiency improvement using machine learning and environmental effect assessment. The goals of the study are met by using the deductive method, which gives a formal setting in which to examine hypotheses. Descriptive research designs allow for in-depth analyses of previously published works, mathematical models and automated learning programs. Finding commonalities and trends in qualitative data is the goal of the thematic data analysis technique. The results of this study provide useful resources, standards and sustainable perspectives for enhancing EDM procedures in manufacturing settings.

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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

Cited by 16 publications

References 65 publications

MFO Tunned SVR Models for Analyzing Dimensional Characteristics of Cracks Developed on Steam Generator Tubes

MFO Tunned SVR Models for Analyzing Dimensional Characteristics of Cracks Developed on Steam Generator Tubes

Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

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