Exploration of GRA based multiobjective optimization of magnetic abrasive finishing process using simulated annealing

Kumar, Singh Rajneesh; Gangwar, Swati; Singh, Devendra Pratap

doi:10.5937/fmet2001195r

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Singh et al combined the Simulated annealing with GRA to improve the surface texture of Aluminum 6060 workpiece finished via MAF. Their hybrid method had efficiently optimized the MAF performance (Kumar et al, 2020). Azhiri et al established a hybrid optimization method by merging "neuro-fuzzy" system with GRA to optimize the "surface roughness and cutting velocity" of Aluminium composite machined by WEDM.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of magnetic abrasive finishing by desirability function with genetic algorithm

Singh¹,

Gangwar²,

Singh³

et al. 2021

Int. J. Eng. Sci. Tech

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Thermal stability and Surface hardness of the super-finished surface is a very important aspect to preserve the surface texture of workpiece in the MAF process. In this present study, the multi-objective optimization of EN-31 finished through the MAF process. “Increase in Temperature” and “Increase in Hardness” are considered for optimization to diminish their impact on the super-finished surface of EN-31. In present work Desirability function analysis (DFA) has been used to optimize the desired responses of the MAF process. Experiments were designed according to Taguchi L9 orthogonal array for the finishing of EN-31. The experiment results are processed using DFA and Desirability fitness function is established to convert the single response to multi-response. Genetic Algorithm (GA) is used to enhance the results of DFA and the regression model was developed to obtain the objective function of Genetic algorithm. Smaller-the-best criteria were used for ‘Increase in Temperature’ and ‘Increase in Hardness’ for obtaining favorable process parameters. The best optimal parametric combination is obtained by using the GA-DFA hybrid approach is at 2.5 mm (working gap), 20 gm (abrasive weight), and 2.0 A (Current) and 300 rpm (rotational speed).

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Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of magnetic abrasive finishing by desirability function with genetic algorithm

Singh¹,

Gangwar²,

Singh³

et al. 2021

Int. J. Eng. Sci. Tech

Self Cite

View full text Add to dashboard Cite

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Optimizing process parameters and a comparative study of post-weld heat treatments on the microstructure and mechanical properties of 0.3%C-Cr-Mo-V steel

Radhakrishnan,

Hameed,

Muralidharan

et al. 2024

Int J Interact Des Manuf

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Intelligent optimization of wire-EDM parameters for surface roughness and material removal rate while machining WC-Co composite

Gupta¹

2021

FME Transactions

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This work presents the wire-EDM of WC-Co composite and optimization of process parameters using an integrated technique of response surface methodology (RSM), Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) and artificial bee colony (ABC) algorithm to obtain the best set of machinability indicators. Wire feed (WF), servo voltage (SV), pulse off-time (Pon) and pulse on-time (Poff) are the variable process parameters, whereas root mean square roughness (Rq), average surface roughness (Ra) and material removal rate (MRR) are the machinability indicators considered in the present work. A total of twenty nine experiments have been conducted based on Box Behnken design (BBD) technique of response surface methodology. VIKOR has been used for normalization of responses and followed by solving empirical models using ABC algorithm to obtain optimized process parameters setting. WF-12 m/min, SV-65V, Pon-116 µs, Poff-20 µs are the optimum wire-EDM parameters obtained by intelligent RSM-VIKOR-ABC technique that produced best values of Ra-4.51 µm, Rq-5.64 µm, MRR-0.061 mm3 /min simultaneously. The validation test confirmed an improvement up to 15% in the response characteristics which proved the effectiveness of this novel hybrid technique for optimization. The optimum parameter setting is for ready industrial reference to attain best surface quality and process productivity for WC-Co composite machining by wire-EDM.

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Exploration of GRA based multiobjective optimization of magnetic abrasive finishing process using simulated annealing

Cited by 4 publications

References 22 publications

Multi-objective optimization of magnetic abrasive finishing by desirability function with genetic algorithm

Multi-objective optimization of magnetic abrasive finishing by desirability function with genetic algorithm

Optimizing process parameters and a comparative study of post-weld heat treatments on the microstructure and mechanical properties of 0.3%C-Cr-Mo-V steel

Intelligent optimization of wire-EDM parameters for surface roughness and material removal rate while machining WC-Co composite

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