Exploiting Tournament Selection-Based Genetic Algorithm in Integrated AHP-Taguchi Analyses-GA Method for Wire Electrical Discharge Machining of AZ91 Magnesium Alloy

Sunday Ayoola Oke, Wasiu Oyediran Adedeji, Meshach Chukwuebuka Ikedue, John Rajan


Concurrent optimization and prioritization of wire EDM parameters can improve resource allocations in material processing and should be effective. This study advances the integrated analytic (AHP)-Taguchi(T)-tournament-based-genetic algorithm (tGA) method to moderate the influence of erroneous resource allocation in parametric analysis decisions in wire electrical discharge machining. The structure builds on the AHP-T method’s platform obtained from the literature and develops it by including the tGA while processing the AZ91 magnesium alloy. The article evaluates the delta values for the average signal-to-noise ratios in the response table and deploys them to arrive at the winners in a league and consequently mutate the chromosomes for performance improvement. The scale of relative importance, consistency index, optimal parametric setting, delta values, and ranks are all established and coupled with the total value and maximum value evaluation at the selection crossover and mutation stages of the genetic algorithm. The results at the mutation, crossover, and selection stages of the tournament selection process showed total values of 124410, 96650, and 70564, respectively. At the selection stage, the maximum value to be the winner of the tournament is 28704. The crossover operation was accomplished after the 5th, 5th, and 6th bit for the first three pairs, respectively. For the selection and crossover operations, the maximum value is 28604 and 27944, respectively. The research clarifies which parameters are the best and worst during optimization using the AHP-T-tGA method.


Optimisation, Prioritisation, AZ91 magnesium alloy, Tournament

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Batra N.K., Singh R.P., Dayal S., 2022, Experimental investigation and statistical modelling of cutting speed in A16063-W composite by wire EDM process, Materials Today: Proceedings, Vol.62, Ni.3, pp.1408-1412. https://doi.org/10.1016/j.matpr.2021.12.418

Biswas A., Roy S.K., Mondal S.P. 2022, Evolutionary algorithm-based approach for solving transportation problems in normal and pandemic scenario, Applied Soft Computing, Vol. 129, Article 109576. https://doi.org/10.1016/j.asoc.2022.109576

Brindle A., 1981, Genetic algorithms for function optimisation, Ph.D. thesis, Department of Computing Science, University of Alberta.

Dayal S., Gupta R.D., Sharma S., Sharma N., Khanna R., 2022, Experimental investigations of WEDM process parameters for magnesium alloy AZ31B biomedical material, Materials Today: Proceedings, Vol.62, No.3, pp.1392-1396. https://doi.org/10.106/j.matpr.2021.12.371

Dutta P., Majumder M., Panja S.C., 2020, Optimisation of material removal rate in wire EDM by polynomial neural network models, Computational Intelligence, Vol.36, No.2, pp. 613-636. https://doi.org/10.1111/coin.12255

Fang Y. and LI. J., 2010, A review of tournament selection in genetic programming, Advances in Computational and Intelligence, 5th International Symposium, ISICA 2010, Wuhan, China, October 22-24. https://doi.org/10.1007/978-3-642-16493-4-19, pp. 181-192.

Golabczak M., Maksim P., Jacquet P., Golabczak A., Wozniak K., Nouveau C., 2019, investigations of geometrical structure and morphology of samples made of hard machinable materials after wire electrical discharge machining and vibro-abrasive finishing, Materials Science and Engineering Technology, Vol. 50, No. 5, pp. 611-615. https://doi.org/10.1002/mawe.201800208

Ghorui N., Ghosh A., Algehyne E.A., Mondal S.P., Saha A.K. 2020, AHP-TOPSIS inspired shopping mall site selection problem with fuzzy data, Mathematics, Vol. 8, Article 1380. https://doi.org/10.3390/math8081380

Ghorui N., Ghosh A., Mondal S.P., Bajuri M.Y., Ahmadian A., Salahshour S., Ferrara M., 2021a, Identification of dominant risk factor involved in spread of COVID-19 using hesitant fuzzy MCDM methodology, Journal: Results in Physics, Vol. 21, Article 103811. https://doi.org/10.1016/j.rinp.2020.103811

Ghosh A., Ghorui N., Mondal S.P., Kumari S., Mondal B.K., Das A., Gupta M.S., 2021b, Application of hexagonal fuzzy MCDM methodology for site selection of electric vehicle charging station, Mathematics, Vol. 9, Article 393. https://doi.org/10.3390/math9040393

Ghosh A., Dey M., Mondal S.P., Shaikh A., Sarkar A., Chatterjee B., 2021c, Selection of best e-rickshaw-a green energy game-changer: An application of AHP and TOPSIS method, Journal of Intelligent and Fuzzy Systems, Vol. 40, No. 6, pp. 11217-11230. https://doi.org/10.3233/JIFS-202406

Grefenstette J.J., Baker J.E., 1989, How genetic algorithms work: A critical look at implicit parallelism. In Schaffer J.D. (Ed.). Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 20-27. Morgan Kaufmann, Publishers, San Francisco.

Gupta D.K. and Dubey A.K., 2021, Multi-process parameters optimisation of wire-EDM on shape memory alloy (Ni54.1Ti) using taguchi approach, Materials Today: Proceeding, Vol. 44, No.1, pp.1423-1427. https://doi.org/10.1016/j.matpr.2020.11.628

Holland J.H. 1975, Adaptation in Natural and Artificial Systems. University of Michigan Press. Ann Arbor.

Ikedue M.C., Oke S.A., 2023, Optimisation of wire electrical discharge machining parameters on AZ91 magnesium alloy using analytical hierarchy process-Taguchi based analyses, in press, Engineering Access.

Juliyana S.J, Prakash J.U., 2022, Multi-objective optimisation of process parameters of wire EDM for machining of AMCs (LM5/ZrO2) using grey relational analysis, Materials Today: Proceedings, Vol.52, No.3, pp. 1494-1498. https://doi.org/10.1016/j.matpr.2021.11.213

Kishore H., Nirak C.K., Agrawal A., 2022, Exploring AZ31B magnesium alloy for innovative micro products by reverse-MEDM, Vol.328, Article 133109, https://doi.org/10.1016/j.matlet.2022.133109

Kumar R., Katyal P., Mandhania S., 2022, Grey relational analysis based multi-response optimisation of WEDM of Z41A magnesium alloy, International Journal of Lightweight Materials and Manufacture, Vol. 5, No. 4, pp. 543-554. https://doi.org/10.1016/j.ij/mm.2022.06.003

Kumar R., Padhi S.S., Sarkar A., 2019, Supplier selection of an Indian heavy locomotive manufacturer: An integrated approach using Taguchi loss function, TOPSIS and AHP, IIMB Management Review, Vol.31, No.1, pp.78-90. https://doi.org/10.1016/j.iimb.2018.08.008

Kumawat A., Goyal A., Dadhich M, Gupta R., 2020, Development and optimisation of triangular profile by using wire EDM machining process, Materials today: proceedings, Vol.28, No.4, pp. 2369-2374. https://doi.org/j.matpr.2020.04.645

Liao C.-N., Kao H.P., 2019, Supplier Selection Model using Taguchi loss function analytic hierarchy process and multi-choice goal programming, computers and industrial Engineering, Vol.58, No.4,pp.571-577. https://doi.org/10.1016/jicie.2009.12.004

Majumder A., 2016, A simple and robust fuzzy-AHP based Taguchi approach for multi-objective optimisation of welding process parameters, Productivity and Quality Management, Vol. 20, No. 1, pp. 116-137

Mohanty A., Nag K.S., Bagal D.K., Barua A., Jeet S., Mahapatra S.S., Cherkia H., 2022, Parametric optimisation of parameters affecting dimension precision of FDM printed part using hybrid Taguchi-MARCOS-nature inspired heuristics optimisation technique, Materials Today: Proceedings, Vol.50, pp. 8930903. https://doi.org/10.1016/j.matpr.2021.06.216

Mohamed M.F., Lenin K., 2020, Optimisation of wire EDM process parameters using Taguchi Technique, Materials Today: Proceedings, Vol. 21, No.1, pp.527-530. https://doi.org/10.1016/j.matpr.2019.06.662

Mojaver P., Khalirya S., Chitsaz A., ASsadi M., 2020, Multiobjective optimisation of a power generation system based on SOFC using Taguchi/AHP/TOPSIS triple method, Sustainable Energy Technologies and Assessments, Vol.18, Article 100674. https://doi.org/10.1016/j.seta.2020.100674

Muniappan A., Sriram M., Thiagarajan C., Raja G.B. and Shaafi T. 2018, Optimization of WEDM process parameters on machining of AZ91 magnesium alloy using MOORA method, IOP Conference Series: Materials Science and Engineering, Vol. 390, Article 012107. http://doi.org/10.1088/1757-899X/390/1/012107

Panwar R., Sharma N., Kumar A., Khanma R., 2022, Experimental investigation of WEDM control parameters for AZ61 mg alloy using ANN modelling, Materials Today: Proceedings, Vol.62, No. 3, pp.1397-1401. https://doi.org/10.1016/j.matpr.2021.12.381

Rahaman M., Mondal S.P., Shaikh A.A., Pramanik P., Roy S., Maiti M.K., Mondal R. & De D., 2020, Artificial bee colony optimization-inspired synergetic study of fractional-order economic production quantity model. Soft Computing, Vol. 24, pp. 15341–15359. https://doi.org/10.1007/s00500-020-04867-y

Song B., Kang S., 2016. A method of assigning weights using ranking a nonhierachy comparison, advances in Decision Schemes, Vol. 2016, Article ID8963214. https://doi.org/10.1155/2016/8963214

Tata N., Pacharu R.K., Devarakonda S.K. (2021). Multiresponse optimisation of process parameters in wire-cut EDM on Inconel 625. Materials Today: Proceedings. 47(19): 6960-6964. https://doi.org/10.1016/j.matpr.2021.05214

DOI: http://dx.doi.org/10.22441/ijiem.v4i1.17387


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