Multi-objective optimization of an EDM process for Monel K-500 alloy using response surface methodology-multi-objective dragonfly algorithm
Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium electronic
Typ dokumentu časopisecké články
PubMed
39237665
PubMed Central
PMC11377816
DOI
10.1038/s41598-024-71697-5
PII: 10.1038/s41598-024-71697-5
Knihovny.cz E-zdroje
- Klíčová slova
- EDM, MODA, Monel K-500, RSM,
- Publikační typ
- časopisecké články MeSH
Monel K-500 is a high-performance superalloy composed of nickel and copper, renowned for its exceptional strength, hardness, and resistance to corrosion. To machine this material more precisely and accurately, Electrical Discharge Machining (EDM) is one of the best choices. In EDM, material removal rate (MRR) and electrode wear rate (EWR) are crucial performance parameters that are often conflicting in nature. These parameters depend on several input variables, including peak current (Ip), pulse on time (Ton), duty cycle (Tau), and servo voltage (SV). Optimizing the EDM process is essential for enhancing performance. In this research, a set of experiments were conducted using EDM on Monel K500 alloy to determine the optimal process parameters. The Box-Behnken design was used to prepare the experimental design matrix. Utilizing the experimental data, a second-order mathematical model was developed using Response Surface Methodology (RSM). R2 value is found to be 99.40% and 96.60% for MRR and EWR RSM-based prediction model, respectively. High value of R2 is indicated is indicated good adequacy for prediction. The mathematical model further used in multi-objective dragonfly algorithm (MODA): a new meta-heuristic optimization technique to solve multi-objective optimization problem of EDM. The MODA is a very useful technique to achieve optimal solutions from the multi decision criteria. Utilizing this technique, a set of non-dominated solutions was obtained. Further, the TOPSIS method was used to determine the most desirable optimal solution, which was found to be 0.0135 mm3/min for EWR and 6.968 mm3/min for MRR. These results were obtained when the optimal process parameters were selected as Ip = 6 A, Ton = 200 µs, Tau = 12, and SV = 41.6 V. Operators can machine Monel K500 by selecting the above-mentioned optimal parameters to achieve the best performance.
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Bahar, D., Dvivedi, A. & Kumar, P. On innovative approach in ECDM process by controlling the temperature and stirring rate of the electrolyte. DOI
Kumar, P., Gupta, M. & Kumar, V. Surface integrity analysis of WEDMed specimen of Inconel 825 superalloy. DOI
Ming, W. DOI
Asif, N., Saleem, M. Q. & Farooq, M. U. Performance evaluation of surfactant mixed dielectric and process optimization for electrical discharge machining of titanium alloy Ti6Al4V. DOI
Mandal, P. & Mondal, S. C. Experimental investigation on the performance of copper-based MWCNT composite electrode in EDM. DOI
Mandal, P. & Mondal, S. C. Development and application of Cu-SWCNT nanocomposite–coated 6061Al electrode for EDM. DOI
Mandal, P. & Mondal, S. C. Performance analysis of copper-based MWCNT composite coated 316L SS tool in electro discharge machining. DOI
Farooq, M. U., Anwar, S., Ali, M. A., Hassan, A. & Mushtaq, R. T. Exploring wide-parametric range for tool electrode selection based on surface characterization and machining rate employing powder-mixed electric discharge machining process for Ti6Al4V ELI. DOI
Nguyen, P. H. DOI
Balasubramanian, K., Palanisamy, D., AGS, E. Experimental investigations on WEDM process for machining high manganese steel. DOI
Pramanik, A. DOI
Chandrashekarappa, M. P. G., Kumar, S., Pimenov, D. Y. & Giasin, K. Experimental analysis and optimization of EDM parameters on HcHcr steel in context with different electrodes and dielectric fluids using hybrid Taguchi-based PCA-utility and CRITIC-utility approaches. DOI
Izwan, N. S. L. B., Feng, Z., Patel, J. B. & Hung, W. N. Prediction of material removal rate in die-sinking electrical discharge machining. DOI
Tran, V. T. DOI
Hussain, M. Z. & Khan, U. Evaluation of material removal rate and electrode wear rate in die sinking EDM with tool material Al DOI
Nahak, B. & Gupta, A. A review on optimization of machining performances and recent developments in electro discharge machining.
Mandal, P. & Mondal, S. C. An application of artificial neural network and particle swarm optimisation technique for modelling and optimisation of centreless grinding process. DOI
Sana, M., Asad, M., Farooq, M. U., Anwar, S. & Talha, M. Sustainable electric discharge machining using alumina-mixed deionized water as dielectric: Process modelling by artificial neural networks underpinning net-zero from industry. DOI
Sana, M., Farooq, M. U., Anwar, S. & Haber, R. Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining. PubMed PMC
Sana, M., Asad, M., Farooq, M. U., Anwar, S. & Talha, M. Machine learning for multi-dimensional performance optimization and predictive modelling of nanopowder-mixed electric discharge machining (EDM). DOI
Seidi, M., Yaghoubi, S. & Rabiei, F. Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis. PubMed DOI PMC
Singh, H., Patrange, P., Saxena, P. & Puri, Y. M. Multi-objective optimization of the process parameters in electric discharge machining of 316L porous stainless-steel using metaheuristic techniques. PubMed DOI PMC
Mandal, P. & Mondal, S. C. Multi-objective optimization of Cu-MWCNT composite electrode in electro discharge machining using MOPSO-TOPSIS. DOI
Bhowmick, S. DOI
Montgomery, D. C.
Joshi, M., Ghadai, R. K., Madhu, S., Kalita, K. & Gao, X. Z. Comparison of NSGA-II, MOALO and MODA for multi-objective optimization of micro-machining processes. PubMed DOI PMC
Chang, X. DOI
Wang, J., Yang, W., Du, P. & Li, Y. Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system. DOI
Shoemaker, L. E. & Smith, G. D. A century of monel metal: 1906–2006. DOI
Akgün, M. Performance analysis of electrode materials in electro discharge machining of Monel K-500. DOI
Ramuvel, S. K. & Paramasivam, S. Study on tool steel machining with ZNC EDM by RSM, GREY and NSGA. DOI
Machno, M., Matras, A. & Szkoda, M. Modelling and analysis of the effect of EDM-Drilling parameters on the machining performance of Inconel 718 using the RSM and ANNs methods. PubMed DOI PMC
Mirjalili, S. Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. DOI
https://www.specialmetals.com/documents/technical-bulletins/monel-alloy-k-500.pdf
Sharifi, M. R., Akbarifard, S., Qaderi, K. & Madadi, M. R. A new optimization algorithm to solve multi-objective problems. PubMed DOI PMC