Application of grey correlation-based EDAS method for parametric optimization of non-traditional machining processes

Document Type : Article

Authors

1 Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, India

2 Department of Production Engineering, Jadavpur University, Kolkata, West Bengal, India

Abstract

Higher dimensional accuracy along with better surface finish of various advanced engineering materials has turned out to be the prime desideratum for the present day manufacturing industries. To achieve this, non-traditional machining (NTM) processes have become quite popular because of their ability to produce intricate shape geometries on diverse difficult-to-machine materials. To allow these processes to operate at their fullest capability, it is often recommended to set their different input parameters at the optimal levels. Thus, in this paper, a new technique combining grey correlation method and evaluation based on distance from average solution is applied for simultaneous optimization of three NTM processes, i.e. photochemical machining process, laser-assisted jet electrochemical machining process and abrasive water jet drilling process. The derived optimal parametric combinations outperform those as identified by the other popular multi-objective optimization techniques with respect to the considered response values. The results of analysis of variance also identify the most influencing parameters for the said NTM processes. Finally, the developed surface plots would help the process engineers in investigating the effects of different NTM process parameters on the corresponding grey appraisal scores.

Keywords

References

  • References

    • El-Hofy, H. “Advanced Machining Processes: Nontraditional and Hybrid Machining Processes”, McGraw-Hill, New York (2005).
    • Pandey, P.C. and Shan, H.S. “Modern Machining Processes”, Tata McGraw-Hill Publishing Company Limited, New Delhi (2005).
    • Aggarwal, A. and Singh, H. “Optimization of machining techniques - A retrospective and literature review”, Sadhana, 30(6), pp. 699-711 (2005).
    • Chakraborty, S., Bhattacharyya, B. and Diyaley, S. “Applications of optimization techniques for parametric analysis of non-traditional machining processes: A review”, Management Science Letters, 9(3), pp. 467-494 (2019).
    • Huang, S.C. and Dao, T.P. “Multi-objective optimal design of a 2-DOF flexure-based mechanism using hybrid approach of grey-Taguchi coupled response surface methodology and entropy measurement”, Arabian Journal for Science and Engineering, 41(12), pp. 5215-5231 (2016).
    • Dao, T.P. and Huang, S.C. “Optimization of a two degrees of freedom compliant mechanism using Taguchi method-based grey relational analysis”, Microsystem Technologies, 23(10), pp. 4815-4830 (2017).
    • Prasad, S.R., Ravindranath, K. and Devakumar, M.L.S. “Experimental investigation and parametric optimization in abrasive jet machining on nickel 233 alloy using WASPAS and MOORA”, Cogent Engineering, 5, (2018) https://doi.org/10.1080/23311916.2018.1497830.
    • Ananthakumar, K., Rajamani, D., Balasubramanian, E. et al.. “Measurement and optimization of multi-response characteristics in plasma arc cutting of Monel 400™ using RSM and TOPSIS”, Measurement, 135, pp. 725-737 (2019).
    • Assarzadeh, S. and Ghoreishi, M. “Mathematical modeling and optimization of the electro-discharge machining (EDM) parameters on tungsten carbide composite: Combining response surface methodology and desirability function technique”, Scientia Iranica. Transaction B, Mechanical Engineering, 22(2), pp. 539-560 (2015).
    • Ghorabaee, M.K., Zavadskas, E.K., Olfat, L. et al. “Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS)”, Informatica, 26(3), pp. 435-451 (2015).
    • Kahraman, C., Ghorabaee, M.K., Zavadskas, E.K. et al. “Intuitionistic fuzzy EDAS method: An application to solid waste disposal site selection”, Journal of Environmental Engineering and Landscape Management, 25(1), pp. 1-12 (2017).
    • Ghorabaee, M.K., Amiri, M., Zavadskas, E.K. et al. “A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations”, Computers & Industrial Engineering, 112, pp. 156-174 (2017).
    • Juodagalvienė, B., Turskis, Z., Šaparauskas, J. et al. “Integrated multi-criteria evaluation of house’s plan shape based on the EDAS and SWARA methods”, Engineering Structures and Technologies, 9(3), pp. 117-125 (2017).
    • Li, Y.Y., Wang, J.Q. and Wang, T.L. “A linguistic neutrosophic multi-criteria group decision-making approach with EDAS method”, Arabian Journal for Science and Engineering, 44(3), pp. 2737-2749 (2019).
    • Turskis, Z. and Juodagalvienė, B. “A novel hybrid multi-criteria decision-making model to assess a stairs shape for dwelling houses”, Journal of Civil Engineering and Management, 22(8), pp. 1078-1087 (2016).
    • Xia, X., Govindan, K. and Zhu, Q. “Analyzing internal barriers for automotive parts remanufacturers in China using grey-DEMATEL approach”, Journal of Cleaner Production, 87, pp. 811-825 (2015).
    • Tian, G., Chu, J. and Qiang, T. “Influence factor analysis and prediction models for component removal time in manufacturing”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227(10), pp. 1533-1540 (2013).
    • Kumar, K. and Agarwal, S. “Multi-objective parametric optimization on machining with wire electric discharge machining”, International Journal of Advanced Manufacturing Technology, 62(5-8), pp. 617-633 (2012).
    • Kolahan, F. and Moghaddam, M.A. “The use of Taguchi method with grey relational analysis to optimize the EDM process parameters with multiple quality characteristics”, Scientia Iranica. Transaction B, Mechanical Engineering, 22(2), pp. 530-538 (2015).
    • Mohanty, C.P., Satpathy, M.P., Mahapatra, S.S. et al. “Optimization of cryo-treated EDM variables using TOPSIS-based TLBO algorithm”, Sadhana, 43(4), pp. 1-18 (2018).
    • Li, M., Yu, T., Yang, L. et al. “Parameter optimization during minimum quantity lubrication milling of TC4 alloy with graphene-dispersed vegetable-oil-based cutting fluid”, Journal of Cleaner Production, 209, pp. 1508-1522 (2019).
    • Świercz, R., Oniszczuk-Świercz, D. and Chmielewski, T. “Multi-response optimization of electrical discharge machining using the desirability function”, Micromachines, 10(1), 25 pages, DOI:10.3390/mi10010072(2019).
    • Das, P.P. and Chakraborty, S. “Parametric optimization of non-traditional machining processes using Taguchi method and super ranking concept”, Yugoslav Journal of Operations Research, 29(2), pp. 249-271, (2019).
    • Agrawal, D. and Kamble, D. “Optimization of photochemical machining process parameters for manufacturing microfluidic channel”, Materials and Manufacturing Processes, 34(1), pp.1-7 (2018).
    • Sidhu, S.S. and Yazdani, M. “Comparative analysis of MCDM techniques for EDM of SiC/A359 composite”, Arabian Journal for Science and Engineering, 43(3), pp. 1093-1102 (2018).
    • Chakraborty, S., Chatterjee, P. and Das, P.P. “A DoE-TOPSIS method-based meta-model for parametric optimization of non-traditional machining processes”, Journal of Modelling in Management, 14(2), pp. 430-455 (2019).
    • Ishfaq, K., Mufti, N.A., Ahmed, N. et al. “An investigation of surface roughness and parametric optimization during wire electric discharge machining of cladded material”, International Journal of Advanced Manufacturing Technology, 97(9-12), pp. 4065-4079 (2018).
    • Baghel, R., Mali, H.S. and Biswas, S.K. “Parametric optimization and surface analysis of diamond grinding-assisted EDM of TiN-Al2O3 ceramic composite”, International Journal of Advanced Manufacturing Technology, 100(5-8), pp.1183-1192 (2019).
    • Chakraborty, S. and Das, P.P. “A multivariate quality loss function approach for parametric optimization of non-traditional machining processes”, Management Science Letters, 8(8), pp. 873-884 (2018).
    • Rao, R., & Yadava, V. “Multi-objective optimization of Nd:YAG laser cutting of thin superalloy sheet using grey relational analysis with entropy measurement”, Optics & Laser Technology, 41(8), pp. 922-930 (2009).
    • Tang, L. and Du, Y.T. “Experimental study on green electrical discharge machining in tap water of Ti-6Al-4V and parameters optimization”, International Journal of Advanced Manufacturing Technology, 70(1-4), pp. 469-475 (2014).
    • Jagadish, Ray, A. “Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA)”, International Journal of Advanced Manufacturing Technology, 87(5-8), pp. 1299-1311 (2016).
    • Nadda, R., Kumar, R., Singh, T. et al. “Experimental investigation and optimization of cobalt bonded tungsten carbide composite by hybrid AHP-TOPSIS approach”,  Alexandria Engineering Journal, 57(4), pp. 3419-3428 (2018).
    • Das, P.P., Diyaley, S., Chakraborty, S. et al. “Multi-objective optimization of wire electro discharge machining (WEDM) process parameters using grey-fuzzy approach”, Periodica Polytechnica Mechanical Engineering, 63(1), pp. 16-25 (2019).
    • Chakraborty, S., Das, P.P. and Kumar, V. “Application of grey-fuzzy logic technique for parametric optimization of non-traditional machining processes”, Grey Systems: Theory and Application, 8(1), pp. 46-68 (2018).
    • Tian, G., Zhang, H., Feng, Y. et al. “Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method”, Renewable and Sustainable Energy Reviews, 81, pp. 682-692 (2018).
    • Jia, Z.Y., Ma, J.W., Wang, F.J. et al. “Characteristics forecasting of hydraulic valve based on grey correlation and ANFIS”, Expert Systems with Applications, 37(2), pp. 1250-1255 (2010).
    • Huang, K.Y. and Jane, C.J. “A hybrid model for stock market forecasting and portfolio selection based on ARX, grey system and RS theories”, Expert Systems with Applications, 36(3), pp. 5387-5392 (2009).
    • Zhou, Z.J. and Hu, C.H. “An effective hybrid approach based on grey and ARMA for forecasting gyro drift”, Chaos, Solitons & Fractals, 35(3), pp. 525-529 (2008).
    • Malik, A. and Manna, A. “Multi-response optimization of laser-assisted jet electrochemical machining parameters based on gray relational analysis”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40, 21 pages (2018). https://doi.org/10.1007/s40430-018-1069-9.
    • Nair, A. and Kumanan, S. “Optimization of size and form characteristics using multi-objective grey analysis in abrasive water jet drilling of Inconel 617”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40, 15 pages, (2018). https://doi.org/10.1007/s40430-018-1042-7.
Scientia Iranica
Volume 29, Issue 2
Transactions on Industrial Engineering (E)
March and April 2022
Pages 864-882

Files

Share

How to cite

Statistics