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Development of a hybrid meta-model for material selection using design of experiments and EDAS method

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Selection of materials for a specific application is one of the extremely demanding problems in a synchronised manufacturing environment as it directly determines perceptible quality and cost of the product. Material selection is a complex process, intending to choose the best material while satisfying a pre-decided set of requirements. Material selection decision is made during preliminary product design stage. An improperly chosen material leads not only to an early component failure but also to a redundant cost involvement. There are numerous materials and various criteria influencing the material selection process for a particular application. Although a good amount of multi-criteria decision-making (MCDM) methods are available to deal with this type of selection applications, this paper aims to propose a hybrid method of design of experiments (DOE) and evaluation based on distance from average solution (EDAS) to solve material selection problems in current industrial applications. DOE and EDAS are used jointly to determine the critical material selection criteria and their interactions by fitting a polynomial to the experimental data in a multiple linear regression analysis. A gear material selection problem is demonstrated to establish the application competence of the DOE-EDAS method. Application results were validated with the results of the previous researchers and they indicate that the proposed DOE-EDAS hybrid model is straightforward, robust and practical in solving complex MCDM problems.
Rocznik
Strony
187--207
Opis fizyczny
Bibliogr. 36 poz., tab.
Twórcy
  • Department of Mechanical Engineering MCKV Institute of Engineering Howrah- 711204, India
autor
  • Department of Mechanical Engineering MCKV Institute of Engineering Howrah- 711204, India
autor
  • Department of Mechanical Engineering Mallabhum Institute of Technology Bankura- 722122, India
autor
  • Subir Chodhury School of Quality and Reliability Indian Institute of Technology Kharagpur- 721302, India
  • Department of Production Engineering Jadavpur University Kolkata- 700032, India
Bibliografia
  • 1. Cicek K., Celik M., Multiple attribute decision-making solution to material selection problem based on modified fuzzy axiomatic design-model selection interface algorithm, Materials & Design, 31(4): 2129–2133, 2010, doi: 10.1016/j.matdes.2009.11.016.
  • 2. Jahan A., Ismail M.Y., Mustapha F., Sapun S.M., Material selection based on ordinal data, Materials & Design, 31(7): 3180–3187, 2010, doi: 10.1016/j.matdes.2010.02.024.
  • 3. Chatterjee P., Athawale V.M., Chakraborty S., Materials selection using complex proportional assessment and evaluation of mixed data methods, Materials & Design, 32(2): 851–860, 2011, doi: 10.1016/j.matdes.2010.07.010.
  • 4. Athawale V.M., Kumar R., Chakraborty S., Decision making for material selection using the UTA method, The International Journal of Advanced Manufacturing Technology, 57(1): 11–22, 2011, doi: 10.1007/s00170-011-3293-7.
  • 5. Huang H., Zhang L., Liu Z., Sutherland J.W., Multi-criteria decision making and uncertainty analysis for materials selection in environmentally conscious design, The International Journal of Advanced Manufacturing Technology, 52(5–8): 421–432, 2011, doi: 10.1007/s00170-010-2745-9.
  • 6. Chauhan A., Vaish R., Magnetic material selection using multiple attribute decision making approach, Materials & Design, 36: 1–5, 2012, doi: 10.1016/j.matdes.2011.11.021.
  • 7. Girubha R.J., Vinodh S., Application of fuzzy VIKOR and environmental impact analysis for material selection of an automotive component, Materials & Design, 37: 478–486, 2012, doi: 10.1016/j.matdes.2012.01.022.
  • 8. Chatterjee P., Chakraborty S., Material selection using preferential ranking methods, Materials & Design, 35: 384–393, 2012, doi: 10.1016/j.matdes.2011.09.027.
  • 9. Maity S.R., Chatterjee P., Chakraborty S., Cutting tool material selection using grey complex proportional assessment method, Materials & Design, 36: 372–378, 2012, doi: 10.1016/j.matdes.2011.11.044.
  • 10. Karande P., Chakraborty S., Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection, Materials & Design, 37: 317– 324, 2012, doi: 10.1016/j.matdes.2012.01.013.
  • 11. Liu H.C., Mao L.X., Zhang Z.Y., Li P., Induced aggregation operators in the VIKOR method and its application in material selection, Applied Mathematical Modelling, 37(9): 6325–6338, 2013, doi: 10.1016/j.apm.2013.01.026.
  • 12. Caliskan H., Kursuncu B., Kurbanoglu C., Guven S.Y., Material selection for the tool holder working under hard milling conditions using different multi criteria decision making methods, Materials & Design, 45: 473–479, 2013, doi: 10.1016/j.matdes.2012.09.042.
  • 13. Prasad K., Chakraborty S., A quality function deployment-based model for materials selection, Materials & Design, 49: 525–535, 2013, doi: 10.1016/j.matdes.2013.01.035.
  • 14. Ilangkumaran M., Avenash A., Balakrishnan V., Kumar S.B., Raja M.B., Material selection using hybrid MCDM approach for automobile bumper, International Journal of Industrial and Systems Engineering, 14(1): 20–39, 2013, doi: 10.1504/IJISE.2013.052919.
  • 15. Giorgetti A., Cavallini C., Citti P., Nicolaie F., Integral aided method for material selection based on quality function deployment and comprehensive VIKOR algorithm, Materials & Design, 47: 27–34, 2013, doi: 10.1016/j.matdes.2012.12.009.
  • 16. Maity S.R., Chakraborty S., Grinding wheel abrasive material selection using fuzzy TOPSIS method, Materials and Manufacturing Processes, 28(4): 408–417, 2013, doi: 10.1080/10426914.2012.700159.
  • 17. Chatterjee P., Chakraborty S., Gear material selection using complex proportional assessment and additive ratio assessment-based approaches: a comparative study, International Journal of Materials Science and Engineering, 1(2): 104–111, 2013, doi: 10.12720/ijmse.1.2.104-111.
  • 18. Karande P., Gauri S.K., Chakraborty S., Applications of utility concept and desirability function for materials selection, Materials & Design, 45: 349–358, 2013, doi: 10.1016/j.matdes.2012.08.067.
  • 19. Anojkumar L., Ilangkumaran M., Sasirekha V., Comparative analysis of MCDM methods for pipe material selection in sugar industry, Expert Systems with Applications: An International Journal, 41(6): 2964–2980, 2014, doi: 10.1016/j.eswa.2013.10.028.
  • 20. Darji V.P., Rao R.V., Intelligent multi criteria decision making methods for material selection in sugar industry, Procedia Materials Science, 5: 2585–2594, 2014, doi: 10.1016/j.mspro.2014.07.519.
  • 21. Yazdani M., Payam A.F., A comparative study on material selection of microelectromechanical systems electrostatic actuators using Ashby, VIKOR and TOPSIS, Materials & Design, 65: 328–334, 2015, doi: 10.1016/j.matdes.2014.09.004.
  • 22. Anojkumar L., Ilangkumaran M., Vignesh M., A decision making methodology for material selection in sugar industry using hybrid MCDM techniques, International Journal of Materials and Product Technology, 51(2): 102–126, 2015, doi: 10.1504/IJMPT.2015.071770.
  • 23. Xue Y.X., You J.X., Lai X.D., Liu H.C., An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information, Applied Soft Computing, 38: 703–713, 2016, doi: 10.1016/j.asoc.2015.10.010.
  • 24. Chandrasekar V.S., Raja K., Material selection for automobile torsion bar using fuzzy TOPSIS tool, International Journal of Advanced Engineering Technology, 7(2): 343–349, 2016.
  • 25. Zhao R., Su H., Chen X., Yu Y., (Wang B., Zhang N., Rosen M.A. – [Eds.]), Commercially available materials selection in sustainable design: an integrated multi-attribute decision making approach, Sustainability, 8(1): 1–15, 2016.
  • 26. Singh T., Patnaik A., Chauhan R., Chauhan P., Selection of brake friction materials using hybrid analytical hierarchy process and Vise Kriterijumska Optimizacija I Kompromisno Resenje approach, Polymer Composites, 2016, doi: 10.1002/pc.24113.
  • 27. Mousavi-Nasab S.H., Sotoudeh-Anvai A., A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems, Materials & Design, 121: 237–253, 2017, doi: 10.1016/j.matdes.2017.02.041.
  • 28. Chatterjee P., Mondal S., Boral S., Banerjee A., Chakraborty S., A novel hybrid method for non-traditional machining process selection using factor relationship and multi-attribute border approximation method, Facta Universitatis, Series: Mechanical Engineering, 15(3): 439–456, 2017, doi.org/10.22190/FUME170508024C.
  • 29. Montgomery D., Design and Analysis of Experiments, John Wiley & Sons, New York, USA, 1997.
  • 30. Ic¸ Y.T., An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies, Robotics and Computer-Integrated Manufacturing, 28(2): 245–256, 2012, doi: 10.1016/j.rcim.2011.09.005.
  • 31. Chatterjee P., Chakraborty S., Development of a meta-model for determination of technological value of cotton fiber using design of experiments and TOPSIS method, Journal of Natural Fibers, 2017, doi:10.1080/15440478.2017.1376303.
  • 32. Chatterjee P., Chakraborty S., A developed meta-model for selection of cotton fabrics using design of experiments and TOPSIS method, Journal of the Institution of Engineers (India): Series E, 98(2): 79–90, 2017, doi: 10.1007/s40034-017-0108-x.
  • 33. Ghorabaee M.K., Zavadskas E. K., Olfat L., Turskis Z., Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS), Informatica, 26(3): 435–451, 2015, doi: 10.15388/Informatica.2015.57.
  • 34. Ghorabaee M. K., Zavadskas E. K., Amiri M., Turskis Z., Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection, International Journal of Computers Communications & Control, 11(3): 358–371, 2016, doi: 10.15837/ijccc.2016.3.2557.
  • 35. Milani A. S., Shanian A., Madoliat R., Nemes J.A., The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection, Structural and Multidisciplinary Optimization, 29(4): 312–318, 2005, doi:10.1007/s00158- 004-0473-1.
  • 36. Ilangkumaran M., Avenash A., Balakrishnan V., Barath Kumar S., Raja M.B., Material selection using hybrid MCDM approach for automobile bumper, International Journal of Industrial and Systems Engineering, 14(1): 20–39, 2013, doi: 10.1504/ IJISE.2013.052919.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-17e949e1-929a-407f-a91d-c78a3dfd2d39
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