A NOVEL HYBRID METHOD FOR NON-TRADITIONAL MACHINING PROCESS SELECTION USING FACTOR RELATIONSHIP AND MULTI-ATTRIBUTIVE BORDER APPROXIMATION METHOD

Selection of the most appropriate non-traditional machining process (NTMP) for a definite machining requirement can be observed as a multi-criteria decision-making (MCDM) problem with conflicting criteria. This paper proposes a novel hybrid method encompassing factor relationship (FARE) and multi-attributive border approximation area comparison (MABAC) methods for selection and evaluation of NTMPs. The application of FARE method is pioneered in NTMP assessment domain to estimate criteria weights. It significantly condenses the problem of pairwise comparisons for estimating criteria weights in MCDM environment. In order to analyze and rank different NTMPs in accordance with their performance and technical properties, MABAC method is applied. Computational procedure of FARE-MABAC hybrid model is demonstrated while solving an NTMP selection problem for drilling cylindrical through holes on non-conductive ceramic materials. The results achieved by FARE-MABAC method exactly corroborate with those obtained by the past researchers which validate the usefulness of this method while solving complex NTMP selection problems.

[1]  Shankar Chakraborty,et al.  A decision-making model for non-traditional machining processes selection , 2014 .

[2]  Shankar Chakraborty,et al.  A combined TOPSIS-AHP-method-based approach for non-traditional machining processes selection , 2008 .

[3]  Dragan Pamucar,et al.  The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC) , 2015, Expert Syst. Appl..

[4]  Hakan Tozan,et al.  A fuzzy based decision model for nontraditional machining process selection , 2014 .

[5]  Shankar Chakraborty,et al.  Applications of the MOORA method for decision making in manufacturing environment , 2011 .

[6]  Romualdas Ginevicius,et al.  A New Determining Method for the Criteria Weights in multicriteria Evaluation , 2011, Int. J. Inf. Technol. Decis. Mak..

[7]  S. Rajakarunakaran,et al.  COPRAS Decision Model to Optimize Blind Spot in Heavy Vehicles: A Comparative Perspective , 2014 .

[8]  Hakan Tozan,et al.  A fuzzy based decision support model for non-traditional machining process selection , 2013 .

[9]  Can Cogun.  Computer-aided preliminary selection of nontraditional machining processes , 1994 .

[10]  Shankar Chakraborty,et al.  Application of fuzzy axiomatic design principles for selection of non-traditional machining processes , 2016 .

[11]  Shankar Chakraborty,et al.  Application of PROMETHEE-GAIA method for non-traditional machining processes selection , 2012 .

[12]  Shankar Chakraborty,et al.  Non-traditional machining processes selection using data envelopment analysis (DEA) , 2011, Expert Syst. Appl..

[13]  Shankar Chakraborty,et al.  Design of an analytic-hierarchy-process-based expert system for non-traditional machining process selection , 2006 .

[14]  S. Boral,et al.  A case-based reasoning approach for non-traditional machining processes selection , 2016 .

[15]  Agha Iqbal Ali,et al.  Comparative advantage and disadvantage in DEA , 1997, Ann. Oper. Res..

[16]  R Jehadeesan,et al.  Web-based Knowledge Based System for Selection of Non-traditional Machining Processes , 2008 .

[17]  Shankar Chakraborty,et al.  Selection of non-traditional machining processes using analytic network process , 2011 .

[18]  Can Cogun.  Computer-aided system for selection of nontraditional machining operations , 1993 .

[19]  Vijay K. Jain,et al.  Advanced Machining Processes , 2014 .

[20]  Gamini Herath,et al.  Using Multi-Criteria Decision Analysis in Natural Resource Management , 2006 .

[21]  Shankar Chakraborty,et al.  A digraph-based expert system for non-traditional machining processes selection , 2009 .

[22]  Mustafa Yurdakul,et al.  Development of a multi-attribute selection procedure for non-traditional machining processes , 2003 .

[23]  Edison Chandraseelan.R.,et al.  A Knowledge Base for Non-Traditional Machining Process Selection , 2008 .

[24]  Shankar Chakraborty,et al.  QFD-based expert system for non-traditional machining processes selection , 2007, Expert Syst. Appl..

[25]  Amitava Ray,et al.  Non-traditional machining process selection using integrated fuzzy AHP and QFD techniques: a customer perspective , 2014 .

[26]  Partha Pratim Das,et al.  Selection of non-traditional machining process: A distance based approach , 2013, 2013 IEEE International Conference on Industrial Engineering and Engineering Management.

[27]  Ivan Petrovic,et al.  Modification of the Best-Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers , 2018, Expert Syst. Appl..

[28]  Ibo van de Poel,et al.  Methodological problems in QFD and directions for future development , 2007 .

[29]  Dragan Pamučar,et al.  Novel approach to group multi-criteria decision making based on interval rough numbers: Hybrid DEMATEL-ANP-MAIRCA model , 2017, Expert Syst. Appl..

[30]  Prasenjit Chatterjee,et al.  Selection of materials using multi-criteria decision-making methods with minimum data , 2013 .

[31]  Dragan Pamučar,et al.  Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia , 2017 .

[32]  MILOŠ MADIĆ,et al.  NON-CONVENTIONAL MACHINING PROCESSES SELECTION USING MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF RATIO ANALYSIS METHOD , 2015 .

[33]  Samah Abu-Assab.  Integration of Preference Analysis Methods into QFD for Elderly People , 2012 .

[34]  Siba Sankar Mahapatra,et al.  Multi-criteria decision making towards selection of industrial robot: Exploration of PROMETHEE II method , 2015 .

[35]  Morteza Yazdani,et al.  New approach to select materials using MADM tools , 2018 .

[36]  Shawna Grosskopf,et al.  Data Envelopment Analysis (DEA): A Framework for Assessing Capacity in Fisheries When Data are Limited , 2001 .