Comparative analysis of multi criteria decision making techniques for material selection of brake booster valve body

Abstract Braking system is one of the most critical parts in a vehicle, and one of the main components of this system is brake booster valve body which plays a significant role in safety standards of the vehicle. The working conditions under the hood of the vehicle necessitate the valve body to have some special properties. This component should have high mechanical strength and the ability to maintain this strength at elevated temperatures. On the other hand, lower weight and lower cost are always desirable in industries. The material selection for the valve body is very important and should satisfy the aforementioned requirements. In order to select the best material for a large number of alternatives considering many different criteria, multi criteria decision making (MCDM) methods are used in this study. The weighting of criteria is carried out by entropy and analytic hierarchy process (AHP) methods and a combination of these two techniques are used as the final weights. MOORA, TOPSIS and VIKOR methods are used in this paper for selecting the best material for braking booster valve body. The alternative materials were ranked using these methods and the results of the analysis were compared using Spearman's rank correlation. PET-gf35 (PET reinforced with 35 wt% glass fiber) was found to be the best material for the valve body. Owing to the desirability of the results, the results of this study can be used in automotive industry in order to enhance the material selection process.

[1]  Jung‐il Song,et al.  Optimization of plastic injection molding process parameters for manufacturing a brake booster valve body , 2014 .

[2]  George F. List,et al.  Multiobjective optimization of a plastic injection molding process , 1994, IEEE Trans. Control. Syst. Technol..

[3]  S. Sapuan,et al.  Hybrid natural and glass fibers reinforced polymer composites material selection using Analytical Hierarchy Process for automotive brake lever design , 2013 .

[4]  Halil Çalışkan,et al.  Material selection for the tool holder working under hard milling conditions using different multi criteria decision making methods , 2013 .

[5]  S. Chakraborty,et al.  Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection , 2012 .

[6]  M. Tsai,et al.  Effects of material compositions on the mechanical properties of wood-plastic composites manufactured by injection molding , 2009 .

[7]  Evangelos Triantaphyllou,et al.  USING THE ANALYTIC HIERARCHY PROCESS FOR DECISION MAKING IN ENGINEERING APPLICATIONS: SOME CHALLENGES , 1995 .

[8]  S. M. Sapuan,et al.  Material screening and choosing methods: A review , 2010 .

[9]  J. R. San Cristóbal,et al.  Multi-criteria decision-making in the selection of a renewable energy project in Spain: the VIKOR method. , 2011 .

[10]  King Jet Tseng,et al.  Multi-objective material selection for wind turbine blade and tower: Ashby’s approach , 2012 .

[11]  Ali Shanian,et al.  TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell , 2006 .

[12]  Cheng-Wei Lin,et al.  Multi-criteria analysis of alternative-fuel buses for public transportation , 2005 .

[13]  F. Erzincanli,et al.  Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm , 2006 .

[14]  Edmundas Kazimieras Zavadskas,et al.  The MOORA method and its application to privatization in a transition economy , 2006 .

[15]  Ching-Lai Hwang,et al.  Multiple attribute decision making : an introduction , 1995 .

[16]  Jens Hainmueller,et al.  Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies , 2012, Political Analysis.

[17]  Jaksan D. Patel,et al.  Application of AHP/MOORA Method to Select Wire Cut Electrical Discharge Machining Process Parameter to Cut EN31 Alloys Steel with Brasswire☆ , 2015 .

[18]  Kang Tai,et al.  An evolutionary approach for cooling system optimization in plastic injection moulding , 2004 .

[19]  Kevin Alam,et al.  A robust optimization of injection molding runner balancing , 2005, Comput. Chem. Eng..

[20]  Y.-M. Deng,et al.  Supporting design decision-making when applying materials in combination , 2007 .

[21]  Thomas L. Saaty,et al.  DECISION MAKING WITH THE ANALYTIC HIERARCHY PROCESS , 2008 .

[22]  Mahmoud M. Farag,et al.  Materials selection for engineering design , 1997 .

[23]  R. Venkata Rao,et al.  Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods , 2013 .

[24]  Mahmoud M. Farag,et al.  Quantitative methods of materials substitution: Application to automotive components , 2008 .

[25]  Vahid Modanloo,et al.  Application of multi criteria decision making methods to select sheet hydroforming process parameters , 2016 .

[26]  H. S. Byun,et al.  A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method , 2005 .

[27]  M. Brunelli.  Introduction to the Analytic Hierarchy Process , 2014 .

[28]  S. M. Sapuan,et al.  A comprehensive VIKOR method for material selection , 2011, Materials & Design.

[29]  Greg Parker,et al.  Encyclopedia of Materials: Science and Technology , 2001 .

[30]  Rolf Sandström,et al.  The assessment and evaluation of property data for materials selection purposes , 1986 .

[31]  L. Myers,et al.  Spearman Correlation Coefficients, Differences between , 2004 .