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Material selection in engineering design based on nearest neighbor search under uncertainty: a spatial approach by harmonizing the Euclidean and Taxicab geometry

Published online by Cambridge University Press:  02 October 2018

Debasis Das Open the ORCID record for Debasis Das [Opens in a new window] ,
Somnath Bhattacharya  and
Bijan Sarkar
Debasis Das*
Affiliation:
Department of Mechanical Engineering, Neotia Institute of Technology, Management and Science, Kolkata, India
Somnath Bhattacharya
Affiliation:
Department of Mechanical Engineering, Jadavpur University, Kolkata, India
Bijan Sarkar
Affiliation:
Department of Production Engineering, Jadavpur University, Kolkata, India
*
Author for correspondence: Debasis Das, E-mail: [email protected]
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Abstract

Material selection is a fundamental step in mechanical design that has to meet all the functional requirements of the component. Multiple-attributed decision-making (MADM) processes are already well established to choose the preeminent alternative from the finite set of alternatives, but there is some lack of geometrical evidence if the alternatives are considered as multi-dimensional points. In this paper, a fresh spatial approach is proposed based on nearest neighbor search (NNS) in which the nearness parameter is considered as a Manhattan norm (Taxicab geometry) in turn which is a function of the Euclidean norm and cosine similarity to raise a preeminent alternative under the MADM framework. Cryogenic storage tank and flywheel are considered as two case studies to check the validity of the proposed spatial approach based on NNS in material selection. The result shows the right choice for the cryogenic storage tank is the austenitic steel (SS 301 FH), and for the flywheel, it is a composite material (Kevler 49-epoxy FRP) those are consistent with the real-world practice and the results are compared with other MADM methods of previous works.

Keywords

Cosine similarityManhattan distancematerial selectionmulti-attributed decision-makingnearest neighbor search
Type
Research Article
Information
AI EDAM , Volume 33 , Issue 3 , August 2019 , pp. 238 - 246
Copyright
Copyright © Cambridge University Press 2018 

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References

Arkhangel'skii, AV and Fedorchuk, VV (1990) The basic concepts and constructions of general topology. In Gamkrelidze, RV (ed.), Encyclopaedia of Mathematical Sciences, vol. 17. pp. 5–18. Berlin: Springer-Verlag.Google Scholar
Ashby, MF (1999) Materials Selection in Mechanical Design, 2nd Edn. Oxford: Butterworth Heinemann.Google Scholar
Barrena, M, Jurado, E, Neila, PM and Pachon, C (2010) A flexible framework to ease nearest neighbor search in multidimensional data spaces. Data & Knowledge Engineering 69, 116–136.Google Scholar
Broek, D (1984) Elementary Engineering Fracture Mechanics. The Hague: Martinus Nijhoff Publishers.Google Scholar
Cables, E, Lamata, MT and Verdegay, JL (2016) RIM-reference ideal method in multicriteria decision making. Information Sciences 337–338, 1–10.Google Scholar
Chan, FTS, Kumar, N, Tiwari, MK, Lau, HCW and Choy, KL (2008) Global supplier selection: a fuzzy-AHP approach. International Journal of Production Research 46, 3825–3857.Google Scholar
Chatterjee, P and Chakraborty, S (2012) Material selection using preferential ranking method. Materials & Design 35, 384–393.Google Scholar
Chatterjee, P, Athawale, VM and Chakraborty, S (2009) Selection of materials using compromise ranking and outranking methods. Materials & Design 30, 4043–4053.Google Scholar
Das, D, Bhattacharya, S and Sarkar, B (2016) Decision-based design-driven material selection: a normative-prescriptive approach for simultaneous selection of material and geometric variables in gear design. Materials & Design 92, 787–793.Google Scholar
Dehghan-Manshadi, B, Mahmudi, H and Abedian, A (2007) A novel method for materials selection in mechanical design: combination of non-linear normalization and a modified digital logic method. Materials & Design 28, 8–15.Google Scholar
Dieter, GE (1983). Engineering Design: A Materials and Processing Approach. Boston: McGraw-Hill.Google Scholar
Farag, MM (2014) Materials and Process Selection for Engineering Design. Boca Raton: CRC Press.Google Scholar
Fayazbakhsh, K, Abedian, A, Manshadi, BD and Khabbaz, RS (2009) Introducing a novel method for materials selection in mechanical design using Z-transformation in statistics for normalization of material properties. Materials & Design 30, 4396–4404.Google Scholar
Flynn, TM (2005) Cryogenic Engineering. New York: Marcel Dekker.Google Scholar
Genta, G (1985) Kinetic Energy Storage: Theory and Practice of Advanced Flywheel System. London: Butterworths.Google Scholar
Girubha, RJ and Vinodh, S (2012) Application of fuzzy VIKOR and environmental impact analysis for material selection of an automotive component. Materials & Design 37, 478–486.Google Scholar
Godula-Jopek, A, Jehle, W and Wellnitz, J (2012) Hydrogen Storage Technologies: New Materials, Transport, and Infrastructure. Boschstr: Wiley-VCH.Google Scholar
Hafezalkotob, A, Hafezalkotob, A and Sayadi, MK (2016) Extension of MULTIMOORA method with interval numbers: an application in materials selection. Applied Mathematical Modelling 40, 1372–1386.Google Scholar
Hatush, Z and Skitmore, MR (1998) Contractor selection using multicriteria utility theory: an additive model. Building and Environment 33, 105–115.Google Scholar
Hazelrigg, GA (2003) A framework for decision-based engineering design. Journal of Mechanical Design 120, 653–658.Google Scholar
Hu, YJ, Wang, Y, Wang, ZL, Wang, YQ and Zhang, BC (2014) Machining scheme selection based on a new discrete particle swarm optimization and analytical hierarchy process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, 71–82.Google Scholar
Huang, W (2002). On the selection of shape memory alloys for actuators. Materials & Design 23, 11–19.Google Scholar
Jahan, A, Bahraminasab, M and Edwards, KL (2012a) A target-based normalization technique for material selection. Materials & Design 35, 647–654.Google Scholar
Jahan, A, Mustafa, F, Sapuan, SM, Ismail, MY and Bahraminasab, M (2012b) A framework for weighting of criteria in ranking stage of material selection. International Journal of Advance Manufacturing Technology 58, 411–420.Google Scholar
Jee, DH and Kang, KJ (2000) A method for optimal material selection aided with decision making theory. Materials & Design 21, 199–206.Google Scholar
Karande, P and Chakraborty, S (2012) Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Materials & Design 37, 317–324.Google Scholar
Khabbaz, RS, Manshadi, BD, Abedian, A and Mahmudi, R (2009) A simplified fuzzy logic approach for materials selection in mechanical engineering design. Materials & Design 30, 687–697.Google Scholar
Kou, G and Lin, C (2014) A cosine maximization method for the priority vector derivation in AHP. European Journal of Operational Research 235, 225–232.Google Scholar
Krause, EF (1986) Taxicab Geometry: An Adventure in Non-Euclidean Geometry. New York: Dover Publications.Google Scholar
Likert, R (1932) A technique for the measurement of attitudes. In Woodworth, RS (ed.), Archives of Psychology. vol. 140, pp. 1–55. New York: The Science Press.Google Scholar
Lourenzutti, R and Krohling, RA (2014) The Hellinger distance in multicriteria decision making: an illustration to the TOPSIS and TODIM methods. Expert Systems with Applications 41, 4414–4421.Google Scholar
Milani, AS, Shanian, A, Madoliat, R and Nemes, JA (2005) The effect of normalization norms in multiple attribute decision making methods: a case study in gear material selection. Structural and Multidisciplinary Optimization 29, 312–318.Google Scholar
Moghtadaiee, V and Dempster, AG (2015) Determining the best vector distance measure for use in location fingerprinting. Pervasive and Mobile Computing 23, 59–79.Google Scholar
Mousavi-Nasab, SH and Sotoudeh-Anvari, A (2017) A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design 121, 237–253.Google Scholar
Mujgan, S, Ozdemir, MS and Gasimov, RN (2004) The analytic hierarchy process and multi objective 0–1 faculty course assignment. European Journal of Operational Research 157, 398–408.Google Scholar
Opricovic, S and Gwo-Hshiung, T (2004). Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research 156, 445–455.Google Scholar
Otto, K and Wood, K (2001) Product Design: Techniques in Reverse Engineering and New Product Development. India: Pearson Education.Google Scholar
Papadopoulos, AN and Manolopoulos, Y (2005) Nearest Neighbor Search: A Database Perspective. New York: Springer.Google Scholar
Peng, A and Xiao, K (2013) Material selection using PROMETHEE combined with analytic network process under hybrid environment. Materials & Design 30, 643–652.Google Scholar
Pfeifer, M (2009) Materials Enabled Designs, the Materials Engineering Perspective to Product Design and Manufacturing. Burlington: Butterworth-Heinemann.Google Scholar
Rao, RV (2008) A decision making methodology for material selection using an improved compromise ranking method. Materials & Design 29, 1949–1954.Google Scholar
Rao, RV and Patel, BK (2010) A subjective and objective integrated multiple attribute decision making method for material selection. Materials & Design 31, 4738–4747.Google Scholar
Shahinur, S, Sharif Ullah, AMM, Noor-E-Alam, M, Haniu, H and Kubo, A (2017) A decision model for making decisions under epistemic uncertainty and its application to select materials. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, 298–312.Google Scholar
Shanian, A and Savadogo, O (2006) TOPSIS multiple criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell. Journal of Power Sources 159, 1095–1104.Google Scholar
Shanian, A, Milani, AS, Carson, C and Abeyaratne, RC (2008) A new application of ELECTRE III and revised Simos’ procedure for group material selection under weighting uncertainty. Knowledge-Based Systems 21, 709–720.Google Scholar
Simon, HA (1988) Rationality as process and as product of thought. In Bell, D, Raiffa, H and Taversky, A. (ed.), Decision Making. pp. 58–77. Cambridge: Cambridge University Press.Google Scholar
Suh, NP (1990) The Principles of Design. New York: Oxford University Press.Google Scholar
Wallenius, J, Dyer, JS, Fishburn, PC, Steuer, RE, Zionts, S and Deb, K (2008) Multiple criteria decision making, multi-attribute utility theory. Management Science 54, 1336–1349.Google Scholar
Wassenaar, HJ and Chen, W (2003) An approach to decision-based design with discrete choice analysis for demand modeling. Journal of Mechanical Design 125, 490–497.Google Scholar
Xia, P, Zhang, L and Li, F (2015) Learning similarity with cosine similarity ensemble. Information Sciences 307, 39–52.Google Scholar
Zha, XF, Sriram, RD and Lu, WF (2004) Evaluation and selection in product design for mass customization: a knowledge decision support approach. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, 87–109.Google Scholar