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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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