Sensitivity Analysis Technique for Fuzzy TOPSIS using Improvised Sensitive-Simple Additive Weighting Method

doi:10.32802/asmscj.2020.sm26(4.15)

Cited by 1 publication

(2 citation statements)

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“…This can help to identify the most critical fuzzy ratings and ensure that they are selected appropriately. Hence, it needs to be done for analyzing the influence of design criteria weights suggested by decision-makers for the verification of quality in the decision-making process, robustness and its implementation (Mohamad and Ibrahim, 2020); The ranking of design alternatives should ultimately be determined by their closeness coefficient, which can then be used to arrange the alternatives in descending order of value. This order can serve as prioritization of DfAM steps to be incorporated into the current industrial component, ensuring successful implementation; The redesign of the industrial component should be done iteratively, taking into account various DfAM design rules, capabilities of the AM machine and results from finite element analysis (FEA) validation; and The final product produced through AM process will be compared with initial and modified redesign.…”

Section: Proposed Methodologymentioning

confidence: 99%

Section: Fuzzy Topsis Implementationmentioning

confidence: 99%

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Prioritization and deployment of design for additive manufacturing strategies to an automotive component

R.,

2023

RPJ

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Purpose This paper aims to propose a new framework on prioritizing and deployment of design for additive manufacturing (DfAM) strategies to an industrial component using Fuzzy TOPSIS multiple criteria decision-making (MCDM) techniques. The proposed framework is then applied to an automotive component, and the results are discussed and compared with existing design. Design/methodology/approach Eight DfAM design alternatives associated with eight design criteria have been identified for framing new DfAM strategies. The prioritization order of the design alternatives is identified by Fuzzy TOPSIS MCDM technique through its closeness coefficient. Based on Fuzzy TOPSIS MCDM output, each of the design alternatives is applied sequentially to an automobile component as a case study. Redesign is carried out at each stage of DfAM implementation without affecting the functionality. Findings On successful implementation of proposed framework to an automotive component, the mass is reduced by 43.84%, from 0.429 kg to 0.241 kg. The redesign is validated by finite element analysis, where von Mises stress is less than the yield stress of the material. Practical implications The proposed DfAM framework and strategies will be useful to designers, R&D engineers, industrial practitioners, experts and consultants for implementing DfAM strategies on any industrial component without impacting its functionality. Originality/value To the best of the authors’ knowledge, the idea of prioritization and implementation of DfAM strategies to an automotive component is the original contribution.

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