Non-traditional machining processes selection using data envelopment analysis (DEA)

Sadhu, Abhijit; Chakraborty, Shankar

doi:10.1016/j.eswa.2011.01.088

Cited by 34 publications

(16 citation statements)

References 13 publications

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“…Das Chakladar et al [11] applied a digraph-based decision-making approach to select the most appropriate NTMPs for some real time manufacturing applications. Sadhu and Chakraborty [12] applied a two-phase decision-making model, based on data envelopment analysis (DEA) and weighted overall efficiency ranking method, to select and rank feasible NTMPs for some certain shape characteristics and work material combinations. Das and Chakraborty [13] developed an analytic network process (ANP)-based graphical user interface model to select the most appropriate NTMPs, captivating interdependency and feedback relationships among different criteria, affecting the NTMP selection decision.…”

Section: Review Of Literature On Ntmp Selectionmentioning

confidence: 99%

A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method

Chatterjee

Mondal²,

Boral

et al. 2017

FU Mech Eng

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Section: Review Of Literature On Ntmp Selectionmentioning

confidence: 99%

A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method

Chatterjee

Mondal²,

Boral

et al. 2017

FU Mech Eng

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“…A fuzzy and crisp AHP-based decision support system was adopted by Tozan [11] for selecting the appropriate AWJ machining technology to have better SF for cutting operation of titanium. Sadhu and Chakraborty [12] applied a two-phase decision tool for NTMP selection. In the first phase, using data envelopment analysis (DEA) approach, the most efficient NTMPs were shortlisted for a given shape feature and work material combination.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of fuzzy axiomatic design principles for selection of non-traditional machining processes

Khandekar

Chakraborty

2015

Int J Adv Manuf Technol

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Since the last few decades, there have been tremendous technological advancements in communication, aeronautics, automobiles, textile engineering, nuclear energy, medical sciences and die-making industries. These have necessitated the use of some totally new and hitherto unknown high-strength temperature-resistant, tough and difficult-tomachine materials and, consequently, some newer unconventional processes for their efficient machining. It has been well established that non-traditional machining processes (NTMPs) far surpass their traditional counterparts in machining such advanced materials with respect to tolerance, surface finish, accuracy, complexity and miniatureness of the machined product/part. These NTMPs are also found to be more effective and economical. Choosing the most appropriate NTMP for generation of a desired shape feature on a given work material involves consideration of numerous conflicting qualitative and quantitative criteria. This paper proposes the application of fuzzy axiomatic design principles for selection of the most suitable NTMPs for generating cavities on ceramics and micro-holes on hardened tool steel and titanium materials, based on their practical/industrial importance. For micro-drilling operation on hardened tool steel, electrical discharge machining is found to be the best process followed by abrasive jet machining and ultrasonic machining. On the other hand, for generation of micro-holes on titanium, electrochemical machining is the most suitable process. Abrasive jet machining emerges out as the most efficient process for generating blind cavities on ceramics. These results are well in accordance with the expected machining practices and perfectly match with the decisions of the machining professionals.

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“…The process of transforming attributes value into a range of [0,1] is called normalization and it is required in MADM methods to transform performance rating with different data measurement unit in a decision matrix into a compatible unit [19][20][21]. The normalization method adopted from the paper [22], and the formulas are given as follows: Step 5.…”

Section: Group Eigenvalue Methodsmentioning

confidence: 99%

A Novel Machine Selection Method Combining Group Eigenvalue Method with TOPSIS Method

Yang¹,

Xiong³

2016

IJFGCN

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Non-traditional machining processes selection using data envelopment analysis (DEA)

Cited by 34 publications

References 13 publications

A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method

A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method

Application of fuzzy axiomatic design principles for selection of non-traditional machining processes

A Novel Machine Selection Method Combining Group Eigenvalue Method with TOPSIS Method

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