Fuzzy Modelling and Parametric Analysis of the Ring Spinning Process

Chakraborty, Shankar; Das, Partha Protim

doi:10.7216/1300759920192611403

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…Barzani et al [19] applied fuzzy-based modeling for prediction of surface roughness in CNC turning of Al-Si-Cu-Fe die casting alloy. Chakraborty and Das [20] employed fuzzy modeling for prediction and optimization of various yarn characterisitcs in ring spinning of cot-ton fibres. From the extensive review of the past literature, it can be noticed that a large number of researchers have already adopted various mathematical techniques to solve complex problems related to modeling of diverse machining processes while correlating different input parameters with the achievable responses.…”

Section: Introductionmentioning

confidence: 99%

Management and Production Engineering Review

Chakraborty,

Das

2019

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The application of artificial intelligence (AI) in modeling of various machining processes has been the topic of immense interest among the researchers since several years. In this direction, the principle of fuzzy logic, a paradigm of AI technique, is effectively being utilized to predict various performance measures (responses) and control the parametric settings of those machining processes. This paper presents the application of fuzzy logic to model two non-traditional machining (NTM) processes, i.e. electrical discharge machining (EDM) and electrochemical machining (ECM) processes, while identifying the relationships present between the process parameters and the measured responses. Moreover, the interaction plots which are developed based on the past experimental observations depict the effects of changing values of different process parameters on the measured responses. The predicted response values derived from the developed models are observed to be in close agreement with those as investigated during the past experimental runs. The interaction plots also play significant roles in identifying the optimal parametric combinations so as to achieve the desired responses for the considered NTM processes.

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Section: Introductionmentioning

confidence: 99%