A Grey Fuzzy Logic Approach for Cotton Fibre Selection

Chakraborty, Shankar; Das, Partha Protim; Kumar, Vidyapati

doi:10.1007/s40034-017-0099-7

Cited by 19 publications

(10 citation statements)

References 15 publications

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“…The location of each triangle in the rule viewer represents the determined fuzzy subsets for each input and output, and the height of the darkened area in each triangle symbolizes the fuzzy membership value for that fuzzy subset. 34 From Figure 10, it can be revealed that in case of normal grinding wheel, when the input GRC values for Ra, V , and G-ratio are 1, 1, and 0.3421 respectively, the corresponding defuzzified value of GFRG is 0.767. Similarly, in Figure 11, for rubber tube-pasted grinding wheel, the input GRC values for Ra, V , and G-ratio as 0.556, 1 and 0.406 respectively, generate the corresponding GFRG value as 0.643.…”

Section: Resultsmentioning

confidence: 99%

Improvement in the performance with less stiff air layer formation around the rubber tube-pasted grinding wheel

Guha

Das

Chakraborty

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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In the grinding operation, a stiff layer of air gets formed around the periphery of the grinding wheel that causes deterioration of its performance. In the present work, in order to restrict the generation of stiff air layer around the periphery of the grinding wheel, a rubber tube is pasted on its surface to improve the grinding performance. An experimental investigation is carried out with low alloy steel as the work material. Taguchi's L9 orthogonal array is considered for the design of experiments while taking cutting speed, depth of cut, and type of the cutting fluid as the input grinding parameters. A comparative analysis using rubber tube-pasted grinding wheel and normal grinding wheel reveals that the developed wheel significantly improves the grinding performance with respect to surface roughness, amplitude of vibration and grinding ratio, as compared to the normal wheel. Moreover, grey relational analysis aided with fuzzy logic is applied in the experimental results to derive the optimal combination of process parameters for further enhancement of the grinding performance. Finally, analysis of variance results identify cutting speed as the most significant parameter while grinding with normal wheel, whereas depth of cut appears to be the most important parameter while machining with rubber tube-pasted grinding wheel.

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Section: Resultsmentioning

confidence: 99%

Improvement in the performance with less stiff air layer formation around the rubber tube-pasted grinding wheel

Guha

Das

Chakraborty

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…The computed GFRG results could then be ranked in decreasing order, the preferred option being the alternative having the greatest GFRG value, which reduces unpredictability and ambiguity in the experimentally obtained data. The combination of the GRA methodology and fuzzy logic has evidently been a simple and effective way to solve multi-variable problems [ 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. In this study, the grey-fuzzy method was utilized as a multi-criteria optimization procedure to evaluate the optimum intermix of input factors for the EDM process under consideration.…”

Section: Methodsmentioning

confidence: 99%

Experimental Investigation and Optimization of Electric Discharge Machining Process Parameters Using Grey-Fuzzy-Based Hybrid Techniques

et al. 2021

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Electrical discharge machining (EDM) has recently been shown to be one of the most successful unconventional machining methods for meeting the requirements of today’s manufacturing sector by producing complicated curved geometries in a broad variety of contemporary engineering materials. The machining efficiency of an EDM process during hexagonal hole formation on pearlitic Spheroidal Graphite (SG) iron 450/12 grade material was examined in this study utilizing peak current (I), pulse-on time (Ton), and inter-electrode gap (IEG) as input parameters. The responses, on the other hand, were the material removal rate (MRR) and overcut. During the experimental trials, the peak current ranged from 32 to 44 A, the pulse-on duration ranged from 30–120 s, and the inter-electrode gap ranged from 0.011 to 0.014 mm. Grey relational analysis (GRA) was interwoven with a fuzzy logic method to optimize the multi-objective technique that was explored in this EDM process. The effect of changing EDM process parameter values on responses was further investigated and statistically analyzed. Additionally, a response graph and response table were produced to determine the best parametric setting based on the calculated grey-fuzzy reasoning grade (GFRG). Furthermore, predictor regression models for response characteristics and GFRG were constructed, and a confirmation test was performed using randomly chosen input parameters to validate the generated models.

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“…In this rule viewer, the first three columns show the input process parameters and the remaining six columns express the output yarn characteristics. The location of each triangle in the rule viewer represents the fuzzy MF and the height of the darkened area denotes the fuzzy membership value for the corresponding fuzzy set [22]. From Figure 5, it can clearly be revealed that when spindle speed = 17000 rpm, yarn 2, it can be identified that at this process parameter combination, the six yarn characteristics have values as BR = 5.7, SS = 17.09 g/tex, IR = 8.31, BE = 3.53%, HI = 5.51 and IM = 122.…”

Section: Examplementioning

confidence: 99%