Grey relational analysis for multi-response optimization of process parameters in green electrical discharge machining of Ti-6Al-4V alloy

Gugulothu, Bhiksha; Rao, G Sudhakara; Bezabih, Minyahil

doi:10.1016/j.matpr.2020.06.135

Cited by 18 publications

(16 citation statements)

References 11 publications

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“…59 Grey relational analysis was appropriate to resolve the complex interrelationships between multiple factors and research objects. 47 − 49 , 60 , 61 In our study, grey relational analysis was used to determine the complicated relationships between multiple parameters for the spontaneous combustion tendency of coal.…”

Section: Resultsmentioning

confidence: 99%

“…Grey system theory was initially proposed by Professor Deng in 1982 to solve situations where information was partly available and unavailable . Grey relational analysis was appropriate to resolve the complex interrelationships between multiple factors and research objects. − ,, In our study, grey relational analysis was used to determine the complicated relationships between multiple parameters for the spontaneous combustion tendency of coal.…”

Section: Resultsmentioning

confidence: 99%

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Study on the Spontaneous Combustion Tendency of Coal Based on Grey Relational and Multiple Regression Analysis

et al. 2021

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The correlation between the spontaneous combustion tendency of coal and its properties are of great importance for safety issues, environmental concerns, and economic problems. In this study, the relationship between multiple parameters, different from the previous single parameter, and the spontaneous combustion tendency was analyzed. The comprehensive judgment index (CJI), which indicates the tendency of coal spontaneous combustion, was obtained for samples collected from different mines. The CJI was measured by the cross-point temperature and had a negative correlation with the spontaneous combustion tendency. Physical pore structures and chemical functional groups were characterized based on cryogenic nitrogen adsorption and Fourier transform infrared spectroscopy measurements, respectively. For analyzing the effect of coal properties on the spontaneous combustion tendency, the grey relational grade was determined by the grey relational analysis between the CJI and the pore structures and functional groups of coal. The grey relational grade of the benzene substituent with CJI had a maximum of 0.8642, and the macropores had the minimum, 0.4169. The higher the gray relational grade was, the more relevant the spontaneous combustion tendency was, indicating that the benzene substituent was the most relevant. To better predict the spontaneous combustion tendency, the average pore diameter, hydroxyl, methyl, methylene, and benzene substituent with a high grey relational grade were selected. Finally, the multiple regression prediction model of CJI was established. The R squared coefficient, significance level, F -distribution, t -distribution, collinearity diagnosis, and residual distribution of the model met the requirements. In addition, two coal samples were selected to verify the spontaneous combustion tendency model. The relative errors between the predicted CJI value and the experimental CJI value were 1.42 and 4.25%, respectively. These small relative errors verified the reasonableness and validity of the prediction model.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Study on the Spontaneous Combustion Tendency of Coal Based on Grey Relational and Multiple Regression Analysis

et al. 2021

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“…Grey relational analysis consists of the basic steps found in multi-criteria (qualified) decision making methods [22,23]. These steps are described below, respectively.…”

Section: Grey Relational Analysismentioning

confidence: 99%

“…Grey relational analysis is a statistical approach used to eliminate uncertainty and obtain the most appropriate results when there are many results in one. Grey relational analysis has applications in machining [21,22]. The term "grey" in grey relational analysis refers to deficiency and/or uncertainty, but is often used in relation to the concept of knowledge.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Experimental and Statistical (Respond Surface Method and Grey Relational Analysis) of Surface Roughness, Vibration and Enerrgy consumption Values of Titanium Alloy During Machining

Akkuş

2021

Scientia Iranica

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This study aims to explain the interaction between the results measured in the turning operation. For this purpose, Ti 6Al-4V alloy workpiece was machined on CNC lathe. Surface roughness (Ra), vibration and energy consumption values were determined by turning. Experimental results were analyzed statistically.Response surface method (RSM) and grey relational analysis were used statistical analysis. In RSM analysis, regression equations, ANOVA, contour graphs, pertubation graphs, real and prediction graphs, % contribution graphs, most significant factor, optimum parameters, it is determined that the effective parameter in surface roughness, vibration and energy consumption is feed rate. Grey relational analysis steps and results are examined.

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“…Nguyen et al [ 14 ] used the Taguchi Data Envelopment Analysis based Ranking (DEAR)-based multi-criteria decision-making technique to obtain optimal EDM process parameters in machining silicon-based steel with a low-frequency vibration assisted EDM process. Bhiksha et al [ 15 ] have investigated the effect of graphite powder concentration in EDM machining on Ti-6Al-4V alloy. They have used Grey relational analysis for the multi-response optimization of EDM process parameters.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of the Process Parameters in Electric Discharge Machining of 316L Porous Stainless Steel Using Metaheuristic Techniques

et al. 2022

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Electric discharge machining is an essential modern manufacturing process employed to machine porous sintered metals. The sintered 316L porous stainless steel (PSS) components are widely used in diverse engineering domains, as interconnected pores are present. The PSS material has excellent lightweight and damping properties and superior mechanical and metallurgical properties. However, conventional machining techniques are not suitable for porous metals machining. Such techniques tend to block the micro-pores, resulting in a decrease in porous materials’ breathability. Thus, the EDM process is an effective technique for porous metal machining. The input process parameters selected in this study are peak current (Ip), pulse on time (Ton), voltage (V), flushing pressure (fp), and porosity. The response parameters selected are material removal rate (MRR) and tool wear rate (TWR). The present work aims to obtain optimum machining process parameters in the EDM of porous sintered SS316L using two meta-heuristic optimization techniques, i.e., Teaching Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithms, to maximize the MRR and minimize the TWR values. In the case of PSS having a 12.60% porosity value, PSO and TLBO algorithms give same optimum machining parameters. However, for PSS having an 18.85% porosity value, the PSO algorithm improves by about 5.25% in MRR and by 5.63% in TWR over the TLBO. In the case of PSS having a 31.11% porosity value, the PSO algorithm improves about 3.73% in MRR and 6.46% in TWR over the TLBO. The PSO algorithm is found to be consistent and to converge more quickly, taking minimal computational time and effort compared to the TLBO algorithm. The present study’s findings contribute valuable information in regulating the EDM performance in machining porous SS316L.

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Grey relational analysis for multi-response optimization of process parameters in green electrical discharge machining of Ti-6Al-4V alloy

Cited by 18 publications

References 11 publications

Study on the Spontaneous Combustion Tendency of Coal Based on Grey Relational and Multiple Regression Analysis

Study on the Spontaneous Combustion Tendency of Coal Based on Grey Relational and Multiple Regression Analysis

Investigation of Experimental and Statistical (Respond Surface Method and Grey Relational Analysis) of Surface Roughness, Vibration and Enerrgy consumption Values of Titanium Alloy During Machining

Multi-Objective Optimization of the Process Parameters in Electric Discharge Machining of 316L Porous Stainless Steel Using Metaheuristic Techniques

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