“…After normalization, the data undergoes sequential grey relational coefficient (GRC) computation. (19) The GRC equation is…”
Section: Taguchi Theory and Gramentioning
confidence: 99%
“…Thankachan et al (18) used the Taguchi method, GRA, and an ANN to predict and optimize the surface roughness of products made of aluminum alloys and the material removal rate. Tamiloli et al (19) performed GRA using Taguchi factorial experiments and developed an adaptive neuro-fuzzy inference system (ANFIS) model to optimize parameter selection. This showed that GRA can use the signal-to-noise ratio (S/N) obtained from Taguchi experiments for relational analysis.…”
In the current precision industry, the rapid production of high-quality parts in bulk quantities has led to high competitiveness. In this study, the Taguchi method and grey relational analysis (GRA) approach were used in a practical investigation of precision lathe processing. The purpose was to find optimal parameters for single-target and multitarget cutting. The production of targets of the highest quality was the research focus, with the aim of strengthening the links between this study and the application to the processing industry. Precision, surface roughness, and material removal rate were selected as targets for improvement. The parameters commonly used for lathe processing were set as control factors, and cutting depth, spindle speed, feed rate, and material elongation were set as experimental factors. The results showed that in the cutting of materials, cutting precision was mainly affected by the depth of cut and spindle speed, surface roughness by spindle speed, and the material removal rate by the cutting depth. In a comparison of the quality loss for the same materials using previous parameters, the cutting precision has about 64 to 99% optimization, the surface roughness has 69 to 96% optimization, and the material removal rate has more than 90% optimization. GRA was also employed to analyze the sequences of parameters from the Taguchi experiments to obtain the target relationships and to find the various combinations of factors for improvement.
“…After normalization, the data undergoes sequential grey relational coefficient (GRC) computation. (19) The GRC equation is…”
Section: Taguchi Theory and Gramentioning
confidence: 99%
“…Thankachan et al (18) used the Taguchi method, GRA, and an ANN to predict and optimize the surface roughness of products made of aluminum alloys and the material removal rate. Tamiloli et al (19) performed GRA using Taguchi factorial experiments and developed an adaptive neuro-fuzzy inference system (ANFIS) model to optimize parameter selection. This showed that GRA can use the signal-to-noise ratio (S/N) obtained from Taguchi experiments for relational analysis.…”
In the current precision industry, the rapid production of high-quality parts in bulk quantities has led to high competitiveness. In this study, the Taguchi method and grey relational analysis (GRA) approach were used in a practical investigation of precision lathe processing. The purpose was to find optimal parameters for single-target and multitarget cutting. The production of targets of the highest quality was the research focus, with the aim of strengthening the links between this study and the application to the processing industry. Precision, surface roughness, and material removal rate were selected as targets for improvement. The parameters commonly used for lathe processing were set as control factors, and cutting depth, spindle speed, feed rate, and material elongation were set as experimental factors. The results showed that in the cutting of materials, cutting precision was mainly affected by the depth of cut and spindle speed, surface roughness by spindle speed, and the material removal rate by the cutting depth. In a comparison of the quality loss for the same materials using previous parameters, the cutting precision has about 64 to 99% optimization, the surface roughness has 69 to 96% optimization, and the material removal rate has more than 90% optimization. GRA was also employed to analyze the sequences of parameters from the Taguchi experiments to obtain the target relationships and to find the various combinations of factors for improvement.
“…Since the past few years, grey-fuzzy logic approach has been successfully implemented by many researchers to solve complex multi-response optimization problems. 29–33 Figure 6.Proposed grey-fuzzy logic approach.…”
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.
“…Though a significant amount of work has been carried out on machining of superalloys with MCDM approaches in EDM and Wire-EDM, optimization analysis related to its machining efficiency with different electrodes and dielectric has not been adequately explored. Inconel-718, one of the toughest materials is a Ni-based superalloy with a chemical composition of C0.8%, Mn0.35%, Ni54%, Cr20%, Ti0.75%, and Fe [3,4]. It is extensively used in making gas turbine blades, jet engine parts, ballistic missiles and automotive applications.…”
In the present work, multi-response optimization of electro-discharge machining (EDM) process is carried out based on an experimental analysis of machining superalloy Inconel-718. The study aims at optimizing and determining an optimal set of process variables, namely discharge current (), pulse-on duration () and dielectric fluid-pressure () for achieving optimal machining performance in EDM. Nine independent experiments based on L9 orthogonal array are carried out by using tungsten as the electrode. The productivity performance of the EDM process is measured in terms of material removal rate (MRR) and its cost parameter is measured in terms of tool wear rate (TWR) and electrode wear rate (EWR). The TOPSIS is used in conjunction with five different criterion weight allocation strategies— (namely, mean weight (MW), standard deviation (SDV), entropy, analytic hierarchy process (AHP) and Fuzzy). While MW, SDV and entropy are based on the objective evaluation of the decision-maker (DM), the AHP can model the DM’s subjective evaluation. On the other hand, the uncertainty in the DM’s evaluation is analyzed by using the fuzzy weighing approach.
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