The present work deals with the comparison of four multi response optimization methods, viz. multiple response signal-to-noise (MRSN) ratio, weighted signal-to-noise (WSN) ratio, Grey relational analysis (GRA), and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian) methods taking a case study in turning mild steel specimen using HSS cutting tool. The various factors like cutting speed, feed rate, depth of cut and coolant flow rate are considered as the input process variables, while the material removal rate (MRR), surface roughness (SR) and specific energy consumption (SEC) are considered as various performance characteristics. One set of experimental data is analyzed using the standardized procedures. The optimization performances of these four methods are compared. The results show that MRSN ratio method proves to be the best optimization method. It is found that the feed rate has a highest impact on the overall performance as compared to other process parameters.
In the present research work, four different multi response optimization techniques, viz. multiple response signal-to-noise (MRSN) ratio, weighted signal-to-noise (WSN) ratio, Grey relational analysis (GRA) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian) methods have been used to optimize the electro-discharge machining (EDM) performance characteristics such as material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) simultaneously. Experiments have been planned on a D2 steel specimen based on L9 orthogonal array. Experimental results are analyzed using the standard procedure. The optimum level combinations of input process parameters such as voltage, current, pulse-on-time and pulse-off-time, and percentage contributions of each process parameter using ANOVA technique have been determined. Different correlations have been developed between the various input process parameters and output performance characteristics. Finally, the optimum performances of these four methods are compared and the results show that WSN ratio method is the best multiresponse optimization technique for this process. From the analysis, it is also found that the current has the maximum effect on the overall performance of EDM operation as compared to other process parameters.Growing Science Ltd. All rights reserved. 7
Wire electrical discharge machining (WEDM) is a popular non-conventional machining process used particularly for making extrusion dies, blanking punches, and tools especially requiring tight dimensional tolerances. Because of the process limitation, the rate of cutting and maintenance of close dimensional tolerance is a challenging task. Given the above facts, the present work has been focused on achieving the maximum possible cutting rate (VC) maintaining good dimensional accuracy and corner radius (RC). In the present research work, a multi-response optimization method (i.e. Taguchi based Utility approach) has been used to obtain an optimum set of input parameters such as pulse on time (TON), pulse off time (TOFF), servo voltage (SV), and wire feed rate (WF) resulting into a best overall cutting performance. Analysis of variance (ANOVA) is also used to find out the significant effect of each machining parameter on the cutting performance. The analysis reported in this paper will be helpful for industry personnel to select the best set of process parameters for achieving a good result without the use of any software or statistical analysis.
In this paper, an effective two stage multi-response optimization technique (i.e. grey relational analysis coupled with Taguchi technique) has been applied to achieve a better performance characteristic in wire-cut electrical discharge machining (WEDM) process. A zinc coated brass wire of 0.25 mm diameter was used as tool electrode for machining a D2 tool steel specimen. Experiments were planned according to Taguchi’s L9 orthogonal array under different cutting parameters such as: pulse on time (TON), pulse off time (TOFF), peak current (IP) and wire feed rate (WF). The three quality characteristics (i.e. performance characteristics), namely cutting rate, kerf width and surface roughness have been simultaneously optimized in two different stages. It wasobserved that the cutting speed is increased by 24.60% compared to first stage/primary optimization process. From the analysis of variance (ANOVA), pulse-on time is found to be the most influencing cutting parameter having 74.91% contribution towards overall performance of the WEDM process. Finally,a confirmatory experiment has been carried out at optimum set of cutting parameters obtained from the precision optimization stage to identify the effectiveness of this proposed method. It was observed that the predicted values of the responses obtained from regression models were in good agreement with the experimental findings.
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