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Showing 5 results for Mrr
EXPERIMENTAL ANALYSIS OF CNC TURNING OF NYLON USING TAGUCHI METHOD
Sujit Kumar Jha,
Volume 27, Issue 2 (6-2016)
Abstract

Manufacturing process frequently employs optimization of machining parameters in order to improve product quality as well as to enhance productivity. The material removal rate is a significant indicator of the productivity and cost efficiency of the process. Taguchi method has been implemented for assessing favorable (optimal) machining condition during the machining of nylon by considering three important cutting parameters like cutting speed, feed rate and depth of cut during machining on CNC. The objective of the paper is to find out, which process parameters having more impacts on material removal rate during turning operation on nylon using analysis of variance (ANOVA). An Orthogonal array has been constructed to find the optimal levels of the turning parameters and further signal-to-noise (S/N) ratio has been computed to construct the analysis of variance table. The results of ANOVA shown that feed rate has most significant factor on MRR compare to cutting speed and depth of cut for nylon. The confirmation experiments have conducted to validate the optimal cutting parameters and improvement of MRR from initial conditions is 555.56%.


Integration of grey-based Taguchi technique in optimization of parameters process during the turning operation of 16MnCr5 steel
Saadat Ali Rizvi, Ali Wajahat ,
Volume 30, Issue 3 (9-2019)
Abstract
CNC turning is widely used as a manufacturing process through which unwanted material is removed to get the high degree of surface rough. In this research article, Taguchi technique was coupled with grey relation analysis (GRA) to optimize the turning parameters for simultaneous improvement of productivity, average surface roughness (Ra), and root mean square roughness (Rq).Taguchi technique L27 (34) orthogonal array was used in this experimental work. Feed, speed, and depth of cut were considered as the controllable process parameters. average roughness (Ra), root mean square roughness (Rq),and material removal rate (MRR) were considered as the performance characteristic and from TGRA result, it was revealed that the optimum combinational parameters for multi-performance, based on mean response values and confirmation experiments with Taguchi-based GRA is A1B1C1 (Vc=400 rpm, f=0.06 mm/rev, and DOC=0.5 mm). The optimum values obtained from experimental investigations for Ra was 6.86 μm, and MRR was 20690.31 mm3/s,further analysis of variance(ANOVA) were applied and it was identified that the depth of cut having most significant effect followed by speed and feed for multiresponse optimization. The percentage contribution of depth of cut was 38.28.71 %, speed was 11.89 % and feed was 8.466 %.
CNC turning is widely used as a manufacturing process through which unwanted material is removed to get a high degree of surface roughness. In this research article, Taguchi technique was coupled with grey relation analysis (GRA) to optimize the turning parameters for simultaneous improvement of productivity, the average surface roughness (Ra), and root means square roughness (Rq). Taguchi technique L27 (34) orthogonal array was used in this experimental work. Feed, speed, and depth of cut were considered as the controllable process parameters. average roughness (Ra), root mean square roughness (Rq), and material removal rate (MRR) were considered as the performance characteristic and from TGRA result, it was revealed that the optimum combinational parameters for multi-performance, based on mean response values and confirmation experiments with Taguchi-based GRA is A1B1C1 (Vc=400 rpm, f=0.06 mm/rev, and DOC=0.5 mm). The optimum values obtained from experimental investigations for Ra was 6.86 μm, and MRR was 20690.31 mm3/s, further analysis of variance(ANOVA) were applied and it was identified that the depth of cut having most significant effect followed by speed and feed for multiresponse optimization. The percentage contribution of the depth of cut was 38.28.71 %, speed was 11.89 % and feed was 8.466 %.
Machining of 304 stainless steel Using Electrochemical Machining (ECM) Process: Response Surface Methodology Approach
Reza Rostami Heshmatabad, Mohammadreza Shabgard,
Volume 31, Issue 3 (9-2020)
Abstract
In this study, the electrochemical machining (ECM) of the 304 stainless steel with the response surface methodology (RSM) approach for designing, analyzing and mathematical modeling was used. The electrolyte type, concentration and current parameters were considered as the machining parameters. The mathematical model for the responses was presented and based on the type of electrolyte including NaCl, NaNO3 and KCl. The results showed that the current has the highest effect on Surface Roughness (SR) and Material Removal Rates (MRR) and respectively it improves them to 0.465μm and 0.425gr/min. The electrolyte concentration has the highest effect on Over Cut (OC) and causes to increase its values. Under the conditions of NaCl electrolyte, 1 molarity concentration and 55 A current, the optimum condition 0.4006 gr/min MRR, 0.75 mm OC and 0.465mm SR was achieved. 
Investigation on Abrasive Water Jet Machining of Al-Al2O3 MMC
Bhanudas Bachchhav,
Volume 32, Issue 1 (1-2021)
Abstract
The present work aims to investigate Abrasive Water Jet Machining parameters for machining of Al-Al2O3 Metal Matrix Composite. Plan of experiments, based on Taguchi’s analysis technique were performed using L9 orthogonal array. A correlation was established between concentration of Al2O3, Stand-off distance, pressure and Transverse feed with Metal Removal Rate, Surface Roughness, Over-cut and Taper angle by regression analysis. On the basis of experimental results and S/N ratio analysis, ranking of the parameters has been done. The analysis of variance (ANOVA) has been used to find out the impact of individual parameters on response parameters. Al2O3 concentration plays a very significant role in determination of MRR and surface roughness. Also overcut is largely influenced by stand off distance. Furthermore,  multi-objective optimization can be carried out using advanced optimization techniques.  This work helped to generate technical database for industrial applications of MMC.
An artificial neural network approach to prediction of surface roughness and material removal rate in CNC turning of C40 steel
Saadat Ali Rizvi, Wajahat Ali,
Volume 32, Issue 3 (9-2021)
Abstract
The present study is focused to investigate the effect of the various machining input parameters such as cutting speed (vc), feed rate (f), depth of cut, and nose radius (r) on output i.e. surface roughness (Ra and Rq) and metal removal rate (MRR) of the C40 steel by application of an artificial neural network (ANN) method.  ANN is a soft computing tool, widely used to predict, optimize the process parameters. In the ANN tool, with the help of MATLAB, the training of the neural networks has been done to gain the optimum solution. A model was established between the computer numerical control (CNC) turning parameters and experimentally obtained data using ANN and it was observed from the result that the predicted data and measured data are moderately closer, which reveals that the developed model can be successfully applied to predict the surface roughness and material removal rate (MRR) in the turning operation of a C40 steel bar and it was also observed that lower the value of surface roughness (Ra and Rq) is achieved at the cutting speed of 800 rpm with a feed rate of 0.1 mm/rev, a depth of cut of 2 mm and a nose radius of 0.4 mm.
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