Saadat Ali Rizvi, Ali Wajahat ,
Volume 30, Issue 3 (IJIEPR 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 %.
Saadat Ali Rizvi, Wajahat Ali,
Volume 32, Issue 3 (IJIEPR 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.
