Integration of Grey-Based Taguchi Technique for the Optimization of Process Parameters During the Turning Operation of 16MnCr5 Steel

CNC turning is widely used as a manufacturing process through which extra unwanted material is turned to get a high degree of surface roughness. In this research article, Taguchi technique was coupled with Grey Relational Analysis (GRA) to optimize the turning variables, or turning parameters, for the simultaneous improvement of productivity, root mean square roughness (Rq), and average surface roughness (Ra). Taguchi technique L27 (34) orthogonal array was used in this experimental work. Depth of cut, feed, and speed were considered as the controllable process parameters. Root mean square roughness (Rq), average surface roughness (Ra), and material removal rate (MRR) were considered as the performance characteristics; according to TGRA results, the optimum combination in this study found A1B1C1 (Vc=400 rpm, f=0.06 mm/rev, and DOC=0.5 mm). The optimum values of Ra and MRR for this study were 6.86 μm and 20690.31 mm3/s, respectively. Further, ANOVA was applied, and showed that depth of cut (DOC) had the most significant effect and followed in line by speed and feed for multi-response optimization. According to the results of Analysis of Variance (ANOA), the %age contribution of DOC (depth of cut), speed, and feed were 38.71 %, 11.89%, and 8.466%, respectively.

Keywords:

Anova , surface roughness , MRR , grey relational analysis , Taguchi technique