期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2017
卷号：6
期号：3
页码：4690
DOI：10.15680/IJIRSET.2017.0603234
出版社：S&S Publications
摘要：The increase of customer needs for quality in metal cutting has driven the metal cutting industry tocontinuously improve quality of metal cutting processes. Within these metals cutting processes, the CNC turningprocess in one of the most fundamental metal removal operations used in the modern manufacturing industries. It is animportant parameter in the highly Automated Manufacturing engineering industries which has significant influence onthe performance of mechanical parts and products. This work focuses on developing an empirical model for theprediction of surface roughness in CNC turning. The following machining parameters are considered for deriving thenew model: Tool Material, Work Material, Feed, Spindle speed and Depth of cut. The existing methods used forpredicting the surface roughness value are only data mining techniques, computational neural network techniques andapplication of Taguchi techniques. This paper presents a new algorithm to establish a statistical model forpredicting the surface roughness value, which is a simple monogram like procedure
关键词：Tool Material; Work Material; Feed; Spindle speed and Depth of cut.
