Surface roughness modeling using RSM for HSLA steel by coated carbide tools

Abstract

Surface roughness is the main indicator of surface quality on machined parts. Accurate predictive models for surface roughness help to choose optimum machining parameters, ultimately support to maximize the productivity without any compromise on quality. In this paper, an average surface roughness (R a) model has been developed for turning high-strength low-alloy steel (AISI 4340 with carbon contents less than 0.3 %) using multilayer coated carbide tools. A series of tests using response surface methodology (RSM) has been employed to develop a relationship between R a and machining parameters (feed, speed, and depth of cut). The feed rate has been observed as the main parameter that influences surface roughness. Contour plots of “feed versus speed” and “feed versus depth of cut” signify that target R a value can be achieved through optimal combination of cutting parameters. The accuracy of proposed model has been confirmed through validation data with average prediction error of 3.38 %.