Hard turning multi-performance optimization for improving the surface integrity of 300M ultra-high strength steel

Abstract

Hard turning technology was proven effective in enhancing the surface finish and dimensional accuracy of hardened steel. For this reason, it is used for finishing operations in the manufacturing of some critical aircraft landing gear components where bulk material hardness exceeds 45 HRC such as 300M ultra-high-strength steel. Nonetheless, the selection of cutting conditions remains critical to ensure generated surface characteristics meet the performance requirements. In this context, this study focuses on the optimization of hard turning cutting conditions to reduce surface roughness, increase compressive surface residual stresses, and reduce surface white layer thickness of 300M ultra-high-strength steel (55 HRC). To this end, a design of experiments (DoE) is used to investigate the effects of cutting tool (T), cutting speed (V), feed rate (f), and depth of cut (D) during hard turning. The cutting tool materials investigated include physical vapor deposited AlTiN–coated cemented carbide, mixed ceramic, and polycrystalline cubic boron nitride (PCBN), all commonly used in hard turning. Three different optimization approaches are evaluated and compared, namely the single-response Taguchi method, the multi-response grey relational analysis (GRA), and the proportion quality loss reduction (PQLR) methodologies. Because responses were found to be correlated, the selected multi-response optimization approaches were combined with the principal component analysis (PCA) method to transform correlated parameters into uncorrelated ones. The effect of cutting parameters and their interactions on surface roughness (Ra) and white layer thickness (WL) were considered in detail, particularly the effect of cutting tool material. Results demonstrate that the PCA-based GRA approach is a very suitable optimization technique for hard turning of 300M steel. Under those optimized conditions, Ra was decreased by 50%, and WL was decreased by 6% while compressive axial surface residual stress (ASRS) and hoop surface residual stress (HSRS) were increased by 307% and 74%, respectively.