Machinability assessment when turning AISI 316L austenitic stainless steel using uncoated and coated carbide inserts

Austenitic stainless steel AISI 316L is mostly used as an implant material and is customarily applied as impermanent devices in orthopedic surgery because of its low cost, adequate mechanical properties, and acceptable biocompatibility. AISI 316L is an extra-low carbon type 316 (austenitic chromium nickel stainless steel containing molybdenum) that minimizes harmful carbide precipitation at elevated temperature. Machining is part and parcel during the fabrication of implants and medical devices made from stainless steels and thus it is of interest to evaluate the machinability of AISI 316L. In this study, austenitic stainless steel AISI 316L was turned using two commercially available cutting tool inserts at various cutting speeds (90, 150, and 210 m/min) and feeds (0.10, 0.16, and 0.22 mm/rev) and at a constant depth of cut of 0.4 mm. The turning of AISI 316L was implemented in dry cutting. The cutting tools used were an uncoated tungsten carbide-cobalt insert (WC-Co) and a multi coated nano-textured TiCN, nano-textured Al2O3 thin layer, and a TiN outer layer insert. The cutting forces, total power consumption, surface roughness, and tool life were measured/obtained and analyzed. The total power consumption of the turning process was obtained from direct measurements as well as using a combination of theoretical formulas and experimental cutting force data. The machining experiments and their responses were designed and evaluated using the three-level full factorial design and the analysis of variance (ANOVA). It was found that the cutting speed and feed significantly affect the various machining responses observed. The cutting force and total power consumption increased with increasing cutting speed, but the surface roughness and tool life decreased. With increasing feed, surface roughness and tool life decreased but the cutting force and total power consumption increased. The empirical mathematical models of the machining responses as functions of cutting speed and feed developed were statistically valid. Confirmation runs helped to prove the validity of the models within the limits of the factors investigated