Improving surface integrity aspects of AISI 316L in the context of bioimplant applications

Bioimplants demand unique surface integrity (SI) requirements wherein the primary target is to have minimum surface roughness and maximum microhardness to improve their corrosion and wear resistance characteristics. This demands a more meticulous approach while machining biocompatible materials such as AISI 316L than may be the case when the alloy is to be machined for other applications. Various machinability studies have been conducted on AISI 316L targeting the aforementioned aspects. However, in view of the range of parameters that could be investigated and the availability of various parametric optimization algorithms, it is felt that research gaps still exist. This paper reports on the improvement of surface integrity aspects of AISI 316L in the context of bioimplant applications. The grey relational analysis (GRA) approach has been used to first optimize the influence of various milling parameters: cutting environment (wet and dry conditions), the cutting speed (CS), the feed rate (FR), and the axial depth of cut (A_p) for the aspects of surface roughness (R_a), and microhardness with biocompatibility requirements in mind. The experimentation work involves two phases: Taguchi L₁₈ array was used for phase I experimentation followed by multi-attribute GRA-based optimization. Phase II experimentation explored the possibility of further increase in microhardness by machining with worn tools taking GRA-identified optimized parameter levels as a baseline and then increasing the cutting speed. It has been found that the use of worn tools at GRA-optimized parameters results in further improvement in SI aspects in general. An extent of 37% improvement in terms of the maximum value of microhardness (301 HV at 15-μm depth) has been reported compared with GRA-optimized value (218 HV) when worn tools at higher cutting speeds are employed. This is accompanied by a machined hardened layer extending up to a depth of 222 μm and an associated R_a of 0.85 μm. Microstructure analysis shows more machined-affected zones with worn tools thus supporting the findings.