For generating a texture or pattern on a work surface, one of the emerging processes is laser surface texturing (LST). It is an effective method for producing texture on a work surface. Literature shows that various lasers have been applied to generate textures on the surface of work materials. Recently, LST has shown tremendous potential in the field of biomedical applications. Applying the LST process, the efficacy of the biomaterial has been drastically improved. This paper presents an in-depth review of laser surface texturing for biomedical applications. The effect of LST on important biomaterial has been thoroughly studied; it was found that LST has extreme potential for surface modification of biomaterial and can be utilized for biomedical applications.

The paper deals with the parametric analysis and ANFIS-based modeling of process parameters during turning operation of stainless steel 202. In this experimental work, the Taguchi L16 DOE was utilized for designing the experiments considering three turning parameters namely feed rate, spindle speed as well as depth of cut. All the experiment was conducted at different parametric combinations and the performance namely material removal rate (MRR) and surface roughness (Ra) were evaluated. With the help of a suitable plot, the influences of process parameters on performance were analyzed. It was observed that depth of cutis is the majority influencing parameter for varying (parametric contribution is 53.33%) the MRR, whereas in case of surface roughness, the most contributing parameter is the spindle speed (parametric contribution is 95.71%). Further, the adaptive network-based fuzzy inference system (ANFIS) based modeling has been done to understand and establish the input-output relationship. The experimental results and ANFIS predicted results were compared and it is found that the ANFIS predicted results are accurate for predicting the responses during turning operation of stainless steel 202.