CNC Machinability study of AISI 316L & Optimization of Machining parameters using TLBO algorithm. (2022) - Kundu, Sumon; Choudhuri, Bikash; Sen, Ruma
Abstract: CNC turning operation plays a significant role in manufacturing industries in the current situation. With the development and use of high generation materials that have very high toughness and hardness, the practice of the CNC machining process has become a global trend. To reduce time and money-consuming tasks for the industries and the best quality of products, it is necessary to select optimum machining parameters, tool geometry, and cutting conditions. Present work shows the process parameters and related aspects like surface roughness, metal removal rate, and surface morphology during wet turning on austenitic stainless steel 316L by CVD Coated Carbide Insert Tool. A comprehensive comparison of results has been made by varying cutting conditions at different speeds, feed, and depth-of-cut levels. The design of the experiment (DOE) was taken from the Taguchi orthogonal array, and ANOVA analyzed the result. The best parametric combination that satisfied all performances at a high level has been evaluated by Teaching-Learning-Based Optimization (TLBO). The study shows that surface roughness is directly affected by Spindle Speed, Feed, and Depth of Cut. Material removal rate related to FEED and Depth-of-cut (DOC).