Multi-response optimization of AISI H11 using Taguchi and Grey relational analysis

Tool & Die is one of the important department in the manufacturing industries that takes care of proper designing and fabrication of tools and dies required for the production. In this sector, tool steels are used as the primary source of materials. These tool steels belongs to the family of carbon and alloy steels. Mostly used alloying elements are chromium, tungsten, molybdenum and vanadium and are heat-treated. The objective of the work involves machinability study of AISI H11 chromium hot-worked steel extensively used for tool & die making. It deals with the analysis of machining parameters and its influences on the responses considered. Here the controlling parameters considered are cutting speed (C-s), feed rate (F-r) and depth of cut (D-c) and responses as surface roughness (R-a) and material removal rate (M-rr). Each controlling parameters are assigned with 3 levels and experimental runs were executed as per taguchi robust design. To determine multi-objective optimal solution, grey relational analysis (GRA) is employed. GRA is used as it provides a feasible platform for converting a multi-objective function into single-objective function. The experimental runs were performed as per L27 orthogonal array sequence in CNC end-milling. The responses recorded are then analyzed using analysis of variance (ANOVA) and optimal solutions are validated through confirmatory runs. The entire machinability study of AISI H11 is performed in two conditions involving rough machining and finish machining. This has been addressed based on the machining scenario followed in industries taking up job orders. The confirmatory results for rough machining recorded was found to be 0.7871 microns against predicted value of 0.7654 microns resulting with a deviation error of 2.88%. Similarly, for finish machining, confirmatory runs recorded 0.8579 microns against predicted value of 0.8357 microns resulting with a deviation error of 2.66%. The deviation level indicated above between predicted and observed values are minimum, which shows the reliability of the optimal solutions arrived in.