Machining Process Parameters Optimization of Aluminium Alloy AA6463 for CNC Turning Using Grey Relational Analysis

Abstract

Most of the present-days machining is accomplished by computer numerical control (CNC), in which computers are used to controlled, the operations of multiple machines like drilling, milling, lathes, and other cutting machines. The CNC lathe machine was selected due to its wide availability and versatility, allowing it to handle multiple operations with minimal changes in setup. Turning was specifically chosen for this study because of its numerous benefits. This experiment focuses on optimizing the CNC turning process for machining AA 6463. The input parameters considered in this investigation include spindle speed (SS), feed rate (FR), depth of cut (DOC), and the use of coolant under both dry and wet conditions, where the output parameters are surface roughness i.e. arithmetic average (Ra).The designed experimental results are used in GRA. A 16 run Full Factorial design is chosen for ultimate tentative design considering two levels for the selected four parameters. The results of the confirmation trials reveal that grey relational analysis identifies the optimal combination of turning parameters. Performance metrics, such as surface roughness and material removal rate, are evaluated by calculating the grey relational grade. Ultimately, confirmation tests conducted under optimal conditions demonstrate improved surface finish, and the experimental values for surface roughness and material removal rate are compared with the predicted values.