Experimental Optimization of High-precision Turning Parameters of AL6061 Materials for Automotive Industry Based on Grey Relational Analysis

Abstract

This research article aims to explore the relationship between the machining parameters of a Slant Bed Turning Centre Computer Numerical Control (SB/C/CNC) precision lathe and surface microhardness, dimensional error and surface roughness of AL6061. A technique called the central composite design (CCD) method with 13 experiments was used to evaluate the surface microhardness, dimensional error, and surface roughness after a turning operation using a micro-grooved texture tool. Separate prediction models were developed for each of these characteristics using the response surface method (RSM) in order to find the optimal process parameters for each characteristic. The analysis of variance revealed that the prediction models for surface microhardness, dimensional error, and surface roughness were highly significant, with p-values less than 0.0001. The process parameters that resulted in the highest surface microhardness were a cutting speed (V_c) of 154.363 m/min and a feed rate (f_z) of 0.231 mm/rev. On the other hand, the process parameters that led to the lowest dimensional error and surface roughness were V_c = 154.363 m/min, f_z = 0.1389 mm/rev, and V_c = 152.081 m/min, f_z = 0.1025 mm/rev, respectively. The multi-objective prediction model based on gray relational analysis showed an error range of 1.5% to 3.1% and a minimum gray relational degree value of 0.3503 within the feasible process parameter range. The accuracy of this multi-objective prediction model was higher, with a stronger response to the cutting speed V_c compared to the feed rate f_z. The determined feasible process parameter range serves as a useful reference for engineers working with AL6061 materials in turning operations.