Performance Optimization in CNC Turning of Aluminium 6082-T6 using Grey-DNMA Approach

Optimizing a computer numerical control (CNC) turning process is challenging due to the presence of multiple input parameters, conflicting performance metrics, nonlinear interactions, stochastic material removal behaviour and uncertainty in decision making. Multi-criteria decision making (MCDM) approaches have emerged as potent tools to unravel the optimization problems of CNC turning processes while finding out the ideal combinations of the input parameters providing the most favourable response values. This paper demonstrates the application of a newly developed MCDM tool, i.e. double normalization-based multiple aggregation (DNMA) along with grey numbers to handle uncertainty and imprecision in CNC turning optimization. Spindle speed (SS), depth of cut (DOC) and feed rate (FR) are treated as the input parameters, while material removal rate (MRR), average arithmetic surface roughness (Ra), machining temperature (T) and vibration (V) are the responses. The relative importance of the stakeholders and responses are expressed using grey numbers to capture real-world ambiguity. The grey-DNMA approach identifies the optimal combination as SS = 1000 rpm, DOC = 0.10 mm and FR = 0.095 mm/rev for improving the turning performance of aluminium 6082-T6 alloy, with the corresponding response values as MRR = 0.0394 g/s, Ra = 1.4333 μm, T = 36.75 ℃ and V = 0.315 μm. Beyond the specific case study, the proposed framework offers a robust and flexible decision making tool for optimizing complex manufacturing processes under uncertain environments involving multiple stakeholders. It can be effectively extended to other machining operations and industrial optimization problems where qualitative judgments and imprecise data are prevalent.