Experimental study and multi-objective optimisation of CNC turning parameters of AL6061 materials

This paper investigates the effectiveness of feed rate, depth of cut, and cutting speed on material removal rate (MRR) and surface roughness (SR) of CNC lathe machines on AL6061 Materials. A combined use of Taguchi, Response Surface Methodology (RSM), and Genetic Algorithms (GA) is proposed to study and optimise turning processes. Firstly, the RSM with a fractional factorial design is implemented to indicate the significance and influence of process parameters on RSM and MRR. With the achieved data, regression models are formulated to present the relationship between control factors and the responses and are proven reliable in predicting through statistical analysis and validating experiment results. Further, a multi-objective genetic algorithm (GA) was used to obtain a Pareto solution set with multiple combinations of factors for optimal MRR and SR. Then, the best trade-off between process parameters is analysed to achieve desired quality outcomes. The results indicate that the proposed approach provides an effective solution for CNC Turning machines and can extend to similar problems in various fields.