Multi-response optimisation of micro-milling performance while machining a novel magnesium alloy and its alumina composites

The present research attempts to maximise the overall performance of micro-milling process while machining Mg-3.0Zn-0.7Zr-1.0Cu alloy and its alumina composites. Three performance measures (surface quality, cutting forces, and tool wear) is used for the assessment. Using a Taguchi L18 orthogonal array, eighteen experiments are carried out to test the effect of three levels of spindle speed, feed per tooth, and cutting depth. Grey relational analysis (GRA) and techniques for order of preference by similarity to ideal solution (TOPSIS) are employed to optimise the parameters. In addition, the equal weight method and entropy weight method (EWM) in combination with the analytic hierarchy process (AHP) are used to assign weights to the parameters. The GRA and TOPSIS results yielded the same optimal parameter conditions for maximising the micro-milling performance while using two different weight-assigning methods. Based on the predicted closeness value results, the GRA method is the most efficient for multi-objective optimisation.