Multi-objective optimization of turning based on grey relational and principal component analysis

The turning experimental model was presented with the cutting speed, feed rate and depth of cut as design variables based on Taguchi method. The multi-objective optimization of turning was performed with the surface roughness, cutting force and tool wear as performance characteristics by using combined grey relational analysis and principal component analysis. In order to objectively reveal the relative importance for each performance characteristic in grey relational analysis, principal component analysis was specially introduced here to determine the corresponding weighting values for each performance characteristic. The result analysis showed that cutting speed of 240 m/min, feed rate of 0.10 mm/r, depth of cut of 0.15 mm were the optimal cutting parameters. Meanwhile, the optimal performance characteristics were surface roughness of 0.168 μm, cutting force of 163.636 N and tool wear of 0.129 mm.