Surface hardness improvement in surface grinding process using combined Taguchi method and regression analysis

Abstract

This study has implemented a combined Taguchi method and regression analysis to optimize grinding parameters to enhance the superficial hardness of workpiece. The workpiece material is AISI1045 annealed steel and the process parameters include depth of cut, wheel speed, workpiece speed, cross feed, and mode of dressing. The DOE technique is used to find out the number of experiments by using Taguchi’s L27 which includes five parameters (depth of cut, wheel speed, workpiece speed, cross feed, and mode of dressing) at three levels. By applying the mean response and signal to noise ratio (SNR), the best optimal grinding condition has been reached at D3/S3/W2/F2/M1 i.e. depth of cut is 0.03 mm, wheel speed is 32 m/s, workpiece speed is 10 m/min, cross feed is 5 mm/rev, and mode of dressing is fine. Based on the ANOVA, the significance and percentage contribution of each parameter is determined. It has been revealed that depth of cut has maximum contribution on surface hardness. The mathematical model of surface hardness has been developed using regression analysis as a function of the above mentioned independent variables. A confirmation experiment, as final step, has been carried out with 94.5% confidence level to certify optimized result.

Keywords

• surface grinding
• microhardness
• microstructure
• optimization
• taguchi
• regression analysis