Predictive modelling and parametric optimization of minimum quantity lubrication–assisted hobbing process

Abstract

This paper focuses on parametric analysis, modelling, and parametric optimization of minimum quantity lubrication–assisted hobbing (MQLAH) using an environment-friendly lubricant for manufacturing superior quality spur gears. Influences of hob cutter speed, axial feed, lubricant flow rate, air pressure, and nozzle angle on the deviations in total profile, total lead, total pitch and radial runout, and flank surface roughness parameters were studied by conducting 46 experiments using the Box-Behnken method of response surface methodology. Results revealed that the effect of air pressure is negligible but other parameters have a significant impact on the considered responses. A back propagation neural network (BPNN) model was developed to predict microgeometry deviations and flank surface roughness values of the MQLAH-manufactured spur gears. The BPNN-predicted results are found to be very closely agreeing with the corresponding experimental results with a mean square error as 0.0063. A real-coded genetic algorithm (RCGA) was used for parametric optimization of MQLAH process for simultaneous minimization of microgeometry deviations and flank surface roughness. Standardized values of the optimized parameters were used to conduct confirmation experiments whose results had very good closeness with RCGA-computed and BPNN-predicted values and produced spur gears of superior quality. This study proves MQLAH to be a potential sustainable replacement of conventional flood lubrication–assisted hobbing for manufacturing cylindrical gears of better quality.