Abstract
Three kinds of cylindrical plunge-grinding experiments are conducted with CBN grinding wheel by single-factor method for the carburizing alloy steel 20CrMnTi to investigate surface quality parameters and grinding force with change of wheel speed, workpiece speed, and the depth of cut. It is clarified that how change trends and correlations among surface quality parameters, grinding force, and grinding process parameters are. Based on the experiment results, analytical models for surface roughness and grinding force are established, which make the multi-object grinding parameter optimization possible and can predict the roughness and grinding force. The alloy steel 20CrMnTi workpiece surface quality and grinding efficiency-oriented optimization are conducted by the Strengthen PARETO algorithm, which make the prediction error good enough with roughness error less than 2% and the removal rate per unit width $$ {Q}_W^{\prime } $$ 1% below. The Strengthen PARETO optimal predictive model proves to be effective and sufficient to solve the problem of surface quality and grinding efficiency-oriented optimization. The alloy steel grinding parameters meet the requirement of the roughness under the value 0.8 μm with high grinding efficiency in automobile industry for shaft parts and gear production.
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