Research on Prediction of Material Rremoval Rate in TC4 Blade Polishing Based on RBF-FA
In order to optimize the material removal rate during the polishing process of titanium alloy TC4 blades,thereby improving process efficiency and extending equipment service life,an orthogonal experiment between polishing process parameters and material removal rate was designed.On the basis of obtaining orthogonal test data,a material removal rate prediction model based on radial basis function(RBF)was constructed,and the firefly algorithm(FA)was introduced for parameter optimization.The RBF-FA prediction model was in-depth trained and verified based on experimental data,and achieved high-precision material removal rate prediction capabilities.The experimental results show that the RBF-FA prediction model has high accuracy,with the average errors of the training group and test group being 8.04%and 5.91%respectively,confirming the effectiveness of the model in predicting the material removal rate in the TC4 polishing process.The method proposed can provide a basis for the optimization of TC4 alloy blade polishing process parameters.
radial basis function neural networkalloy TC4blade polishingmaterial removal rate
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