To establish a high-precision prediction model for the material removal rate (
MRR) of TC4 titanium alloy material in vertical vibratory finishing equipment, an orthogonal experiment was conducted using TC4 titanium alloy plate as the experimental specimen. We performed variance analysis of
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To establish a high-precision prediction model for the material removal rate (
MRR) of TC4 titanium alloy material in vertical vibratory finishing equipment, an orthogonal experiment was conducted using TC4 titanium alloy plate as the experimental specimen. We performed variance analysis of the impact of vibration frequency, the phase difference, the mass of upper eccentric block, and the mass of lower eccentric block on the
MRR. We then drew the main effect diagram and analyzed the influence of various process parameters on the
MRR. Mathematical regression and a neural network were used to construct predictive models for the
MRR with respect to process parameters, and a genetic algorithm (GA) was coupled to optimize the neural network to improve the predictive performance of the model. By calculating the
R2, validating the set sample prediction error, and averaging the absolute percentage error (
MAPE) of each model, it was found that the neural network model had better prediction performance than the mathematical regression model, with an accuracy of 82.2%. After coupling with the GA, the prediction accuracy reached 95.5%. The research results indicated that, compared with mathematical regression and the original neural networks, the neural network coupled with the GA had better predictive performance, providing an effective method for predicting the
MRR in vertical vibratory finishing.
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