An enhanced neural network-based method for predicting step error in a five-axis finishing machining

To address the low efficiency of traditional geometry-based step error calculations in five-axis numerical control (NC) finishing that cannot reuse historical data, this study proposes a step error prediction method based on a SSA-Attention-LSTM (Sparrow search algorithm–attention mechanism–long short-term memory) model. The method employs principal component analysis (PCA) for dimensionality reduction, optimizes LSTM hyperparameters using the Sparrow search algorithm, incorporates an attention mechanism to enhance key feature extraction, and introduces dropout to prevent overfitting. Experimental results on two surface models show that the proposed model achieves an average R2 of 99.4 %, which is 31 %, 10 %, 5 %, and 6 % higher than that of back propagation (BP), long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and bidirectional long short-term memory (BiLSTM) models. In the blade test case, the model achieves high-precision prediction using only 9480 training samples, with an R2 of 0.995, demonstrating high prediction accuracy for step error in five-axis finishing tool paths.