Surface roughness prediction based on fusion of dynamic-static data

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The surface roughness in mechanical milling is a critical indicator of machining quality. Due to the variability in the geometric structure of the workpieces and the complexity of the milling mechanisms, traditional roughness prediction methods often fail to meet the requirements of actual machining scenarios. To enhance the accuracy of surface roughness prediction during the milling process and to ensure its applicability in real-world production, this paper introduces a novel model that integrates Multi-scale Convolutional Neural Network, Stacked Bidirectional Long Short-Term Memory, and Self-Attention mechanisms (MC-SBiL-SA), which integrates dynamic current signals with static process data for roughness prediction. This model involves extracting features from dynamic signal data in various ways, then feeding these features into a BiLSTM network for training. To enable the model to learn more effective data features, an attention mechanism is introduced after the BiLSTM to assign different weights to the data features, thereby amplifying the impact of important features. Finally, the dynamic signal and static process data are combined in a shallow neural network for regression prediction. Comparative results with various baseline models demonstrate that the MC-SBiL-SA model, which fuses dynamic and static data, exhibits superior generalizability and effectiveness in predicting surface roughness.