A Study of Tool Wear Prediction Based on Digital Twins - LIU Minghao; MAO Xinhui; XIA Wei; YUE Caixu; LIU Xianli (Journal of Harbin University of Science and Technology, 2025)
In the context of the global intelligent transformation of manufacturing, digital twin technology, through the deep integration of physical entities and virtual models, provides an innovative path for the implementation of smart manufacturing. Taking the VMC-C50 five-axis CNC machine tool milling titanium alloy as the research object, a digital twin architecture-based milling tool wear monitoring system was constructed based on the technical route of “ virtual-real interaction, data-driven”. By integrating the physical perception layer, virtual modeling layer, data interconnection layer, and intelligent service layer, a bidirectional communication mechanism between the physical machine tool and the virtual model was established, achieving full-factor mapping and dynamic optimization of the machining process. With tool wear prediction as the application scenario, a deep learning model based on the fusion of multi-scale convolutional neural network, residual network, bidirectional long short-term memory network, and gated recurrent unit (MSCNN-ResNet-BiLSTM-GRU) was proposed. This model can deeply extract spatial features and dynamic temporal features, significantly improving prediction accuracy compared to conventional models. Through virtual-real interaction and data fusion mechanisms, it provides an engineering solution for the dynamic perception of tool wear during the milling process.