<p>Existing tool wear prediction models often exhibit limited adaptability to general machining processes, frequently resulting in inaccuracies or even failures. Moreover, comprehensive collection and analysis of cutting data under complex working conditions remain challenging. To overcome these limitations, this study proposes a parallel dual-prediction framework integrating a self-attention temporal convolutional network (SATCN) with a digital twin (DT) mechanism model, enabling dynamic tool wear monitoring in complex environments. Firstly, A DT-enabled data acquisition system and virtual modeling platform were established to capture real-time acceleration sensor data and tool wear measurements throughout the machining process. Secondly, A DT mechanism model was developed across four dimensions (geometric, physical, behavioral, and rule-based) to simulate tool wear dynamics. A twin data model leveraging SATCN was designed to extract critical features from sensor signals. Thirdly, Sensor-derived analytical results continuously update and refine the DT mechanism model, ensuring precise wear quantification. Finally, Experimental validation utilized a digital-physical integration platform, with comparative assessments against single-model approaches employing Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). The parallel hybrid-driven model demonstrated superior performance: 50.71% lower MAPE, 59.51% reduced average RMSE, and 3.64% higher average R² compared to standalone data-driven models. These metrics conclusively validate the enhanced accuracy and robustness of the proposed methodology in tool wear detection.</p>