Online Prediction of Tool Life Based on Multi-source Data Fusion

To address the challenges of difficult data acquisition and low prediction accuracy in tool life prediction, a method for data collection from FANUC numerical control machine tools and prediction of tool life based on the SSA-BP neural network is proposed. Data such as tool running time, spindle load rate, spindle load, X-axis load rate, Y-axis load rate, Z-axis load rate, X-axis current, Y-axis current, and Z-axis current are collected. By optimizing the core parameters of the BP neural network with the SSA algorithm, a tool life prediction model is constructed. Compared with traditional BP neural networks and GWO-BP neural networks, experimental results show that this model has the closest tool life prediction values to the actual values and better network stability, making it more suitable for tool life prediction.