关键词: 数控, 数据驱动, 钛合金铣削, 刀具健康预测

Abstract: A large number of multi-source heterogeneous data existed in the monitoring data due to the inconsistency of the data structures of the different equipment in the workshops, which were difficult to collect and monitor through a single communication protocol. To solve this problem, a data model of machine-tools, communication frameworks and access strategies were developed to realize multi-source heterogeneous data acquisition based on the OPC-UA and MTConnect herein. Carbide tools were easily damaged and quick-wear in high speed machining(HSM) of titanium alloys, as it was difficult to predict the remaining useful tool life in real time.Therefore, a BP neural network model based PCA method was developed to reflect the relationship among machine conditions and the parameters of the tools. The model realizes the monitoring and predicting of important informations of tool healthy conditions based on RUL.

Key words: computer numerical control(CNC), data-driven, titanium alloy milling, tool health condition prediction