A Data Driven Model for Predicting Tool Health Condition in High Speed Milling of Titanium Plates Using Real-Time SCADA

摘要: Abstract Carbide tools are easy to be damaged and quick-wear in the process of high speed machining (HSM) titanium plates, as it is difficult predict its working condition accurately. The quality requirement thin wall parts so high, health cutter closely related processing. Predicting change tool very important a controllable processing quality. In this paper, data model machine tool, communication framework access strategy based on OPC UA was developed healthy condition. A prognostics management (PHM) technology applied deal with data. BP neural network built reflect relationship between parameters tools. parameters, dimensions errors, surface roughness texture workpiece were inspected revise prediction model, which accuracy make more valuable. realized monitoring predicting information made intelligent judgment technical level cycle improved while efficiency increased.