Real-Time Diagnostics of Metal-Cutting Machines by Means of Recurrent LSTM Neural Networks

摘要

Real-time diagnostics of modules in metal-cutting machines may be based on neural-network algorithms for simulation of the standard process, identification of defects, and the introduction of corrections in the cutting machine’s control system. The machining conditions in normal operation of the machine are recorded by means of a trained neural network with long short-term memory (LSTM network). In real-time operation, the difference between the standard neural-network model and the actual process characteristics is used to determine the type of defect and the module of the machine where it occurs on the basis of a second neural network, the classification unit.