A Machine Learning based Approach to Autogenerate Diagnostic Models for CNC machines
Second place SRC - Graduate
This article presents a description of a system for the automatic generation of predictive diagnostic models of CNC machine tools. This system allows machine tool maintenance specialists to select and operate models based on LSTM neural networks to determine the state of elements of CNC machines. Examples of changes in the accuracy of the models used during operation are given to determine the state of the cutting tool (more than 95%) and the bearings of electric motors (more than 91%).