Learning digital emulators for closed architecture machine tool controllers

Machine tool controllers (MTCs) are evolving rapidly in response to the growing precision requirements, industry-wide thrusts to integrate cloud applications, external sensors and other data sources, as well to assure cybersecurity. However, most of the MTCs have closed architecture. This severely impedes the innovations to enhance their performance as well as assure their safety and security. Digital emulators, as alternatives to real closed architecture MTCs, are considered essential to assess the performance of the closed MTCs and various innovations therein. We present a machine learning method to create digital emulators that can mimic the dynamics of closed architecture MTCs. The proposed method is based on interrogating the controller using a set of production recipes (e.g., G-codes) and employing the response of the controller to learn a low-dimensional parameterization of the underlying architecture. The low-dimensional base model captures the key aspects of the structure and dynamic connectivity among the various components of an MTC. The parameters of the base model are tuned based on the outputs of an MTC gathered using standard data exchange protocols (e.g. OPC UA, MTConnect). We applied the proposed approach to develop emulators for a SIEMENS controller that mimics X–Y motion of their 2-axis motion control systems. The current implementation of the emulator can track the measured path trajectories up to 0.23 mm accuracy for various geometries tested. Our approach can be used to generalize emulator development for different types of real world MTCs.