DIGITAL TWIN MACHINE TOOL FEED DRIVE TEST BENCH FOR RESEARCH ON CONDITION MONITORING AND MODELING

Abstract

Machine tools are essential components of modern manufacturing. They are com posed of various mechanical, hydraulic, and electrical systems such as the spindle, tool changer, cooling system, and the linear and rotary feed drives. Due to their com plexity, high cost, and importance to the manufacturing process it is recommended to implement some sort of condition monitoring and predictive maintenance to ensure that they remain reliable and high performing. One way of potentially implement ing predictive maintenance and condition monitoring is digital twins. Digital twins enable the real-time, accurate, and complex modeling and monitoring of mechanical systems. They utilize data collected from the system to constantly update their mod els which can be used for monitoring of the systems state and future predictions. This work presents a digital twin workbench of a machine tool feed drive. The workbench enables the collection and analysis of large, varied, high-frequency data which can be used to construct a digital twin of the feed drive. A digital twin can enable many other useful functionalities. Some of these functionalities include condition moni toring, modeling, control, visualization, and simulation. These functionalities can enable maximum asset performance and are key in implementing effective predictive maintenance. The main contributions of this work are the following: The design and iv construction of a machine tool feed drive which implements a novel external distur bance force method. A new method of fault detection in ball screws using interacting multiple models which was shown to provide accurate estimates of levels of preloads in a ball screw driven feed drive. A digital twin based modeling strategy and analysis of the data generated by the system including system modeling and observations on modeling difficulties.