Poster abstract

Abstract

Precision screw assembly (hereafter called ball screw), as shown in Fig. 1, is a mechanical wear out part that widely used in CNC (computer numerical control) machine tools to control the movement of processing targets and spindles. Up until now, there has been no simple way to directly measure screw for knowing the state of wear quantitatively. An indirect approach is logging all the signals (vibration, temperature, and preload change) during the operation of screw, and to use them to construct the wear model for estimating its remaining lifetime. To achieve this goal, we proposed a cloud-based logging system in this study that emphasizes (1) logging all the signals during operation in a screw's whole lifetime, and transferring to the data server without data loss; and (2) saving all the data into the cloud data storage of the screw's manufacturer. The data collected from many screws can be used to analyze and construct the wear model of screw, allowing the manufacturer to understand the state of wear and send a warning to the tool machine's owner before excessive wear.