@inproceedings{a6d6bac3681149ac95b50baeded4c60b,
title = "Digital twin of dynamic error of a collaborative robot",
abstract = "This paper proposed a new digital twin method to effectively, accurately and in real-time in-situ track machine dynamic error using accelerometer data. The digital twin tracked the positioning data measured by its built-in encoders and superimposes it with displacement data obtained from the accelerometers for more accurate positioning, resulting in micrometre level improvements. In this paper, the digital twin dynamic error tracking approach was implemented on a collaborative robot. Ball-bar tests were conducted to evaluate the effectiveness of the proposed digital twin dynamic error tracking approach. The results show a significantly improved position tracking accuracy of up to 75%, compared with using the collaborative robot's built-in encoders. The digital twin provides a cost-effective solution to track machine dynamic errors. This method could also be expanded to work on other CNC machines and robots, making it a universal solution for improving machine dynamic m easurement accuracy.",
keywords = "accelerometer, COBOT, Digital twin, dynamic error",
author = "Charlie Walker and Xichun Luo and Abhilash, {P. M.} and Qi Liu and Rajeshkumar Madarkar and Erfu Yang",
year = "2023",
month = jun,
day = "16",
language = "English",
series = "European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 23rd International Conference and Exhibition, EUSPEN 2023",
publisher = "EUSPEN",
pages = "309--312",
editor = "O. Riemer and C. Nisbet and D. Phillips",
booktitle = "European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 23rd International Conference and Exhibition, EUSPEN 2023",
note = "23rd International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2023 ; Conference date: 12-06-2023 Through 16-06-2023",
}
