@inproceedings{ed9602af791a4ef68c822baea04d5c14,
title = "Time Domain Identification Method of Cutting Forces in Robotic Milling Process",
abstract = "Cutting forces are important for the monitoring and control of robotic milling processes. In practice, it is very difficult to measure cutting forces directly during the machining of large components. The indirect identification of cutting force is distorted as the frequency response function (FRF) changes with the machining pose. In this paper, the displacement sensor mounted on the spindle is used for the identification of cutting forces. Firstly, the modal parameters for a few machining positions are used to develop the Gaussian process regression (GPR) model. Secondly, the system equations of motion are transformed into state-space equations. Thirdly, a cutting force identification method based on Kalman filter is established. Finally, the effectiveness of the proposed method is verified by numerical simulations.",
keywords = "Dynamic force identification, Industrial robot, Peripheral milling, Spindle",
author = "Maxiao Hou and Hongrui Cao and Jianghai Shi",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference of The Efficiency and Performance Engineering Network, TEPEN 2022 ; Conference date: 18-08-2022 Through 21-08-2022",
year = "2023",
doi = "10.1007/978-3-031-26193-0\_7",
language = "英语",
isbn = "9783031261923",
series = "Mechanisms and Machine Science",
publisher = "Springer Science and Business Media B.V.",
pages = "64--73",
editor = "Hao Zhang and Yongjian Ji and Tongtong Liu and Xiuquan Sun and Ball, \{Andrew David\}",
booktitle = "Proceedings of TEPEN 2022 - Efficiency and Performance Engineering Network",
}