@inproceedings{4f0a3e8a360440f68f90c3ed3a8c6ac6,
title = "Control Method and Model of Constant Cutting Depth for Cutting Low Stiffness Parts",
abstract = "During the cutting process of low stiffness parts, deformation is prone to occur, making it difficult to ensure the dimensional accuracy and surface roughness of the parts. The machining of the neck of the dynamically tuned gyroscope's flexible joint is taken as an example. Based on the optimization of cutting parameters using a cutting parameter - surface roughness neural network model, which is used to control the surface roughness of the part, a constant cutting depth control method is explored and a mathematical model is established to address the problem of cutting depth's dynamic changes caused by low or variable stiffness of the part structure, which affects dimensional accuracy. Then a model foundation for dimensional accuracy control and process optimization of low or variable stiffness parts is established.",
keywords = "cutting process, dimensional accuracy, low stiffness, surface roughness",
author = "Zhichao Sheng and Xin Jin and Ruilin Gao and Jiajing Guo and Chaojiang Li",
note = "Publisher Copyright: {\textcopyright} 2024 The Authors.; 5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023 ; Conference date: 30-06-2023 Through 02-07-2023",
year = "2024",
month = feb,
day = "12",
doi = "10.3233/FAIA231408",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "1054--1061",
editor = "Chenglizhao Chen",
booktitle = "Artificial Intelligence Technologies and Applications - Proceedings of the 5th International Conference, ICAITA 2023",
address = "Netherlands",
}