@inproceedings{ae7a0cd853d0415dbc922aa4639c2bf5,
title = "Straightness Error Assessment Model of the Linear Axis of Machine Tool Based on Data-Driven Method",
abstract = "In batch assembly, fast and accurate assessment of MT-LA straightness error is significant important for controlling of MT-LA assembly quality. In this study, in order to construct MT-LA straightness error assessment model, a data-driven method based on the bootstrap resampling approach improved fast correlation based filter (BR-FCBF) algorithm and genetic algorithm optimized multi-class support vector machine (GA-MSVM) algorithm is proposed. Firstly, the BR-FCBF algorithm is used to select the key assembly parameters that affect the straightness error. Secondly, the GA-MSVM algorithm is applied to construct the straightness error assessment model. Finally, the assembly-related data collected on a MT-LA assembly workshop is used to verify the proposed method. The experimental results show that the constructed straightness error assessment model has shown good performance in straightness error assessment.",
keywords = "BR-FCBF, Data-driven, GA-MSVM, Linear axis, Straightness error assessment model",
author = "Yang Hui and Xuesong Mei and Gedong Jiang and Fei Zhao",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 ; Conference date: 08-08-2019 Through 11-08-2019",
year = "2019",
doi = "10.1007/978-3-030-27538-9\_47",
language = "英语",
isbn = "9783030275372",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "554--563",
editor = "Haibin Yu and Jinguo Liu and Lianqing Liu and Yuwang Liu and Zhaojie Ju and Dalin Zhou",
booktitle = "Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings",
}