Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications

Machining accuracy retainability prediction of machine tool based on least square support vector machine

Authors
Qiang Cheng, Baobao Qi, Bingwei Sun, Guobin Yan
Corresponding Author
Qiang Cheng
Available Online January 2017.
DOI
https://doi.org/10.2991/icmmita-16.2016.152How to use a DOI?
Keywords
machine tool; accuracy retainability; machining accuracy; LS-SVM.
Abstract

The accuracy retainability is becoming an important performance index of machine tool, and how to improve it is a tough problem faced to manufacturers and users. Generally, it needs to measure the errors termly and repeatedly during the specified period to analyze the timeliness machining accuracy retainability, which generates intricate and vast error data. In this paper, a solution to predict machining accuracy retainability is proposed based on least square support vector machine (LS-SVM). A vertical machining center that machines plane and hole continuously for half a year is selected as an illustrative example. The analysis results show that the proposed method is good at predicting the timeliness machining accuracy retainability of machine tool.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
Series
Advances in Computer Science Research
Publication Date
January 2017
ISBN
10.2991/icmmita-16.2016.152
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmmita-16.2016.152How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Qiang Cheng
AU  - Baobao Qi
AU  - Bingwei Sun
AU  - Guobin Yan
PY  - 2017/01
DA  - 2017/01
TI  - Machining accuracy retainability prediction of machine tool based on least square support vector machine
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 817
EP  - 823
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.152
DO  - https://doi.org/10.2991/icmmita-16.2016.152
ID  - Cheng2017/01
ER  -