A Load-based tool Wear Monitoring method for Machining process
A Load-based tool Wear Monitoring method for Machining process
- Author(s): Pengju Ma ; Saisai Tong ; Wen Xu ; Yan Gao ; Xuezhu Zheng ; Bo Ye
- DOI: 10.1049/cp.2018.0354
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
CSAA/IET International Conference on Aircraft Utility Systems (AUS 2018) — Recommend this title to your library
Thank you
Your recommendation has been sent to your librarian.
- Author(s): Pengju Ma ; Saisai Tong ; Wen Xu ; Yan Gao ; Xuezhu Zheng ; Bo Ye Source: CSAA/IET International Conference on Aircraft Utility Systems (AUS 2018), 2018 page (8 pp.)
- Conference: CSAA/IET International Conference on Aircraft Utility Systems (AUS 2018)
- DOI: 10.1049/cp.2018.0354
- ISBN: 978-1-78561-791-1
- Location: Guiyang, China
- Conference date: 19-22 June 2018
- Format: PDF
It is very difficult to avoid the problem of tool wear or breakage during the machining of CNC machine tools. Failure to detect and stop the machine in time is likely to cause damage to the machine or workpiece. This requires the on-line monitoring of the tool state during machining. In this paper, a load-based tool wear monitoring method is proposed. The self-learning method is adopted to obtain the load range of a specific machining process, so that on-line monitoring of the subsequent same machining process can be carried out. Self-learning is mainly used to process the load signal by using the statistical algorithm (6σ algorithm), so as to obtain the upper and lower boundary of the monitoring range. The load signals in the subsequent machining process are compared with the upper and lower boundaries determined by the self-learning algorithm to determine whether the tool is worn or not. Finally, the feasibility of this method was verified by experiments.
Inspec keywords: cutting tools; learning (artificial intelligence); machining; wear; computerised numerical control; condition monitoring; production engineering computing; machine tools
Subjects: Machining; Industrial applications of IT; Production engineering computing; Tribology (mechanical engineering); Production equipment
Related content
content/conferences/10.1049/cp.2018.0354
pub_keyword,iet_inspecKeyword,pub_concept
6
6