Abstract
Catastrophic tool failure (CTF) in milling process can cause damage to the product’s machined surface and the machine tools, leading to huge financial losses. It is therefore critical to detect CTF in advance and promptly respond to it. Because of the safety and quality requirements imposed in practice, there are far fewer failure samples than normal samples, and this disequilibrium makes it difficult to detect failures. The aim of this study is to develop a new, easy, and practical automatic system for tool breakage detection using the acoustic emission (AE) technique. Components of AE raw data are analysed to locate the moments of tool breakages and to screen the corresponding AE feature samples. A support vector machine-based cost-sensitive breakage detection model is established and optimized. The proposed model is applied and validated by experiments conducted on a factory’s milling machine. The model achieves an accuracy of 91.18% in the detection of breakages. The results show the practicability and validity of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Journal of Advanced Manufacturing Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
