In-process drilling-states monitoring in machining centre by time series analysis

Gengsheng S. Li; W. S. Lau; Y. Z. Zhang; C. Y. Jiang

doi:10.1117/12.138480

18 January 1993 In-process drilling-states monitoring in machining centre by time series analysis

Gengsheng S. Li, W. S. Lau, Y. Z. Zhang, C. Y. Jiang

Author Affiliations +

Proceedings Volume 1713, International Conference on Manufacturing Automation; (1993) https://doi.org/10.1117/12.138480
Event: International Conference on Manufacturing Automation, 1992, Hong Kong, Hong Kong

Abstract

Monitoring of the cutting process is one of the most crucial and difficult problems in unmanned machining technology. In this paper, the effects to the thrust and the torque in drilling by some abnormal cutting states have been investigated. Experiments show that the dynamic components of the thrust and the torque provide useful information for identifying different tool failures. Reasonable explanations have been given for the phenomena from physical mechanisms. A mathematical model for the dynamic cutting process was proposed and some problems about the estimation of the time series AR(n) model were also discussed. After having compared several defined monitoring parameters, a new inprocess signal processing method, `adaptive variance-residual recursive algorithm,' based on least squares theory has been suggested with which signal signatures of cutting states can be extracted effectively. It bears the advantages of rapid response, less amount of calculation, and easy to set deterministic thresholds. The in-process monitoring system developed shows a bright prospect in practical application.

Citation Download Citation

Gengsheng S. Li, W. S. Lau, Y. Z. Zhang, and C. Y. Jiang "In-process drilling-states monitoring in machining centre by time series analysis", Proc. SPIE 1713, International Conference on Manufacturing Automation, (18 January 1993); https://doi.org/10.1117/12.138480

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE