Detection of tool breakage in turning operations by using neural network

Yongyue Zhang; Kunhua Zhang; Zhijun Han

doi:10.1117/12.217533

28 August 1995 Detection of tool breakage in turning operations by using neural networks

Yongyue Zhang, Kunhua Zhang, Zhijun Han

Proceedings Volume 2620, International Conference on Intelligent Manufacturing; (1995) https://doi.org/10.1117/12.217533
Event: International Conference on Intelligent Manufacturing, 1995, Wuhan, China

Abstract

In this research, supervised and unsupervised neural network systems are used to detect tool breakage in turning operations. Before applying the neural network, the sensory signals of the cutting force are processed in time domain into more representative forms for the neural network to make the decision correctly. The back propagation (BP) network must be trained with samples of measurements taken at tool breakage. Utilizing feature mapping, the Kohonen's self-organizing network adapts the prototype values but cannot be used effectively on-line. Only relying on the normal category, the single category-based classifier (SCBC) adapts weight values on-line so as to continuously update the normal category. Extensive tests prove that the SCBC network correctly categorized 92% of the presented experimental data.

Citation Download Citation

Yongyue Zhang, Kunhua Zhang, and Zhijun Han "Detection of tool breakage in turning operations by using neural networks", Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); https://doi.org/10.1117/12.217533

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

Includes PDF, HTML & Video, when available