A Customized Neural Network for Sensor Fusion in On-Line Monitoring of Cutting Tool Wear-Reference-Cited by-同舟云学术

A Customized Neural Network for Sensor Fusion in On-Line Monitoring of Cutting Tool Wear

Published:1995-05-01 Issue:2 Volume:117 Page:152-159
ISSN:0022-0817
Container-title:Journal of Engineering for Industry
language:en
Short-container-title:

Author:

Leem Choon Seong¹,Dornfeld D. A.²,Dreyfus S. E.³

Affiliation:

1. Dept. of Industrial Engineering, Rutgers University, Piscataway, NJ

2. Dept. of Mechanical Engineering, University of California at Berkeley, Berkeley, CA

3. Dept. of Industrial Engineering and Operations Research, University of California at Berkeley, Berkeley, CA

Abstract

A customized neural network for sensor fusion of acoustic emission and force in on-line detection of tool wear is developed. Based on two critical concerns regarding practical and reliable tool-wear monitoring systems, the maximal utilization of “unsupervised” sensor data and the avoidance of off-line feature analysis, the neural network is trained by unsupervised Kohonen’s Feature Map procedure followed by an Input Feature Scaling algorithm. After levels of tool wear are topologically ordered by Kohonen’s Feature Map, input features of AE and force sensor signals are transformed via Input Feature Scaling so that the resulting decision boundaries of the neural network approximate those of error-minimizing Bayes classifier. In a machining experiment, the customized neural network achieved high accuracy rates in the classification of levels of tool wear. Also, the neural network shows several practical and reliable properties for the implementation of the monitoring system in manufacturing industries.

Publisher

ASME International

Subject

General Medicine

Link

http://asmedigitalcollection.asme.org/manufacturingscience/article-pdf/117/2/152/6494018/152_1.pdf

Reference23 articles.

1. Altintas, Y., and Yellowley, I., 1987, “In-Process Detection of Tool Failure in Milling Using Cutting Force Models,” Sensors for Manufacturing, ASME, New York, pp. 1–16.

2. Burke, L. I., 1989, “Automated Identification of Tool Wear States in Machining Processes: An Application of Self-Organizing Neural Networks,” Ph.D. Thesis, University of California, Berkeley.

3. Carpenter G. , and GrossbergS., 1987, “ART2: Self-Organization of Stable Category Recognition Codes for Analog Input Patterns,” Applied Optics, Vol. 26, pp. 4919–4946.

4. Chryssolouris, G., and Domroese, M., 1988, “Sensor Integration for Tool Wear Estimation in Machining,” Proceedings of the Winter Annual Meeting of the ASME, Symposium on Sensors and Controls for Manufacturing, pp. 115–123.

5. Dornfeld, D. A., 1992a, “Monitoring of Machining Processes-Literature Review,” presented at CIRP STC “C” meeting, Paris, January.

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