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An application of Fuzzy ARTMAP neural network to real-time learning and prediction of time-variant machine tool error maps

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2 Author(s)
Srinivasa, N. ; Dept. of Mech. Eng., Florida Univ., Gainesville, FL, USA ; Ziegert, J.C.

The problem of real-time learning of thermal error maps in machine tools is investigated. This problem is treated as an incremental approximation of a functional mapping between thermal sensor readings and the associated positional errors at each location of the cutting tool. The Fuzzy ARTMAP is used as a tool to achieve this approximation in real-time. Experimental measurements of the positional errors for a turning center were performed using a laser ball-bar over two separate thermal duty cycles. The Fuzzy ARTMAP was trained online using the data collected during the first duty cycle. Data from a new duty cycle is used to test the performance of the trained network. Results show that the Fuzzy ARTMAP is not only able to learn thermal errors in real-time but can also make accurate predictions of the test data

Published in:

Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on

Date of Conference:

26-29 Jun 1994