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

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 use...

Full description

Saved in:
Bibliographic Details
Published inProceedings of 1994 IEEE 3rd International Fuzzy Systems Conference pp. 1725 - 1730 vol.3
Main Authors Srinivasa, N., Ziegert, J.C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1994
Subjects
Cutting tools
Fuzzy neural networks
Machine learning
Machine tools
Neural networks
Performance evaluation
Position measurement
Testing
Thermal sensors
Turning
Online AccessGet full text

Cover

More Information
Summary: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.< >
ISBN:078031896X
9780780318960
DOI:10.1109/FUZZY.1994.343596