Application of Random Threshold Neural Networks for Diagnostics of Micro Machine Tool Condition
Micro equipment based manufacturing requires a higher automation level than traditional manufacturing, because a human operator should be able to supervise the parallel operation of many micro equipment units. We briefly describe the prototypes of micro machine tools and the random threshold neural network classifiers created by us for the experimental investigation of problems on the way to fully automated micro manufacturing. The problems of diagnostics of micro machine tools and machining modes are discussed as well as the results of experiments with the acoustic diagnostics of cutting modes using the random threshold classifier.
E. M. Kussul et al., "Application of Random Threshold Neural Networks for Diagnostics of Micro Machine Tool Condition," Proceedings of the IEEE World Congress on Computational Intelligence (1998, Anchorage, AK), vol. 1, pp. 241 - 244, Institute of Electrical and Electronics Engineers (IEEE), May 1998.
The definitive version is available at https://doi.org/10.1109/IJCNN.1998.682270
IEEE World Congress on Computational Intelligence, WCCI'98 / INNS International Joint Conference on Neural Networks '98 (1998: May 4-9, Anchorage, AK)
Electrical and Computer Engineering
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Article - Conference proceedings
© 1998 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
09 May 1998