Condition monitoring of the cutting process using a self-organizing spiking neural network map

This paper presents a new approach to sensor based condition monitoring using a self-organizing spiking neuron network map. Experimental evidence suggests that biological neural networks, which communicate through spikes, use the timing of these spikes to encode and compute information in a more eff...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2010-12, Vol.21 (6), p.823-829
Main Author: Silva, Rui G.
Format: Article
Language:English
Subjects:
Business and Management
Control
Machines
Manufacturing
Mechatronics
Neural networks
Processes
Production
Robotics
Studies
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Summary:This paper presents a new approach to sensor based condition monitoring using a self-organizing spiking neuron network map. Experimental evidence suggests that biological neural networks, which communicate through spikes, use the timing of these spikes to encode and compute information in a more efficient way. The paper introduces the basis of a simplified version of the Self-Organizing neural architecture based on Spiking Neurons. The fundamental steps for the development of this computational model are presented as well as some experimental evidence of its performance. It is shown that this computational architecture has a greater potential to unveil embedded information in tool wear monitoring data sets and that faster learning occurs if compared to traditional sigmoidal neural networks.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-009-0258-x