The Manipulator Tool Fault Diagnostics Based on Vibration Analysis in the Frequency Domain

Abstract:

The issues presented in the article, relate to the detection of damage of a cutting tool used in robotised machining. Due to the time saving requirements, it is desirable to carry out the current control of the tool mounted in the holder of the robotic manipulator. The tool is a ceramic fiber brush used for grinding. A typical damage of the brush is fiber breakage, which leads to an unbalance of the tool and vibrations. The phenomenon of vibrations and parameters of the vibratory motion of the tool have been used as a carrier of information about the state of the tool. On the basis of the measurement data, obtained during tests of tools with varying degrees of damage, classifiers of the tool state were built. Two types of classifiers were tested: decision trees and artificial neural networks. The results confirm that it is possible to build a classifier of the tool state with high effectiveness reaching up to 99,875%.

• Periodical: Applied Mechanics and Materials (Volume 817)
• Pages: 234-244
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.817.234
• Online since: January 2016
• Authors: Piotr Gierlak*, Marcin Szuster
• Keywords: Decision Tree, Neural Network, Tool Fault Diagnostics, Vibration Analysis

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