Tool Condition Monitoring Using the Electromotive Force from the Chip-Tool Thermocouple

Abstract:

Tool condition monitoring systems are extensively study. However, the machining processes are non-stationary and comprise many details that interfere in its monitoring. Aiming to develop a simple, low cost and efficient tool condition monitoring system, this study analyzed the electromotive force (EMF) from a chip-tool thermocouple in turning tests with AISI 1045. Since EMF comprises time and frequency variations related to machining conditions a Wavelet Packet Transform extracted the signals features from EMF. These signals features fed inputs of a neural network that aimed to evaluate the cutting tool maximum flank wear. The maximum error of the neural network was 1.88% for tested signals. Moreover, EMF showed changes that allow the detection of cutting tool breakage. Therefore, the chip-tool thermocouple may be a promising method for tool condition monitoring. This is the first report of electromotive force analysis in time-frequency domain aiming to quantify the wear of the cutting tool and evaluate its condition.

• Periodical: Applied Mechanics and Materials (Volume 798)
• Pages: 271-275
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.798.271
• Online since: October 2015
• Authors: Rubens Roberto Ingraci Neto*, Renan Luis Fragelli, Arthur Alves Fiocchi, Luiz Eduardo de Angelo Sanchez
• Keywords: Chip-Tool Thermocouple, Neural Network, Tool Condition Monitoring, Turning, Wavelet Packet Transform

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