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A GENERALIZED METHOD FOR FEATURIZATION OF MANUFACTURING SIGNALS, WITH APPLICATION TO TOOL CONDITION MONITORING

Published

Author(s)

Max Ferguson, Kincho Law, Raunak Bhinge, Yung-Tsun Lee

Abstract

The application of machine learning techniques in the manufacturing sector provides opportunities for increased production efficiency and product quality. In this paper, we describe how audio and vibration data from a sensor unit can be combined with machine controller data to predict the condition of a milling tool. Emphasis is placed on the generalizability of the method to a range of prediction tasks in a manufacturing setting. Time series audio and acceleration signals are collected from a Computer Numeric Control (CNC) milling machine and discretized into blocks. Fourier transform is employed to create generic power spectrum feature vectors. A Gaussian Process Regression model is then trained to predict the condition of the milling tool from the feature vectors. We highlight that this multi-step procedure could be useful for a range of manufacturing applications where the frequency content of a signal is related to a value of interest.
Proceedings Title
The 37th Computers and Information in Engineering Conference
IDETC2017
Conference Dates
August 6-9, 2017
Conference Location
Cleveland, OH, US

Keywords

Gaussian process regression, machine learning, manufacturing, tool condition

Citation

Ferguson, M. , Law, K. , Bhinge, R. and Lee, Y. (2017), A GENERALIZED METHOD FOR FEATURIZATION OF MANUFACTURING SIGNALS, WITH APPLICATION TO TOOL CONDITION MONITORING, The 37th Computers and Information in Engineering Conference IDETC2017, Cleveland, OH, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922857 (Accessed July 25, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created August 5, 2017, Updated April 6, 2022