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

Published: August 06, 2017

Author(s)

Max Ferguson, Kincho H. Law, Raunak Bhinge, Yung-Tsun T. 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
Pub Type: Conferences

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Keywords

Gaussian process regression, machine learning, manufacturing, tool condition
Created August 06, 2017, Updated August 11, 2017