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 transformation 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.

Volume Subject Area:
37th Computers and Information in Engineering Conference
Topics:
Condition monitoring, Manufacturing, Signals, Milling, Computer numerical control machine tools, Computers, Control equipment, Fourier transforms, Machine learning, Machinery, Milling machines, Product quality, Regression models, Sensors, Time series, Vibration
This content is only available via PDF.
Copyright © 2017 by ASME
You do not currently have access to this content.