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
Gaussian process regression, machine learning, manufacturing, tool condition