Journal Published Online: 15 June 2018
Volume 2, Issue 1

A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data

CODEN: SSMSCY

Abstract

With recent advances in sensor and computing technology, it is now possible to use real-time machine learning techniques to monitor the state of manufacturing machines. However, making accurate predictions from raw sensor data is still a difficult challenge. In this work, a data processing pipeline is developed to predict the condition of a milling machine tool using raw sensor data. Acceleration and audio time series sensor data are aggregated into blocks that correspond to the individual cutting operations of the Computer Numerical Control milling machine. Each block of data is preprocessed using well-known and computationally efficient signal processing techniques. A novel kernel function is proposed to approximate the covariance between preprocessed blocks of time series data. Several Gaussian process regression models are trained to predict tool condition, each with a different covariance kernel function. The model with the novel covariance function outperforms the models that use more common covariance functions. The trained models are expressed using the Predictive Model Markup Language where possible to demonstrate how the predictive model component of the pipeline can be represented in a standardized form. The tool condition model is shown to be accurate, especially when predicting the condition of lightly worn tools.

Author Information

Ferguson, M.
Civil and Environmental Engineering, Stanford University, Y2E2 Building, Stanford, CA, USA
Bhinge, R.
Infinite Uptime, Inc., Berkeley, CA, USA
Park, J.
Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
Lee, Y. T.
Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
Law, K. H.
Civil and Environmental Engineering, Stanford University, Y2E2 Building, Stanford, CA, USA
Pages: 24
Price: Free
Related
Reprints and Permissions
Reprints and copyright permissions can be requested through the
Copyright Clearance Center
Details
Stock #: SSMS20180019
ISSN: 2520-6478
DOI: 10.1520/SSMS20180019