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Real-Time Energy Prediction for a Milling Machine Tool Using Sparse Gaussian Process Regression

Published

Author(s)

Sudarsan Rachuri, Jinkyoo Park, Kincho Law, Raunak Binge, Mason Chen, David Dornfeld

Abstract

This paper describes a real-time data collection framework and an adaptive machining learning method for constructing a real-time energy prediction model for a machine tool. To effectively establish the energy consumption pattern of a machine tool over time, the energy prediction model is continuously updated with new measurement data to account for time-varying effects of the machine tool, such as tool wear and machine tool deterioration. In this work, a real-time data collection and processing framework is developed to retrieve raw data from a milling machine tool and its sensors and convert them into relevant input features. The extracted input features are then used to construct the energy prediction model using Gaussian Process (GP) regression. To update the GP regression model with real-time streaming data, we investigate the use of sparse representation of the covariance matrix to reduce the computational and storage demands of the GP regression. We compare computational efficiency of sparse GP to that of full GP regression model and show the effectiveness of the sparse GP regression model for tracking the variation in the energy consumption pattern of the target machine.
Proceedings Title
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2015)
Conference Dates
October 29-November 1, 2015
Conference Location
Santa Clara , CA, US
Conference Title
2015 IEEE International Conference on Big Data (IEEE BigData 2015)

Keywords

machine learning, Sparse Gaussian regression, energy prediction model, milling machine, MTConnect

Citation

Rachuri, S. , Park, J. , Law, K. , Binge, R. , Chen, M. and Dornfeld, D. (2015), Real-Time Energy Prediction for a Milling Machine Tool Using Sparse Gaussian Process Regression, Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara , CA, US, [online], https://doi.org/10.1109/BigData.2015.7363906 (Accessed April 19, 2024)
Created December 28, 2015, Updated December 10, 2022