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

Published

June 15, 2018

Author(s)

Max K. Ferguson, Kincho H. Law, Raunak Bhinge, Jinkyoo Park, Yung-Tsun Lee

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 is aggregated into blocks that correspond to the individual cutting operations of the Computer Numerical Control (CNC) 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 (PMML), 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.

Citation

The ASTM Journal of Smart and Sustainable Manufacturing

Volume

2

Issue

1

Pub Type

Journals

Download Paper

https://doi.org/10.1520/SSMS20180019

Local Download

Keywords

Machine learning, Gaussian Process Regression, Tool condition monitoring.

Citation

Ferguson, M. , Law, K. , Bhinge, R. , Park, J. and Lee, Y. (2018), A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data, The ASTM Journal of Smart and Sustainable Manufacturing, [online], https://doi.org/10.1520/SSMS20180019, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923351 (Accessed April 18, 2024)