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

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 290
  • Download : 0
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.
Publisher
AMER SOC TESTING MATERIALS
Issue Date
2018-10
Language
English
Article Type
Article
Citation

SMART AND SUSTAINABLE MANUFACTURING SYSTEMS, v.2, no.1, pp.40 - 63

ISSN
2520-6478
DOI
10.1520/SSMS20180019
URI
http://hdl.handle.net/10203/246204
Appears in Collection
RIMS Journal Papers
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0