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An automated feature extraction method with application to empirical model development from machining power data

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journal contribution
posted on 22.02.2019, 15:17 by Dimitrios Pantazis, Paul GoodallPaul Goodall, Paul ConwayPaul Conway, Andrew WestAndrew West
Machining shop floor jobs are rarely optimised for minimisation of the energy consumption, as no clear guidelines exist in operating procedures and high production rates and finishing quality are requirements with higher priorities. However, there has been an increased interest recently in more energy-efficient process designs, due to new regulations and increases in energy charges. Response Surface Methodology (RSM) is a popular procedure using empirical models for optimising the energy consumption in cutting operations, but successful deployment requires good understanding of the methods employed and certain steps are time-consuming. In this work, a novel method that automates the feature extraction when applying RSM is presented. Central to the approach is a continuous Hidden Markov model, where the probability distribution of the observations at each state is represented by a mixture of Gaussian distributions. When applied to a case study, the automated extracted material cutting energies lay within 1.12% of measured values and the spindle acceleration energies within 3.33% of their actual values.

Funding

This project was supported by the EPSRC through the project Adaptive Informatics for Intelligent Manufacturing (EP/K014137/1) and the Centre for Doctoral Training in Embedded Intelligence (EP/L014998/1).

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Mechanical Systems and Signal Processing

Volume

124

Pages

21 - 35

Citation

PANTAZIS, D. ... et al, 2019. An automated feature extraction method with application to empirical model development from machining power data. Mechanical Systems and Signal Processing, 124, pp.21-35.

Publisher

Elsevier © The Authors

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

11/01/2019

Publication date

2019-02-01

Notes

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

ISSN

0888-3270

Language

en