Energy efficiency state identification in milling processes based on information reasoning and Hidden Markov Model

Energy efficiency state identification in milling processes based on information reasoning and... Energy efficiency state identification of milling process plays an important role in energy saving efforts for manufacturing systems. However, it is very difficult to track energy efficiency state in machining processes based on traditional signal processing strategies due to the fact that energy state is usually coupled with a lot of factors like machine tool states, tool conditions, and cutting conditions. An identification method of information reasoning and Hidden Markov model for energy efficiency state is proposed in this paper. Utilizing cutting conditions, empirical models of the energy efficiency, experimental data and signal features, an expert system is established for initial probability optimization and the state is further identified by Hidden Markov model. The experiments show that energy efficiency state can be identified with this method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Cleaner Production Elsevier

Energy efficiency state identification in milling processes based on information reasoning and Hidden Markov Model

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0959-6526
D.O.I.
10.1016/j.jclepro.2018.04.265
Publisher site
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Abstract

Energy efficiency state identification of milling process plays an important role in energy saving efforts for manufacturing systems. However, it is very difficult to track energy efficiency state in machining processes based on traditional signal processing strategies due to the fact that energy state is usually coupled with a lot of factors like machine tool states, tool conditions, and cutting conditions. An identification method of information reasoning and Hidden Markov model for energy efficiency state is proposed in this paper. Utilizing cutting conditions, empirical models of the energy efficiency, experimental data and signal features, an expert system is established for initial probability optimization and the state is further identified by Hidden Markov model. The experiments show that energy efficiency state can be identified with this method.

Journal

Journal of Cleaner ProductionElsevier

Published: Aug 20, 2018

References

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