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Energy Efficiency State Identification in Milling Processing Based on Improved HMM

[+] Author Affiliations
Yun Cai, Hua Shao

Shanghai Jiao Tong University, Shanghai, China

Paper No. MSEC2017-2735, pp. V002T03A011; 11 pages
doi:10.1115/MSEC2017-2735
From:
  • ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
  • Volume 2: Additive Manufacturing; Materials
  • Los Angeles, California, USA, June 4–8, 2017
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5073-2
  • Copyright © 2017 by ASME

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 (HMM) 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 HMM. The experiments show that energy efficiency state can be identified with this method.

Copyright © 2017 by ASME

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