Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection-Reference-Cited by-同舟云学术

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Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection

Published:2019-03-07 Issue:9-12 Volume:102 Page:4179-4197
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Nasir Vahid,Cool Julie,Sassani Farrokh^ORCID

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

http://link.springer.com/content/pdf/10.1007/s00170-019-03526-3.pdf

Reference46 articles.

1. Nasir V, Cool J (2018) A review on wood machining: characterization, optimization, and monitoring of the sawing process. Wood Mater Sci Eng https://doi.org/10.1080/17480272.2018.1465465

2. Abellan-Nebot JV, Subirón FR (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1–4):237–257

3. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann. Manuf. Technol 59(2):717–739

4. Sutowski P, Święcik R (2018) The estimation of machining results and efficiency of the abrasive electro-discharge grinding process of Ti6Al4V titanium alloy using the high-frequency acoustic emission and force signals. Int J Adv Manuf Technol 94(1–4):1263–1282

5. Li X (2002) A brief review: acoustic emission method for tool wear monitoring during turning. Int J Mach Tools Manuf 42(2):157–165

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