Predicting tool wear with multi-sensor data using deep belief networks
Predicting tool wear with multi-sensor data using deep belief networks
About this item
Full title
Author / Creator
Chen, Yuxuan , Jin, Yi and Jiri, Galantu
Publisher
London: Springer London
Journal title
Language
English
Formats
Publication information
Publisher
London: Springer London
Subjects
More information
Scope and Contents
Contents
Tool wear is a crucial factor influencing the quality of workpieces in the machining industry. The efficient and accurate prediction of tool wear can enable the tool to be changed in a timely manner to avoid unnecessary costs. Various parameters, such as cutting force, vibration, and acoustic emission (AE), impact tool wear. Signals are collected b...
Alternative Titles
Full title
Predicting tool wear with multi-sensor data using deep belief networks
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2262150996
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2262150996
Other Identifiers
ISSN
0268-3768
E-ISSN
1433-3015
DOI
10.1007/s00170-018-2571-z
