Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders

Title
Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders
Authors
Keywords
Stacked sparse autoencoders, Feature extraction, Data fusion, Principal component analysis, Tool wear prediction, Milling
Journal
MEASUREMENT
Volume 190, Issue -, Pages 110719
Publisher
Elsevier BV
Online
2022-01-07
DOI
10.1016/j.measurement.2022.110719

Ask authors/readers for more resources

Reprint

Contact the author

Publish scientific posters with Peeref

Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.

Learn More

Ask a Question. Answer a Question.

Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.

Get Started