A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM

A Novel Tool Wear Identification Method Based on a Semi-Supervised LSTM

Machine learning models have been widely used in the field of cutting tool wear identification, achieving favorable results. However, in actual industrial scenarios, obtaining sufficient labeled samples is time consuming and costly, while unlabeled samples are abundant and easy to collect. This situ...

Full description

Saved in:
Bibliographic Details
Main Authors: Xin He, Meipeng Zhong, Chengcheng He, Jinhao Wu, Haiyang Yang, Zhigao Zhao, Wei Yang, Cong Jing, Yanlin Li, Chen Gao
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Lubricants
Subjects:
tool wear identification
long short-term memory
manifold regularization
semi-supervised learning
Online Access:https://www.mdpi.com/2075-4442/13/2/72
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items