Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation
Accurately identifying tool wear states with variable cutting parameters can improve machining quality and efficiency. However, existing wear state recognition methods based on unsupervised domain adaptation mostly employ the knowledge transfer learning strategy in a single source domain. They canno...
Saved in:
| Main Authors: | Zhigang Cai, Wangyang Li, Jianxin Song, Hongyu Jin, Hongya Fu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/6/1742 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Numerical Model of Cutting Tool Blade Wear
by: Shvets S. V., et al.
Published: (2021-12-01) -
Modelling of cutting force relationship as a function of cutting conditions and tool wear
by: Pavel Kovač, et al.
Published: (2025-03-01) -
Image Based Detection of Coating Wear on Cutting Tools with Machine Learning
by: Jan Wolf, et al.
Published: (2024-12-01) -
Improvement of the Physical and Mechanical Properties of the Cutting Tool by Applying Wear-resistant Coatings Based on Ti, Al, Si, and N
by: Hovorun T., et al.
Published: (2021-12-01) -
GAN-based unsupervised domain adaptive person re-identification
by: Shengsheng ZHENG, et al.
Published: (2021-02-01)