Retraction
Segmentation and quantitative evaluation for tool wear condition via an improved SE-U-Net
Paper Information
Record ID:
65973
Author(s):
Journal:
Publication Date:
May 16, 2022
Retraction Date:
May 13, 2024
(1.5 year years ago)
Subjects:
Institutions:
Country:
🇨🇳 ChinaArticle Type:
Publisher:
Springer - Nature Publishing Group
Open Access:
Yes
PubMed ID:
Not indexed in PubMed
Retraction PubMed ID:
Not indexed in PubMed
Retraction Details
Citations (6)
6
Total Citations0
Post-Retraction5
Pre-Retraction0
Same DayHeavy Metals Removal Using Carbon Based Nanocomposites
Unknown Authors
Unknown Journal
Published: Unknown
Semi-supervised auxiliary learning for surface defect detection and segmentation of injection-molded products from small image datasets
Zian Yu, Yiming Zhang, Shuyou Zhang et al. (5 authors)
The International Journal of Advanced Manufacturing Technology
Published: Mar 2024
2 citations
2 citations
70 days before retraction
Research on Classification and Recognition of Micro Milling Tool Wear Based on Improved DenseNet
Zhaoxiang Li, Zhanjiang Yu, Yiquan Li et al. (6 authors)
2022 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)
Published: Jul 2023
287 days before retraction
Semantic segmentation of end mill wear area based on transfer learning with small dataset
Chang Chen, Lin Chen, Zhen Meng et al. (6 authors)
The International Journal of Advanced Manufacturing Technology
Published: Jun 2023
2 citations
2 citations
332 days before retraction
An improved UNet model based on adaptive activation function and squeeze-and-excitation module for milling tool wear segmentation
Canyu Cai, Zhichao You, Changgen Li et al. (6 authors)
Unknown Journal
Published: May 2023
1 citation
1 citation
378 days before retraction
Classification of Tool Wear State based on Dual Attention Mechanism Network
Jiaqi Zhou, Caixu Yue, Xianli Liu et al. (8 authors)
Robotics and Computer-Integrated Manufacturing
Published: Apr 2023
20 citations
20 citations
381 days before retraction
Quick Stats
Total Citations:
12
Years Since Retraction:
1.5 year
Open Access:
Yes
Last Checked:
Jul 24, 2025