CdualTAL: multi-domain tool wear prediction using a dual-channel Transformer and cross-attention network

Na LI; Zhendong LIU; Xiao WANG; Jiamin JIANG; Yanjie WEI

doi:10.1631/ENG.ITEE.2025.0111

Your Location：

Home >

Browse articles >

CdualTAL: multi-domain tool wear prediction using a dual-channel Transformer and cross-attention network

Research Articles | Updated：2026-02-11

- CdualTAL: multi-domain tool wear prediction using a dual-channel Transformer and cross-attention network
  Enhanced Publication
- CdualTAL: multi-domain tool wear prediction using a dual-channel Transformer and cross-attention network
- “In the field of manufacturing efficiency, a new algorithm called CdualTAL has been introduced. This improved Transformer-based encoder–attention–decoder algorithm strategically leverages strong and weak features to predict tool wear accurately. Validated on tool wear datasets, CdualTAL outperforms 11 state-of-the-art methods, achieving superior prediction stability and accuracy.”
- ENGINEERING Information Technology & Electronic Engineering Vol. 27, Issue 2, Pages: 65-77(2026)
- Affiliations：
  
  1.School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan 250200, China
  2.Shandong Provincial Key Laboratory of Industrial Big Data and Intelligent Manufacturing, Jinan 250200, China
  3.School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China
  4.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Author bio：
  
  Zhendong LIU, liuzd2000@126.com
- Funds：
- DOI：10.1631/ENG.ITEE.2025.0111
  CLC： TP391.41
- Received：01 November 2025，
  
  Revised：2026-01-31，
  
  Published：23 February 2026
- Accepted：
Scan QR Code
LI Na,LIU Zhendong,WANG Xiao,et al.CdualTAL: multi-domain tool wear prediction using a dual-channel Transformer and cross-attention network[J].ENGINEERING Information Technology & Electronic Engineering,2026,27(02):65-77.
DOI：

LI Na,LIU Zhendong,WANG Xiao,et al.CdualTAL: multi-domain tool wear prediction using a dual-channel Transformer and cross-attention network[J].ENGINEERING Information Technology & Electronic Engineering,2026,27(02):65-77. DOI： 10.1631/ENG.ITEE.2025.0111.

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

An attention mechanism-based multi-domain feature fusion approach for active sonar target recognition

Algorithm for 3D point cloud steganalysis based on composite operator feature enhancement

Attention-based efficient robot grasp detection network

Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

Underwater object detection by fusing features from different representations of sonar data

Related Author

Denghui ZHANG

Shishuo REN

Haoran XU

Tongjing SUN

Suya ZHENG

Hao GONG

Shuai REN

Xuedian ZHANG

Related Institution

School of Automation, Hangzhou Dianzi University

Key Laboratory of Communication Information Transmission and Fusion Technology, Hangzhou Dianzi University

School of Information Engineering, Chang'an University

School of Geological Engineering and Geomatics, Chang'an University

School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology

Intro

Map

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰

CdualTAL: multi-domain tool wear prediction using a dual-channel Transformer and cross-attention network

DOI：10.1631/ENG.ITEE.2025.0111