Cross-sectional performance prediction of metal tubes bending with tangential variable boosting based on parameters-weight-adaptive CNN-Reference-Cited by-同舟云学术

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Cross-sectional performance prediction of metal tubes bending with tangential variable boosting based on parameters-weight-adaptive CNN

Published:2024-03 Issue: Volume:237 Page:121465
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Xiang Yongzhe,Wang Zili^ORCID,Zhang Shuyou,Jiang Lanfang,Lin Yaochen,Tan Jianrong

Funder

Zhejiang Province Public Welfare Technology Application Research Project

State Key Laboratory of Fluid Power and Mechatronic Systems

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference40 articles.

1. A comprehensive review of recent advances on deep vision systems;Abbas;Artificial Intelligence Review,2019

2. Spring-back and residual stresses in bending of thin-walled aluminum tubes;Al-Qureshi;Materials & Design,2002

3. Visual inspection of fault type and zone prediction in electrical grids using interpretable spectrogram-based CNN modeling;Ardito;Expert Systems with Applications,2022

4. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. CVPR (pp. 1251-1258). 10.48550/arXiv.1610.02357.

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1. Digital-Twin virtual model real-time construction via spatio-temporal cascade reconstruction for full-field plastic deformation monitoring in metal tube bending manufacturing;Robotics and Computer-Integrated Manufacturing;2025-02

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