Estimation of low-cycle fatigue damage of sputtered Cu thin films at the micro scale using deep learning
Author:
Publisher
Elsevier BV
Subject
Electrical and Electronic Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering
Reference24 articles.
1. Deformation behavior of thin copper films on deformable substrates;Hommel;Acta Mater,2001
2. The ‘size effect’ on the stress–strain, fatigue and fracture properties of thin metallic foils;Klein;Mater Sci Eng A,2001
3. Thermomechanical behavior of different texture components in cu thin films;Baker;Acta Mater,2001
4. Texture transition in cu thin films: Electron backscatter diffraction vs. X-ray diffraction;Sonnweber-Ribic;Acta Mater,2006
5. Electrical failure and damage analysis of multi-layer metal films on flexible substrate during cyclic bending deformation;Kim,2011
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1. A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection;Journal of Imaging;2023-09-25
2. Few-shot fatigue damage evaluation of aircraft structure using neural augmentation and deep transfer learning;Engineering Failure Analysis;2023-06
3. Prediction of low-cycle fatigue crack development of sputtered Cu thin film using deep convolutional neural network;International Journal of Fatigue;2022-09
4. Numerical simulation of fatigue behavior of flexible metal films in multiphysics fields;Fatigue & Fracture of Engineering Materials & Structures;2022-08-17
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