Fatigue damage detection of aerospace-grade aluminum alloys using feature-based and feature-less deep neural networks-Reference-Cited by-同舟云学术

Fatigue damage detection of aerospace-grade aluminum alloys using feature-based and feature-less deep neural networks

Published:2022-03 Issue: Volume:7 Page:100247
ISSN:2666-8270
Container-title:Machine Learning with Applications
language:en
Short-container-title:Machine Learning with Applications

Author:

Dharmadhikari Susheel,Basak Amrita^ORCID

Funder

Department of Mechanical Engineering, College of Engineering, Michigan State University

Pennsylvania State University

Publisher

Elsevier BV

Subject

General Medicine

Reference41 articles.

1. Estimating damage size and remaining useful life in degraded structures using deep learning-based multi-source data fusion;Aria;Structural Health Monitoring,2020

2. E466-15, standard practice for conducting force controlled constant amplitude axial fatigue tests of metallic materials;ASTM;ASTM International,2015

3. Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review;Azimi;Sensors,2020

4. The state of the art of data science and engineering in structural health monitoring;Bao;Engineering,2019

5. Early detection of fatigue crack damage in ductile materials: A projection-based probabilistic finite state automata approach;Bhattacharya;ASME Letters in Dynamic Systems and Control,2021

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