GMDH-Kalman Filter prediction of high-cycle fatigue life of drilled industrial composites: A hybrid machine learning with limited data-Reference-Cited by-同舟云学术

GMDH-Kalman Filter prediction of high-cycle fatigue life of drilled industrial composites: A hybrid machine learning with limited data

Published:2023-04 Issue: Volume:216 Page:119425
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Khayyam Hamid,Shahkhosravi Naeim Akbari,Jamali Ali,Naebe Minoo,Kafieh Rahele,Milani Abbas S.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference32 articles.

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3. Fatigue life reduction of GFRP composites due to delamination associated with the introduction of functional discontinuities;Akbari Shahkhosravi;Composites Part B: Engineering,2019

4. SHyFTOO, an object-oriented Monte Carlo simulation library for the modeling of stochastic hybrid fault tree automaton;Chiacchio;Expert Systems with Applications,2020

5. A machine learning framework with dataset-knowledgeability pre-assessment and a local decision-boundary crispness score: an industry 4.0-based case study on composite autoclave manufacturing;Crawford;Computers in Industry,2021

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