Cargando…

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is use...

Descripción completa

Detalles Bibliográficos
Autores principales:	Hijazi, Ala, Al-Dahidi, Sameer, Altarazi, Safwan
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2020
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698861/ https://www.ncbi.nlm.nih.gov/pubmed/33218153 http://dx.doi.org/10.3390/ma13225216

Ejemplares similares

Machine Learning Models for Predicting and Classifying the Tensile Strength of Polymeric Films Fabricated via Different Production Processes
por: Altarazi, Safwan, et al.
Publicado: (2019)

Short-Time Wind Speed Forecast Using Artificial Learning-Based Algorithms
por: Ibrahim, Mariam, et al.
Publicado: (2020)

Optimization of Geometry Parameters of Inkjet-Printed Silver Nanoparticle Traces on PDMS Substrates Using Response Surface Methodology
por: Abu-Khalaf, Jumana, et al.
Publicado: (2019)

Residual Compressive Strength of Short Tubular Steel Columns with Artificially Fabricated Local Corrosion Damage
por: Toledo, Kyra Kamille, et al.
Publicado: (2020)

Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method
por: Perfetto, Donato, et al.
Publicado: (2021)

Site-Selective Artificial Ribonucleases: Oligonucleotide Conjugates Containing Multiple Imidazole Residues in the Catalytic Domain
por: Beloglazova, Natalia G., et al.
Publicado: (2011)

To Line Artificial Dentures with Aluminum
por: La Salle, Clint W.
Publicado: (1904)

Numerical Investigation into In-Plane Crushing of Tube-Reinforced Damaged 5052 Aerospace Grade Aluminum Alloy Honeycomb Panels
por: Djemaoune, Younes, et al.
Publicado: (2021)

Optimization of Aluminum Stressed Skin Panels in Offshore Applications
por: van Hove, Dianne, et al.
Publicado: (2014)

Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys
por: Merayo, David, et al.
Publicado: (2020)

Aluminum exposure from food in the population of Lebanon
por: EL Daouk, Sarine, et al.
Publicado: (2020)

Entropy Generation Optimization for Rarified Nanofluid Flows in a Square Cavity with Two Fins at the Hot Wall
por: Al-Kouz, Wael, et al.
Publicado: (2019)

Aluminum as a Base for Artificial Dentures
por: Hipple, A. Hugh
Publicado: (1890)

Aluminum as a Base for Artificial Teeth
por: Furman, S. M.
Publicado: (1874)

Aluminum as a Base for Artificial Teeth
por: Hodgen, J. A.
Publicado: (1870)

Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling (†)
por: Wiciak-Pikuła, Martyna, et al.
Publicado: (2020)

Operational Load Monitoring of a Composite Panel Using Artificial Neural Networks
por: Mucha, Waldemar, et al.
Publicado: (2020)

A targeted sequencing panel identifies rare damaging variants in multiple genes in the cranial neural tube defect, anencephaly
por: Ishida, M., et al.
Publicado: (2018)

Aluminum V. Vulcanite. An Improvement in Artificial Teeth
por: Percy, Sam'l R.
Publicado: (1868)

Investigation of Energy Absorbed by Composite Panels with Honeycomb Aluminum Alloy Core
por: Mogilski, Maciej, et al.
Publicado: (2020)

Experimental and Numerical Study on the Strength of Aluminum Extrusion Welding
por: Bingöl, Sedat, et al.
Publicado: (2015)

Training high-strength aluminum alloys to withstand fatigue
por: Zhang, Qi, et al.
Publicado: (2020)

Iron and aluminum homeostasis in neural disorders.
por: Joshi, J G, et al.
Publicado: (1994)

Evaluation of Residual Compressive Strength and Behavior of Corrosion-Damaged Carbon Steel Tubular Members
por: Ahn, Jin-Hee, et al.
Publicado: (2018)

Study on Low-Velocity Impact Damage and Residual Strength of Reinforced Composite Skin Structure
por: Li, Hanhua, et al.
Publicado: (2020)

Application of artificial neural network modeling techniques to signal strength computation
por: Igwe, K.C., et al.
Publicado: (2021)

Enhancement of quasi-static compression strength for aluminum closed cell foam blocks shielded by aluminum tubes
por: Dadoura, Mohamed H., et al.
Publicado: (2023)

NDT of Residual Stress in Thick Aluminum Alloy Plates under Different Aging Conditions Using Multiple Techniques
por: Li, Zhengyi, et al.
Publicado: (2022)

Electromagnetic Force on an Aluminum Honeycomb Sandwich Panel Moving in a Magnetic Field
por: Yu, Yunfeng, et al.
Publicado: (2023)

Analysis of Unconfined Compressive Strength of Rammed Earth Mixes Based on Artificial Neural Network and Statistical Analysis
por: Mustafa, Yassir Mubarak Hussein, et al.
Publicado: (2022)

Multiple Fingerprinting Localization by an Artificial Neural Network
por: Yoo, Jaehyun
Publicado: (2022)

Nonlinear Fatigue Damage Model of Asphalt Mixture Based on Dynamic Modulus and Residual Strength Decay
por: Liu, Hongfu, et al.
Publicado: (2019)

Hierarchical nanostructured aluminum alloy with ultrahigh strength and large plasticity
por: Wu, Ge, et al.
Publicado: (2019)

The Effect of Tool Geometry on the Strength of FSW Aluminum Thin Sheets
por: Vairis, Achilles, et al.
Publicado: (2022)

Surface Treatments for Enhancing the Bonding Strength of Aluminum Alloy Joints
por: Luo, Juncheng, et al.
Publicado: (2023)

Predicting the protein localization sites using artificial neural networks
por: Arulmozhi, V, et al.
Publicado: (2013)

Modelling the Influence of Waste Rubber on Compressive Strength of Concrete by Artificial Neural Networks
por: Hadzima-Nyarko, Marijana, et al.
Publicado: (2019)

Prediction of the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network
por: Bu, Liangtao, et al.
Publicado: (2021)

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network
por: Amar, Mouhamadou, et al.
Publicado: (2022)

Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP
por: Kim, Younghwan, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_pe2fd7huv5ue5nldlepigvdesp