Parametric investigation of the effects of load level on fatigue crack growth in trabecular bone based on artificial neural network computation

2020 ◽  
Vol 234 (8) ◽  
pp. 784-793
Author(s):  
Marouane El Mouss ◽  
Said Zellagui ◽  
Makrem Nasraoui ◽  
Ridha Hambli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fatigue Crack ◽  
Crack Length ◽  
Cancellous Bone ◽  
Volume Fraction ◽  
Crack Density ◽  
Load Level ◽  
Parametric Investigation ◽  
Artificial Neural

This study reports the development of an artificial neural network computation model to predict the accumulation of crack density and crack length in cancellous bone under a cyclic load. The model was then applied to conduct a parametric investigation into the effects of load level on fatigue crack accumulation in cancellous bone. The method was built in three steps: (1) conducting finite element simulations to predict fatigue growth of different three-dimensional micro-computed tomography cancellous bone specimens considering input combinations based on a factorial experimental design; (2) performing a training stage of an artificial neural network based on the results of step 1; and (3) applying the trained artificial neural network to rapidly predict the crack density and the crack length growth for cancellous bone under a cyclic loading for a given applied apparent strain, cycle frequency, bone volume fraction, bone density and apparent elastic modulus.

An Artificial Neural Network Approach to Fatigue Crack Growth

2011 ◽  
Vol 275 ◽  
pp. 3-6
Author(s):  
K. Zarrabi ◽  
W.W. Lu ◽  
A.K. Hellier
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fatigue Crack ◽  
Crack Length ◽  
Neural Network Approach ◽  
Proposed Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Amplitude Mode ◽  
Artificial Neural Network Approach

This paper proposes a new three-layer artificial neural network (ANN) to predict the fatigue crack length under constant amplitude mode I cyclic loading. It is shown that the proposed model predicts the crack length with an error of less than 0.05%, and more accurately than the current commonly-used models.

Bridging Precipitate Microstructure and Mechanical Hardness of Nickel-Based Superalloy Through Artificial Neural Network Model

2020 ◽  
Author(s):  
Yangping Li ◽  
Yangyi Liu ◽  
Sihua Luo ◽  
Zi Wang ◽  
Ke Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Cooling Rate ◽  
Volume Fraction ◽  
Sem Images ◽  
Mechanical Hardness ◽  
Nickel Based Superalloy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract The attractive mechanical properties of nickel-based superalloys primarily arise from an assembly of γ′ precipitates with desirable size, volume fraction, morphology and spatial distribution. In addition, the solutioning cooling rate after super solvus heat treatment is critical for controlling the features of γ′ precipitates. However, the correlation between these multidimensional parameters and mechanical hardness has not been well established to date. Scanning electron microscope (SEM) images with different γ′ precipitates were investigated in this study, and artificial neural network (ANN) method was used to build a microstructure-mechanical property model. The critical step in this work is to extract different microstructural features from hundreds of SEM images. In order to improve the accuracy of prediction, the cooling rate was also considered as the input. In this work, the methodology was proved to be capable of bridging microstructural features and mechanical properties under the inspiration of material genome spirit.

Prediction of High Cycle Fatigue Property of Ti-6Al-4V Alloy Using Artificial Neural Network

2016 ◽  
Vol 849 ◽  
pp. 360-367
Author(s):  
Ye Man Zhao ◽  
Hong Chao Kou ◽  
Wei Wu ◽  
Ying Deng ◽  
Bin Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cyclic Loading ◽  
Bp Neural Network ◽  
High Cycle Fatigue ◽  
Volume Fraction ◽  
Fatigue Property ◽  
Loading Frequency ◽  
Cycle Fatigue ◽  
Artificial Neural

In this paper, the relationship between microstructure, parameters of cyclic loading and high cycle fatigue property of Ti-6Al-4V alloy was established by artificial neural network (ANN) modeling. The back propagation (BP) neural network and radial basis function (RBF) neural network were established by MATLAB. The input parameters of these models were the primary α volume fraction, primary α size, cyclic loading frequency and stress ratio. The output parameter was high cycle fatigue strength. The neural networks were trained with dataset collected from the literature. The prediction results showed that both of the networks have good generalization ability. In addition, the BP neural network with Levenberg-Merquardt (LM) learning algorithm has better fault tolerance and versatility in dealing with high cycle fatigue property, which is able to predict the high cycle fatigue property with a high accuracy.

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