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.

Based on BP Neural Network Discrete Data Forecast

2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Bp Neural Network ◽  
Model Fitting ◽  
Predictive Ability ◽  
Discrete Data ◽  
Engineering System ◽  
Forecast Performance ◽  
Artificial Neural

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

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.

High-Cycle Fatigue and Thermal Dissipation Investigations for Low Carbon Steel Q345

2015 ◽  
Vol 664 ◽  
pp. 305-313 ◽  
Author(s):  
Han Qing Liu ◽  
Qing Yuan Wang ◽  
Zhi Yong Huang ◽  
Zhen Jie Teng
Keyword(s):  
Carbon Steel ◽  
High Cycle Fatigue ◽  
Fatigue Testing ◽  
Low Carbon Steel ◽  
Infrared Camera ◽  
Fatigue Property ◽  
Thermal Dissipation ◽  
Loading Frequency ◽  
Cycle Fatigue ◽  
Low Carbon

Carbon steel is a kind of metallic material that widely used in construction, machinery, manufacturing and other domains. In the mechanical structure system, long-term cyclic stress may cause the mechanical components failure. In this work, the characteristic of fatigue crack propagate in low carbon steel Q345 and the effect of loading frequency to the fatigue property of Q345 steel were investigated. Meanwhile, the dispersion of high-cycle fatigue of life of the Q345 steel under high fatigue testing frequency was analyzed, and the P-S-N curve with the test data was given out. With the help of infrared camera, temperature rise curve during fatigue test was analyzed to study the thermal dissipation of Q345 steel.

Resistivity-depth imaging of airborne transient electromagnetic method based on an artificial neural network

2020 ◽  
Author(s):  
Jifeng Zhang ◽  
Bing Feng ◽  
Dong Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Electromagnetic Response ◽  
Model Parameters ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic ◽  
Artificial Neural

<p>An artificial neural network, which is an important part of artificial intelligence, has been widely used to many fields such as information processing, automation and economy, and geophysical data processing as one of the efficient tools. However, the application in geophysical electromagnetic method is still relatively few. In this paper, BP neural network was combined with airborne transient electromagnetic method for imaging subsurface geological structures.</p><p>We developed an artificial neural network code to map the distribution of geologic conductivity in the subsurface for the airborne transient electromagnetic method. It avoids complex derivation of electromagnetic field formula and only requires input and transfer functions to obtain the quasi-resistivity image section. First, training sample set, which is airborne transient electromagnetic response of homogeneous half-space models with the different resistivity, is formed and network model parameters include the flight altitude and the time constant, which were taken as input variables of the network, and pseudo-resistivity are taken as output variables. Then, a double hidden layer BP neural network is established in accordance with the mapping relationship between quasi-resistivity and airborne transient electromagnetic response. By analyzing mean square error curve, the training termination criterion of BP neural network is presented. Next, the trained BP neural network is used to interpret the airborne transient electromagnetic responses of various typical layered geo-electric models, and it is compared with those of the all-time apparent resistivity algorithm. After a lot of tests, reasonable BP neural network parameters were selected, and the mapping from airborne TEM quasi-resistivity was realized. The results show that the resistivity imaging from BP neural network approach is much closer to the true resistivity of model, and the response to anomalous bodies is better than that of all-time apparent resistivity numerical method. Finally, this imaging technique was use to process the field data acquired by the airborne transient method from Huayangchuan area. Quasi-resistivity depth section calculated by BP neural network and all-time apparent resistivity is in good agreement with the actual geological situation, which further verifies the effectiveness and practicability of this algorithm.</p>

Resistivity-depth Imaging with the Airborne Transient Electromagnetic Method Based on an Artificial Neural Network

2020 ◽  
Vol 25 (3) ◽  
pp. 355-368
Author(s):  
Bing Feng ◽  
Ji-feng Zhang ◽  
Dong Li ◽  
Yang Bai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Electromagnetic Response ◽  
Model Parameters ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic ◽  
Artificial Neural

We developed an artificial neural network to map the distribution of geologic conductivity in the earth subsurface using the airborne transient electromagnetic method. The artificial neural network avoids the need for complex derivations of electromagnetic field formulas and requires only input and transfer functions to obtain a quasi-resistivity image. First, training sample set from the airborne transient electromagnetic response of homogeneous half-space models with different resistivities was formed, and network model parameters, including the flight altitude, time constant, and response amplitude, were determined. Then, a double-hidden-layer back-propagation (BP) neural network was established based on the mapping relationship between quasi-resistivity and airborne transient electromagnetic response. By analyzing the mean square error curve, the training termination criterion of the BP neural network was determined. Next, the trained BP neural network was used to interpret the airborne transient electromagnetic responses of various typical layered geo-electric models, and the results were compared with that from the all-time apparent resistivity algorithm. The comparison indicated that the resistivity imaging from the BP neural network approach was much closer to the true resistivity of the model, and the response to anomalous bodies was better than that from an all-time apparent resistivity. Finally, this imaging technique was used to process field data acquired by employing the airborne transient method from the HuaYin survey area. Quasi-resistivity depth sections calculated with the BP neural network and the actual geological situation were in good.

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