scholarly journals Neural network modeling of the rheology of the AlMg6 alloy under the dispersoid barrier effect and the inhibition of dynamic relaxation processes

2020 ◽  
pp. 10-26
Author(s):  
A. S. Smirnov ◽  
◽  
A. V. Konovalov ◽  
V. S. Kanakin ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Strain Rate ◽  
Strain Rate Range ◽  
Neural Network Modeling ◽  
Relaxation Processes ◽  
Dynamic Relaxation ◽  
Rate Range ◽  
The Neural Network ◽  
Hidden Layer ◽  
Temperature Strain

The paper deals with a neural network to model the flow stress of the AlMg6 alloy at temperatures ranging between 300 and 500 C and strain rates from 1 to 25 s−1. In this temperature–strain-rate range, the movement of free dislocations is blocked and dynamic relaxation processes are inhibited. The results of training the neural network and its verification at a temperature not used in the training show that neural networks with a single hidden layer can correctly approximate and predict the rheological behavior of the AlMg6 alloy for the studied temperature–strain-rate range of deformation.

Implementation of a Neural Network into a User-Material Subroutine for Finite Element Simulation of Material Viscoplasticity

2021 ◽  
pp. 1-24
Author(s):  
Lahouari Benabou
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Strain Rate ◽  
Strain Rate Range ◽  
Material Behavior ◽  
Transient Effects ◽  
Data Set ◽  
User Subroutine ◽  
The Neural Network ◽  
Element Simulation

Abstract In this study, a neural network is trained to predict the response of a viscoplastic solder alloy based on a reduced data set. The model is shown to accurately describe the behavior of the material for the temperature range from 298°K to 398°K and the strain rate range from 2e-5 sec−1 to 2e-2 sec−1. The model is then implemented in the form of a user subroutine in the finite element code Abaqus to be used for simulations of the material behavior. The implementation requires that the weights and biases of the network are extracted and that its gradients (derivatives of the output with respect to the inputs) are calculated to be passed on to the user subroutine. FE simulations based on the implemented neural network are compared with those based on the physical viscoplastic model of Anand, showing an overall good agreement between both approaches. However, some limitations concerning the neural network ability to predict the transient effects during a strain rate jump or a temperature change are identified and discussed.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

scholarly journals Advanced thyroid carcinomas: neural network analysis of ultrasonographic characteristics

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael Cordes ◽  
Theresa Ida Götz ◽  
Elmar Wolfgang Lang ◽  
Stephan Coerper ◽  
Torsten Kuwert ◽  
...  
Keyword(s):  
Neural Network ◽  
Imaging Modality ◽  
Follicular Carcinoma ◽  
Predictive Values ◽  
Thyroid Carcinomas ◽  
The Neural Network ◽  
Group 2 ◽  
Line Imaging ◽  
Hidden Layer ◽  
Group 1

Abstract Background Ultrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain characteristics observable by ultrasound have recently been identified that may indicate malignancy. This retrospective cohort study was conducted to test the hypothesis that advanced thyroid carcinomas show distinctive clinical and sonographic characteristics. Using a neural network model as proof of concept, nine clinical/sonographic features served as input. Methods All 96 study enrollees had histologically confirmed thyroid carcinomas, categorized (n = 32, each) as follows: group 1, advanced carcinoma (ADV) marked by local invasion or distant metastasis; group 2, non-advanced papillary carcinoma (PTC); or group 3, non-advanced follicular carcinoma (FTC). Preoperative ultrasound profiles were obtained via standardized protocols. The neural network had nine input neurons and one hidden layer. Results Mean age and the number of male patients in group 1 were significantly higher compared with groups 2 (p = 0.005) or 3 (p <  0.001). On ultrasound, tumors of larger volume and irregular shape were observed significantly more often in group 1 compared with groups 2 (p <  0.001) or 3 (p ≤ 0.01). Network accuracy in discriminating advanced vs. non-advanced tumors was 84.4% (95% confidence interval [CI]: 75.5–91), with positive and negative predictive values of 87.1% (95% CI: 70.2–96.4) and 92.3% (95% CI: 83.0–97.5), respectively. Conclusions Our study has shown some evidence that advanced thyroid tumors demonstrate distinctive clinical and sonographic characteristics. Further prospective investigations with larger numbers of patients and multicenter design should be carried out to show whether a neural network incorporating these features may be an asset, helping to classify malignancies of the thyroid gland.

Deformation resistance and fracture of iron over a wide strain rate range

2014 ◽  
Vol 56 (8) ◽  
pp. 1569-1573 ◽  
Author(s):  
G. I. Kanel ◽  
S. V. Razorenov ◽  
G. V. Garkushin ◽  
S. I. Ashitkov ◽  
P. S. Komarov ◽  
...  
Keyword(s):  
Strain Rate ◽  
Strain Rate Range ◽  
Deformation Resistance ◽  
Rate Range

Neuron Optimization Based PID Approach for Cutting Force Control

2006 ◽  
Vol 315-316 ◽  
pp. 85-89
Author(s):  
S. Jiang ◽  
Yan Shen Xu ◽  
J. Wu
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Open Loop ◽  
Network Control ◽  
Neural Network Control ◽  
System A ◽  
The Neural Network ◽  
Nc Milling ◽  
Hidden Layer ◽  
Control Principle

To improve the cutting efficiency, one of key approaches is to control with constant force in the full depth working condition. And the controller design is vital to realize the real-time feasibility and robustness of the system. A neuron optimization based PID approach is proposed in this paper and adopted in the NC cutting process. This approach optimizes the parameters of PID controller real-timely with the neural network control principle. It not only overcomes the mismatch of the open-loop system model which occurred in constant PID control, but also solves the contradiction between the calculation speed and precision in the neural network which caused by the node choosing of the hidden layer. At last, the simulation has been carried out on a NC milling machine to prove the validity and effectiveness of the proposed approach.

Tensile Stress-Strain Curves Modeling of Four Kinds of Solders with Temperature and Rate Dependence

2005 ◽  
Vol 297-300 ◽  
pp. 905-911 ◽  
Author(s):  
Xu Chen ◽  
Li Zhang ◽  
Masao Sakane ◽  
Haruo Nose
Keyword(s):  
Tensile Stress ◽  
Constitutive Model ◽  
Strain Rate ◽  
Constant Strain Rate ◽  
Tensile Tests ◽  
Strain Rate Range ◽  
Rate Dependence ◽  
Strain Rates ◽  
Stress Strain ◽  
Rate Range

A series of tensile tests at constant strain rate were conducted on tin-lead based solders with different Sn content under wide ranges of temperatures and strain rates. It was shown that the stress-strain relationships had strong temperature- and strain rate- dependence. The parameters of Anand model for four solders were determined. The four solders were 60Sn-40Pb, 40Sn-60Pb, 10Sn-90Pb and 5Sn-95Pb. Anand constitutive model was employed to simulate the stress-strain behaviors of the solders for the temperature range from 313K to 398K and the strain rate range from 0.001%sP -1 P to 2%sP -1 P. The results showed that Anand model can adequately predict the rate- and temperature- related constitutive behaviors at all test temperatures and strain rates.

scholarly journals Study on Plastic Deformation Behavior of Mo-10Ta under Ultra-High Strain Rate

Metals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1153
Author(s):  
Ping Song ◽  
Wen-Bin Li ◽  
Yu Zheng ◽  
Jiu-Peng Song ◽  
Xiang-Cao Jiang ◽  
...  
Keyword(s):  
Activation Energy ◽  
High Strain Rate ◽  
Strain Rate ◽  
Strain Rate Sensitivity ◽  
Deformation Behavior ◽  
High Strain ◽  
Rate Sensitivity ◽  
Strain Rate Range ◽  
Rate Range ◽  
Strain Relationship

This study investigated the deformation behavior of the Mo-10Ta alloy with a strain rate range of 102–105 s−1. The Split Hopkinson pressure bar (SHPB) experiments were conducted to investigate the influence of deformation conditions on the stress-strain relationship and strain rate sensitivity of the material within a strain rate range of 0.001–4500 s−1. The Shaped Charge Jet (SCJ) forming experiments under detonation loading was conducted to clarify the dynamic response and microstructure evolution of the material within an ultra-high strain rates range of 104–105 s−1. Based on the stress-strain relationship of Mo-10Ta alloy at high temperature (286–873 K) and high strain rate (460–4500 s−1), the influence of temperature and strain rate on the activation energy Q was analyzed. The results indicate that the material strain rate sensitivity increased with the increase in strain rate and strain. Meanwhile, the activation energy Q decreased as the temperature and strain rate increased. The plasticity of the Mo-10Ta alloy under the condition of SCJ forming was substantially enhanced compared with that under quasi-static deformation. The material grain was also refined under ultra-high strain rate, as reflected by the reduction in grain size from 232 μm to less than 10 μm.

scholarly journals Feed Forward Neural Network Modeling for Rainfall Prediction

2018 ◽  
Vol 73 ◽  
pp. 05017
Author(s):  
Yasin Hasbi ◽  
Warsito Budi ◽  
Santoso Rukun
Keyword(s):  
Neural Network ◽  
Autocorrelation Function ◽  
Seasonal Pattern ◽  
Optimization Method ◽  
Rainfall Data ◽  
Neural Network Modeling ◽  
Least Mean Square ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Hidden Layer

Prediction of rainfall data by using Feed Forward Neural Network (FFNN) model is proposed. FFNN is a class of neural network which has three layers for processing. In time series prediction, including in case of rainfall data, the input layer is the past values of the same series up to certain lag and the output layer is the current value. Beside a few lagged times, the seasonal pattern also considered as an important aspect of choosing the potential input. The autocorrelation function and partial autocorrelation function patterns are used as aid of selecting the input. In the second layer called hidden layer, the logistic sigmoid is used as activation function because of the monotonic and differentiable. Processing is done by the weighted summing of the input variables and transfer process in the hidden layer. Backpropagation algorithm is applied in the training process. Some gradient based optimization methods are used to obtain the connection weights of FFNN model. The prediction is the output resulting of the process in the last layer. In each optimization method, the looping process is performed several times in order to get the most suitable result in various composition of separating data. The best one is chosen by the least mean square error (MSE) criteria. The least of in-sample and out-sample predictions from the repeating results been the base of choosing the best optimization method. In this study, the model is applied in the ten-daily rainfall data of ZOM 136 Cokrotulung Klaten. Simulation results give a consecution that the more complex architecture is not guarantee the better prediction.

scholarly journals Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1362-1370 ◽  
Author(s):  
Yuen Liang ◽  
Suan Xu ◽  
Kaixing Hong ◽  
Guirong Wang ◽  
Tao Zeng
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Piezoelectric Actuators ◽  
Neural Network Modeling ◽  
Proportional Integral Derivative ◽  
Polynomial Fitting ◽  
Proportional Integral ◽  
The Neural Network ◽  
Fitting Method ◽  
One To One

A new polynomial fitting model based on a neural network is presented to characterize the hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural networks can only solve one-to-one mapping, a hysteresis mathematical model is proposed to expand the input of the neural network by converting the multi-valued into one-to-one mapping. Experiments were performed under designed excitation with different driven voltage amplitudes to obtain the parameters of the model using the polynomial fitting method. The simulation results were in good accordance with the measured data and demonstrate the precision with which the model can predict the hysteresis. Based on the proposed model, a single-neuron adaptive proportional–integral–derivative controller combined with a feedforward loop is designed to correct the errors induced by the hysteresis in the piezoelectric actuator. The results demonstrate superior tracking performance, which validates the practicability and effectiveness of the presented approach.

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