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.

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.

Watermelon Sorting Process by Frequency Identification and Artificial Neural Network

2021 ◽  
Author(s):  
Komsan Wongkalasin ◽  
Teerapon Upachaban ◽  
Wacharawish Daosawang ◽  
Nattadon Pannucharoenwong ◽  
Phadungsak Ratanadecho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Selection Process ◽  
Network Processor ◽  
Data Set ◽  
The Neural Network ◽  
Frequency Identification ◽  
Sorting Process ◽  
Artificial Neural ◽  
Reasonable Prediction

This research aims to enhance the watermelon’s quality selection process, which was traditionally conducted by knocking the watermelon fruit and sort out by the sound’s character. The proposed method in this research is generating the sound spectrum through the watermelon and then analyzes the response signal’s frequency and the amplitude by Fast Fourier Transform (FFT). Then the obtained data were used to train and verify the neural network processor. The result shows that, the frequencies of 129 and 172 Hz were suit to be used in the comparison. Thirty watermelons, which were randomly selected from the orchard, were used to create a data set, and then were cut to manually check and match to the fruits’ quality. The 129 Hz frequency gave the response ranging from 13.57 and above in 3 groups of watermelons quality, including, not fully ripened, fully ripened, and close to rotten watermelons. When the 172 Hz gave the response between 11.11–12.72 in not fully ripened watermelons and those of 13.00 or more in the group of close to rotten and hollow watermelons. The response was then used as a training condition for the artificial neural network processor of the sorting machine prototype. The verification results provided a reasonable prediction of the ripeness level of watermelon and can be used as a pilot prototype to improve the efficiency of the tools to obtain a modern-watermelon quality selection tool, which could enhance the competitiveness of the local farmers on the product quality control.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Dynamic constitutive relation of 5083P-O aluminium alloy and its influence on energy-absorbing structure

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880733
Author(s):  
Yue Feng ◽  
Shoune Xiao ◽  
Bing Yang ◽  
Tao Zhu ◽  
Guangwu Yang ◽  
...  
Keyword(s):  
Finite Element ◽  
Strain Rate ◽  
Aluminium Alloy ◽  
Constitutive Relation ◽  
High Strain ◽  
Deformation Mode ◽  
Strain Rates ◽  
Strengthening Effect ◽  
Element Simulation ◽  
Energy Absorbing

Dynamic and quasi-static tensile tests of 5083P-O aluminium alloy were carried out using RPL100 electronic creep/fatigue testing machine and the split Hopkinson tension bar, respectively. The dynamic constitutive relation of the material at high strain rates was studied, and the constitutive model in accordance with Cowper–Symonds form was established. At the same time, a method to describe the constitutive relation of material using the strain rate interpolation method which is included in LS-DYNA software was proposed. The advantages and accuracy of this method were verified by comparing the results of the finite element simulation with the fitting results of the Cowper-Symonds model. The influence of material strain rate effect on squeezing force, energy absorption and deformation mode of the squeezing energy-absorbing structure based on the constitutive models of 5083P-O were studied by means of finite element simulation. The results show that when the strain rate of the structure deformation is low, the material strain rate strengthening effect has little influence on the structure. However, with the increase of the strain rate, the strengthening effect of the material will improve the squeezing force and the energy absorption of the structure, and will also influence the deformation mode, that is, the decrease of the deformation with high strain rates while the increase of the deformation with low strain rates.

Linear Static Response of Suspension Arm Based on Artificial Neural Network Technique

2011 ◽  
Vol 213 ◽  
pp. 419-426
Author(s):  
M.M. Rahman ◽  
Hemin M. Mohyaldeen ◽  
M.M. Noor ◽  
K. Kadirgama ◽  
Rosli A. Bakar
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Network Model ◽  
Neural Network Model ◽  
Mesh Size ◽  
Maximum Displacement ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis

Modeling and simulation are indispensable when dealing with complex engineering systems. This study deals with intelligent techniques modeling for linear response of suspension arm. The finite element analysis and Radial Basis Function Neural Network (RBFNN) technique is used to predict the response of suspension arm. The linear static analysis was performed utilizing the finite element analysis code. The neural network model has 3 inputs representing the load, mesh size and material while 4 output representing the maximum displacement, maximum Principal stress, von Mises and Tresca. Finally, regression analysis between finite element results and values predicted by the neural network model was made. It can be seen that the RBFNN proposed approach was found to be highly effective with least error in identification of stress-displacement of suspension arm. Simulated results show that RBF can be very successively used for reduction of the effort and time required to predict the stress-displacement response of suspension arm as FE methods usually deal with only a single problem for each run.

scholarly journals Bootstrapping a Neural Morphological Generator from Morphological Analyzer Output for Inuktitut

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Jeffrey Micher
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Data Set ◽  
Set Size ◽  
The Neural Network ◽  
Surface Character ◽  
Finite State ◽  
Character Sequences ◽  
Finite State Transducer

We present a method for building a morphological generator from the output of an existing analyzer for Inuktitut, in the absence of a two-way finite state transducer which would normally provide this functionality. We make use of a sequence to sequence neural network which “translates” underlying Inuktitut morpheme sequences into surface character sequences. The neural network uses only the previous and the following morphemes as context. We report a morpheme accuracy of approximately 86%. We are able to increase this accuracy slightly by passing deep morphemes directly to output for unknown morphemes. We do not see significant improvement when increasing training data set size, and postulate possible causes for this.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

scholarly journals The Prediction of Buckling Load of Laminated Composite Hat-Stiffened Panels Under Compressive Loading by Using of Neural Networks

2018 ◽  
Vol 12 (1) ◽  
pp. 468-480 ◽  
Author(s):  
Shashi Kumar ◽  
Rajesh Kumar ◽  
Sasankasekhar Mandal ◽  
Atul K. Rahul
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Network Architecture ◽  
Laminated Composite ◽  
Buckling Load ◽  
Stiffened Panel ◽  
Stiffness Ratio ◽  
Data Set ◽  
Stiffened Panels ◽  
Structural Problems

Background:Stiffened panels are being used as a lightweight structure in aerospace, marine engineering and retrofitting of building and bridge structure. In this paper, two efficient analytical computational tools, namely, Finite Element Analysis (FEA) and Artificial Neural Network (ANN) are used to analyze and compare the results of the laminated composite 750-hat-stiffened panels.Objective:Finite Element (FE) is an efficient and versatile method for the analysis of a complex problem. FE models have been used to generate data set of four different parameters. The four parameters are extensional stiffness ratio of skin in the longitudinal direction to the transverse direction, orthotropy ratio of the panel, the ratio of twisting stiffness to transverse flexural stiffness and smeared extensional stiffness ratio of stiffeners to that of the plate.Results and Conclusion:For training of ANN, multilayer feedforward back-propagation has been used as a network function with two-hidden layers in the neural network. The good network architecture is achieved after several iterations to predict the buckling load of the stiffened panel. ANN prediction for unknown new data set is in good agreement with FEA results of different cases, which show that ANN tool can be used for the design of complex structural problems in civil engineering and optimization of the laminated composite stiffened panel.

Tail Deviation Mechanism and Neural Network Control of the Tandem Rolling Strap

2011 ◽  
Vol 103 ◽  
pp. 488-492
Author(s):  
Guang Bin Wang ◽  
Xian Qiong Zhao ◽  
Yi Lun Liu
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Control Policy ◽  
Element Model ◽  
Rolling Process ◽  
Network Control ◽  
Neural Network Control ◽  
The Neural Network ◽  
The Finite Element Model

In the rolling process, deviation is the phenomenon that the strap width direction's centerline deviates from rolling system setting centerline,serious deviation will cause product quality drop and rolling equipment fault. This paper has established the finite element model to the hot tandem rolling aluminum strap, analyzed the strap’s deviation rule under four kinds of incentives,obtained the neural network predictive model and the control policy of the tail deviation.The result to analyze a set of fact deviation data shows this method may control tail deviation in preconcerted permission range.

Simulation of hot high-speed yielding of intermetallic titanium alloy VTI-4 under conditions of high-speed loading

2020 ◽  
Vol 0 (12) ◽  
pp. 10-16
Author(s):  
V.V. Avtaev ◽  
◽  
D. V. Grinevich ◽  
A. V. Zavodov
Keyword(s):  
Finite Element ◽  
Titanium Alloy ◽  
Strain Rate ◽  
Finite Element Simulation ◽  
Modulus Of Elasticity ◽  
High Speed ◽  
Residual Deformation ◽  
Test Results ◽  
Element Simulation ◽  
The Relationship

Yielding tests of VTI-4 alloy specimens have been carried out at temperature 1010 °C under conditions of high-speed loading. Based on the test results the modulus of elasticity as well as axial and radial residual deformation values in the end and central zones for each loading stage were determined. Fitting criteria for finite element simulation and the experiment are proposed with tracing VTI-4 alloy diagram deformation at temperature 1010 °C and strain rate of 2.5 sec–1. As a result of finite element simulation the relationship between the material structures obtained during high-speed yielding and the deflected modes in different zones was determined.

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