application of artificial neural network in materials research wei sha professor of materials...
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Application of artificial neural network in materials research
Wei SHAProfessor of Materials Sciencehttp://space.qub.ac.uk:8077/cber/Sha
The modelsIntegrated
Alloy Composition
Heat Treatment
Temperature
Al
Mo
V
Art
ific
ial
Neu
ral
Net
wo
rk M
od
els
R—1, Fatigue Strength, MPa
Impact Strength, Charpy, J
RA, Reduction of Area, %
Rm, Tensile Strength, MPa
R0,2, 0.2% Yield Strength, MPa
E, Elongation, %
HRC, Rockwell Hardness
Modulus of Elasticity, GPa
K1C, Fracture Toughness
INPUT OUTPUT
TTT Diagram
Grade
Corrosion Rate, mm/Yr
Concentration
Microstructure
Fatigue Stress Life Diagram
Str
ess
Am
pli
tud
e
Cycles to Failure
Environment
Texture
Surface Treatment
Stress ratio
Part III (pp. 301-410)
“Application of neural-network models”pp. 553-565
Composition-processing-temperature-mechanical properties TTT diagrams
Fatigue life CCT diagrams Microhardness profile
The modelsGraphical user interfaces of software for modelling
Basic principles of neural network modellingNatual Artificial
Input Layer
Hidden Layer
Output Layer
1
2
3
5
4
Neuron
Neural Network
The human brain contains 1010 – 1011 neurons
Basic principles of neural network modellingSteps
• Database collection
• Analysis and pre-processing of the data
• Design and training of the neural network
• Test of the trained network
• Using the trained NN for simulation and prediction
Database construction and analysisDistribution of the input dataset
Reading of file with database
Normalisation of the data
Creating neural network and defining training parameters
Neural network training
Post-training analyses for training and test subsets
Use of the model
Experimental verification
Random redistribution of database
Dividing to training and test subsets for inputs and corresponding outputs
Loop for new random redistribution of the
database
Loops for new network architecture,
training algorithm, transfer function and training parameters
Algorithm of computer programCreation of neural network model
-40 -30 -20 -10 0 10 20 30 400
50
100
150
200
250
300
350
400N
umbe
r
×100 (%)HV
HVHVError
EXP
NNEXP -=
Min = -37.94Max = 35.89Mean = -0.13STDEV = 10.31
Algorithm of computer program Post-training validation of the software simulations
Algorithm of computer program Comparison between prediction and experiments
0
100
200
300
400
500
600
Ti-48Al-2Cr-2Nb duplex
750°C
Ti-48Al-2Cr-2Nb fully
transformed750°C
Ti-48Al-1V-0.2C duplex
20°C
Ti-48Al-1V-0.2C equiaxedbimodal 20°C
Ti-48Al-2Nb-2Mn equiaxedbimodal 650°C
Ti-48Al-2Nb-2Mn fully
lamellar 650°C
Ult
imat
e S
tren
gth
(M
Pa)
Model Prediction
Experimental
Use of the softwareBlock diagram of software system
Databases
Computer program for
training (learning)
Trained artificial neural
networks
Graphical User Interfaces for
use of the models
Graphical User
Interfaces for
upgrade of the
models
Module for new data input
Module for input of re-
training parameters
Module for materials selection
Use of the software Influence of alloy composition in -TiAl, 1040 °C
0 1 2 3 4 5 01 2
3 45100
150
200
250
Nb, at.%Cr, at.%
Yie
ld S
tres
s (M
Pa)
Use of the software Microhardness profiles of titanium after nitriding
Use of the software Ti–15Mo–5Zr–3Al, nitrided in N2 at 750 °C for 60 h
0 100 200 300 400 500200
400
600
800
1000
Distance from the surface ( m)
HK
Experimental NN prediction (II model)
(µm)
Use of the softwareOptimization of alloy composition and processing
http://space.qub.ac.uk:8077/cber/Sha
Optimisation Criteria
TrainedNeural
Network
Solution
Loops for Heat Treatment
Loops forTemperature
Loops for Alloy Composition
FindAlloy composition with max strength at 420°C
FixHeat treatment = Annealing
T = 420°CSn, Cr, Fe, Si, Nb, Mn = 0; O = 0.12
VaryAl, Mo, Zr, V
SolutionAl = 5.8; Mo = 7.3; Zr = 5.2; V = 0
Tensile strength (420°C) = 932 MPa;Yield strength = 665 MPa;
Elongation = 10%; Modulus of elasticity = 94 GPa;Fatigue strength = 448 MPa;
Fracture toughness = 101 MPa m1/2