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