Application of artificial neural network in materials research Wei SHA Professor of Materials Science http://space.qub.ac.uk:8077/cber/Sha 25 June 2007

Modelling methodologies Metals Research Group, Queen’s University Thermodynamic modelling The Johnson-Mehl-Avrami method and its adaption to continuous cooling and heating Finite element method Phase field method Atomistic simulation Neural network method

The models Integrated Malinov, Sha, JOM, 57(11), 2005, 54.

The models Graphical user interfaces of software for modelling Composition-processing-temperature-mechanical properties TTT diagrams Fatigue life CCT diagrams Microhardness profile Malinov, Sha, Computational Materials Science, 28, 2003, 179.

Basic principles of neural network modelling Natual Artificial Neuron Input Layer Hidden Layer Output Layer Neural Network The human brain contains 1010 – 1011 neurons Malinov, Sha, McKeown, Computational Materials Science, 21, 2001, 375.

Basic principles of neural network modelling Architecture of the neural network Malinov, Sha, Guo, Materials Science and Engineering A, 283, 2000, 1.

Basic principles of neural network modelling Neural network modelling steps 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 analysis Training database

Database construction and analysis Distribution of the input dataset Guo, Malinov, Sha, Computational Materials Science, 32, 2005, 1.

Algorithm of computer program Creation of neural network model 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 program Steps in creating the model Master database containing all the data for each input and output Sub-spreadsheets for each individual output Matrices in sub-spreadsheets copied-and-pasted into a notepad file for each output MATLAB files created for the training of each individual neural network Individual neural networks ‘trained’ to best possible accuracy, resulting data files saved for each output Master model file created using MATLAB to read in the output mat files from the training, and predict the outputs. Model runs from a central GUI (Graphical User Interface) for user control McBride, Malinov, Sha, Materials Science and Engineering A, 384, 2004, 129.

Neural Network Predictions Algorithm of computer program Post-training linear regression analysis 500 1000 1500 2000 2500 Training R = 0.97 Neural Network Predictions Experimental Test R = 0.963 Data Points A = T Best Linear Fit

Algorithm of computer program Training parameters

Algorithm of computer program Post-training validation of the software simulations 400 Min = - 37.94 Max = 35.89 Mean = -0.13 STDEV = 10.31 350 300 250 Number 200 150 100 50 -40 -30 -20 -10 10 20 30 40 ×100 (%) HV Error EXP NN - =

Algorithm of computer program Comparison between prediction and experiments Sha, JOM, 58(9), 2006, 64.

Noise tolerance The experimental values are values including noise

Noise tolerance The experimental values are values without noise

Use of the software Block diagram of software system for modelling 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 g-TiAl, 1040 °C Malinov, Sha, Materials Science and Engineering A, 365, 2004, 202.

Use of the software Microhardness profiles of titanium after nitriding Zhecheva, Malinov, Sha, JOM, 59(6), 2007, 38.

Use of the software Ti–15Mo–5Zr–3Al, nitrided in N2 at 750 °C for 60 h Zhecheva, Malinov, Sha, Surface and Coatings Technology, 200, 2005, 2332.

Use of the software Optimization of the alloy composition & processing Optimisation Criteria Trained Neural Network Solution Loops for Heat Treatment Temperature Alloy Composition Find Alloy composition with max strength at 420°C Fix Heat treatment = Annealing T = 420°C Sn, Cr, Fe, Si, Nb, Mn = 0; O = 0.12 Vary Al, Mo, Zr, V Al = 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 http://space.qub.ac.uk:8077/cber/Sha