There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach objectives in a cost and time--effective way. Any model which deals with only a small part of the required technology is therefore unlikely to be treated with respect. Neural network analysis is a form of regression or classification modelling which can help resolve these difficulties whilst striving for longer term solutions. This paper begins with an introduction to neural networks and contains a review of some applications of the technique in the context of materials science.
Quicktime movieshowing the function z=0.8[tanh(nx-2) + tanh(x
2-n)+tanh ny+2) + tanh(y
2-n)+1], which is a neural network of two inputs x and y, with four hidden units.
"The Strength of Ni-base Superalloys - a Bayesian Neural Network Analysis"
Proceedings of the 5th International Symposium on Advanced Materials, Pakistan, (1995) 659-666.
J. Jones, D. J. C. MacKay and H. K. D. H. Bhadeshia
"
An Affordable, Creep-Resistant, Nickel-base Superalloy for Power Plant" Proceedings of the 6th International Charles Parsons Turbine Conference, Engineering Issues in Turbine Machinery, Power Plant and Renewables, eds A. Strang, R. D. Conroy, W. M. Banks, M. Blackler, J. Leggett, G. M. McColvin, S. Simplson, M. Smith, F. Starr and R. W. Vanstone, Institute of Materials, London, 2003, 525-535.
F. C. Tancret and H. K. D. H. Bhadeshia
"Components of the Creep Strength of Steel Welds" Mathematical Modelling of Weld Phenomena - VI
Published by the Institute of Materials, eds H. Cerjak and H. K. D. H. Bhadeshia, 2002, 243-260
M. Muruganath and H. K. D. H. Bhadeshia
"Components of the creep strength of steel welds" Trends in Welding Research,eds S. A. David, J. Vitek, T. Debroy, J. Lippold and H. Smartt, ASM International, USA, 2002, 719-723.
M. Murugananth and H. K. D. H. Bhadeshia
"
An Affordable, Creep-Resistant, Nickel-base Superalloy for Power Plant" Proceedings of the 6th International Charles Parsons Turbine Conference, Engineering Issues in Turbine Machinery, Power Plant and Renewables, eds A. Strang, R. D. Conroy, W. M. Banks, M. Blackler, J. Leggett, G. M. McColvin, S. Simplson, M. Smith, F. Starr and R. W. Vanstone, Institute of Materials, London, 2003, 525-535.
F. C. Tancret and H. K. D. H. Bhadeshia
Advanced Fabricated 10Cr Rotor Technology for Increased Efficiency Seventh International EPRI Conference on {\em Welding and Repair Technology for Power Plants}, June 21--23, 2006, Florida, USA, pp. 1--10.
D. R. Amos, R. D. Conroy, W. Janssen, T.--U. Kern and H. K. D. H. Bhadeshia
"Welding Procedures and Type IV Phenomena" Trends in Welding Research, ASM International, eds S. A. David, T. DebRoy, J. C. Lippold, H. B. Smart and J. M. Vitek, 2005, pp. 737--742.
J. Francis, W. Mazur and H. K. D. H. Bhadeshia
Scaling Laws From Statistical Data and Dimensional Analysis
Introduction to Materials Modelling
The book, edited by Zoe Barber, is based on a one-year, interdisciplinary
Masters Coursegiven by the Materials Science and Metallurgy Department of Cambridge University. The course is designed for graduates from the sciences, mathematics, and engineering. It covers techniques spanning a huge range of length scales, from atoms to engineering structures.
Contents
General Aspects of Materials Modelling, by P. D. Bristowe and P. J. Hansip
Modelling of Electronic Structure, by A. H. Cottrell
Advanced Atomistic Modelling, by P. J. Hansip
Thermodynamics, by H. K. D. H. Bhadeshia
Kinetics, by H. K. D. H. Bhadeshia and A. L. Greer
Monte Carlo and Molecular Dynamics, by J. A. Elliott
Mesoscale Methods and Multiscale Modelling, by J. A. Elliott
Finite Elements, by S. Tin and H. K. D. H. Bhadeshia
Neural Networks, by T. Sourmail and H. K. D. H. Bhadeshia
