Thomas Sourmail and Carlos Garcia Mateo
Phase Transformations Group,
Department of Materials Science and Metallurgy,
University of Cambridge,
Cambridge CB2 3QZ, U.K.
E-mail: ts228athermesdotcamdotacdotuk
Added to MAP: June 2004.
A program for the prediction of the Ms temperature of steels as a function of chemical composition.
Language: | C |
Product form: | Source Code (all Unix flavours), binary executable (DOS), package for Neuromat Predictor |
Operating System : Tested on Solaris, Irix, Linux RH and Windows 98/2000/XP.
MAP_STEEL_MS_2004 contains the programs which enable the user to estimate the Ms temperature of steels as a function of chemical composition. It was trained using Neuromat's Model Manager, which is based on David Mackay's bayesian neural network program. Previously published databases on Ms temperatures were thoroughly verified and corrected for a large number of mistakes. An additional 700 data points were added, leading to a total of about 1100 data points.
If you have compiled the Unix/Linux version or are using the Windows/DOS program, the only file which you should be concerned with is 'test.dat', which you should edit to make predictions on your own compositions. An example file is provided which contains data for 9 different steels.
The input variables are C, Mn, Si, Cr, Ni, Mo, V, Co, Al, W, Cu, Nb, Ti, B and N. The maximum and minimum values for each variable are given in the file MINMAX.
This model predict ln(-ln(Ms/1000)) where Ms is in Kelvin. It is for you to
calculate Ms=1000*exp(-exp(output)) to obtain the correct result.
If you use Neuromat's Predictor, this is automatic.
A full calculation of the error bars are given in reference [4].
No information supplied.
Read README for installation instruction on Linux/Unix
Complete program.
Prediction Error Pred-Err Pred+Err -0.977297 0.019441 -0.996738 -0.957856 -0.901787 0.015718 -0.917505 -0.886068 -0.762616 0.011469 -0.774084 -0.751150 -0.680312 0.008202 -0.688514 -0.672110 -0.612506 0.009239 -0.621746 -0.603271 -0.508404 0.011323 -0.519727 -0.497080 -0.437531 0.012479 -0.450006 -0.425052 -0.370368 0.012560 -0.382927 -0.357808 -0.286682 0.014232 -0.300914 -0.272453
Neural networks, Ms temperature, martensite start temperature, steels.