University of Cambridge Stphane Forsik 5th June 2006,Neural network: A set of four case studies,What does Neural network analysis mean for you?,Neural network?,4 examples of neural network analysis:,Estimation of the amount of retained austenite in austempered ductile irons,Neural network model of creep strength of austenitic stainless steels,Neural-network analysis of irradiation hardening in low-activation steels,Application of Bayesian Neural Network for modeling and prediction of ferrite number in austenitic stainless steel welds,Four practical examples,How to build a neural network?,1 - Identification of a problem which is too complex to be solved.,2 - Compilation of a set of data.,3 - Testing and training of the neural network.,4 - Predictions.,4 examples of neural network analysis:,Estimation of the amount of retained austenite in austempered ductile irons,Neural network model of creep strength of austenitic stainless steels,Neural-network analysis of irradiation hardening in low-activation steels,Application of Bayesian Neural Network for modeling and prediction of ferrite number in austenitic stainless steel welds,Estimation of the amount of retained austenite in austempered ductile irons,Analysis of the problem,Retained austenite helps to optimize the mechanical properties of austempered ductile irons.,The maximization of the amount of retained austenite gives the best mechanical properties.,Many variables are involved in this calculation and no models can give quantitative accurate predictions.,A neural network is the solution.,Input parameters,wt% C, wt% Si, wt% Mn, wt% Ni, wt% Cu,Austenising time (min) and temperature (K),Austempering time (min) and temperature (K),Volume fraction of retained austenite (%),HIDDEN UNITS,Inputs/outputs,Training and testing of the model,Predictions of Si and C,Volume fraction max for 3-3.25 wt% Si.,No effect below 3.6 wt% C.,Below 3.1 wt% Si, more bainitic transformation and more austenite carbon enrichment.,Over 3.1 wt% Si, formation of islands of pro-eutectod ferrite in the bainite structure.,Slight stabilization over 3.6 wt% C, possibly longer time to reach equilibrium for high concentrations.,No effect below 2 wt% Ni,Slight stabilization below 1 wt% Cu,Predictions of Ni and Cu,First conclusion,A neural network can give predictions in agreement with theory and experimental values.,Error bars are an indication of the reliability of the model.,More data should be collected or more experiments should be carried out in the range of concentration where error bars are large.,4 examples of neural network analysis:,Estimation of the amount of retained austenite in austempered ductile irons,Neural network model of creep strength of austenitic stainless steels,Neural-network analysis of irradiation hardening in low-activation steels,Application of Bayesian Neural Network for modeling and prediction of ferrite number in austenitic stainless steel welds,Neural network model of creep strength of austenitic stainless steels,Analysis of the problem,Austenitic stainless steels are used in the power generation industry at 650 C, 50 MPa or more for more than 100 000 hours.,Creep stress rupture is a major problem for those steels.,No experiments can be carried out for 100 000 hours and pseudo-linear relations cannot take in account complex interactions between components.,A neural network is the solution.,Input parameters,wt% Cr, wt% Ni, wt% Mo, wt% Mn, wt% Si, wt% Nb, wt% Ti, wt% V, wt% Cu, wt% N, wt% C, wt% B, wt% B, wt% P, wt% S, wt% Co, wt% Al,Test stress (Mpa), test temp. (C), log(rupture life, h),Solution treatment temperature (C),104 h creep rupture stress,HIDDEN UNITS,Inputs/outputs,Training and testing of the model,Predictions,Mechanism is not understood,Comparison with other methods,Second conclusion,Good agreement in trend, limited by error bars.,Good agreement when predictions are compared to experimental values, more precise than other models.,4 examples of neural network analysis:,Estimation of the amount of retained austenite in austempered ductile irons,Neural network model of creep strength of austenitic stainless steels,Neural-network analysis of irradiation hardening in low-activation steels,Application of Bayesian Neural Network for modeling and prediction of ferrite number in austenitic stainless steel welds,Neural-network analysis of irradiation hardening in low-activation steels,Fusion reaction,Insterstitials, vacancies,Transmuted helium,Precipitates,Hardening, embrittlement,dpa = displacement-per-atom,Analysis of the problem,Future fusion power plants will be based on a 100 million degree plasma which will produce 14 MeV neutrons.,Energetic neutrons are a major problem for materials composing the magnetic confinement.,Today, no fusion sources, no sources of 14 MeV neutrons. Need to extrapolate from fission results.,A neural network is the solution.,Input parameters,wt% C, wt% Cr, wt% W, wt% Mo, wt% Ta, wt% V, wt% Si, wt% Mn, wt% Mn, wt%