1、PDF外文：http:/ 5518 字 出处： Journal of Materials Processing Technology 142 (2003) 2028 翻译原文 Delta ferrite prediction in stainless steel welds using neural network analysis and comparison with other prediction methods M. Vasudevan a, A.K. Bhaduri a, Baldev Raj a, K. Prasad Raob a Metallurgy and Mat
2、erials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam, India b Department of Metallurgy, Indian Institute of Technology, Chennai, India Received 2 May 2002； received in revised form 11 December 2002； accepted 17 February 2003 Abstract The ability to predict the delta ferrite
3、 content in stainless steel welds is important for many reasons. Depending on the service requirement,manufacturers and consumers often specify delta ferrite content as an alloy specification to ensure that weld contains a desired minimum or maximum ferrite level. Recent research activities have bee
4、n focused on studying the effect of various alloying elements on the delta ferrite content and controlling delta ferrite content by modifying the weld metal compositions. Over the years, a number of methods including constitution diagrams, Function Fit model, Feed-forward Back-propagation neural net
5、work model have been put forward for predicting the delta ferrite content in stainless steel welds. Among all the methods, neural network method was reported to be more accurate compared to other methods. A potential risk associated with neural network analysis is over-fitting of the training data.
6、To avoid over-fitting, Mackay has developed a Bayesian framework to control the complexity of the neural network. Main advantages of this method are that it provides meaningful error-bars for the model predictions and also it is possible to identify automatically the input variables which are import
7、ant in the non-linear regression. In the present work, Bayesian neural network (BNN) model for prediction of delta ferrite content in stainless steel weld has been developed. The effect of varying concentration of the elements on the delta ferrite content has been quantified for Type 309 austenitic
8、stainless steel and the duplex stainless steel alloy 2205. The BNN model is found to be more accurate compared to that of the other methods for predicting delta ferrite content in stainless steel welds. 1. Introduction The ability to estimate the delta ferrite content accurately has proven ver
9、y useful in predicting the various properties of austenitic SS welds. A minimum delta ferrite content is necessary to ensure hot cracking resistance in these welds 1,2, while an upper limit on the delta ferrite content determines the propensity to embrittlement due to secondary phases, e.g. sigma ph
10、ase, etc., formed during elevated temperature service 3. At cryogenic temperatures, the toughness of the austenitic SS welds is strongly influenced by the delta ferrite content 4. In duplex stainless steel weld metals,a lower ferrite limit is specified for stress corrosion cracking resistance
11、while the upper limit is specified to ensure adequate ductility and toughness 5. Hence, depending on the service requirement a lower limit and/or an upper limit on delta ferrite content is generally specified. During the selec-tion of filler metal composition, the most accurate diagram to date WRC-1
12、992 is used generally to estimate the _-ferrite content 6. The Creq and Nieq formulae used for generating the WRC-1992 constitution diagram is given by Creq =Cr+Mo+0.7Nb and Nieq = Ni+35C+20N+0.25Cu. The limitation of these equations is that values of the coefficients for the different elements rema
13、in unchanged irrespective of the change in the base composition of the weld. However, the relative influence of each alloying addition given by the elemental coefficients in the Creq and Nieq expressions is likely to change over the full composition range. Furthermore,these expressions ignore the in
14、teraction between the elements. Also, there are a number of alloying elements that have not been considered in the WRC-1992 diagram. Elements like Si, Ti, W have not been given due to considerations, though they are known to influence the delta ferrite content. Hence, the delta ferrite content estim
15、ated using the WRC-1992 diagram would always be less accurate and may never be close to the actual measured value. In the Function Fit model 7 for estimating ferrite, the difference in free energy between the ferrite and the austenite was calculated as a function of composition and this was related
16、to ferrite number (FN). The equation used in this model to determine FN is given below: FN = A1 + exp(B + C_G)1 (1) where A, B and C are the constants. The advantages of this semi-empirical model over the WRC-1992 diagram include its considering effect of other alloying elements and the ease
17、of extrapolation to higher Creq and Nieq values. This Function Fit method can be used for a wide range of weld metal compositions and owing to the analytical form of this model, the FN can be quantified easily. However, the accuracy of this method is not greater than the WRC-1992 diagram. Vitek et a
18、l. 8,9 sought to overcome the major limitation of the constitution diagram and the Function Fit method of not taking into account the elemental interactions, by using neural networks for predicting ferrite in SS welds.The improvement in accuracy in predicting the delta ferrite content by using neura
19、l networks, involving a feed-forward network with a back-propagation optimization scheme, has been clearly brought in their study. The effect of various elements on the delta ferrite content for a few base compositions was examined by calculating the FN as a function of composition. However, it was
20、not possible in their analysis to directly interpret the elemental contributions to the final FN. The prediction and measurement of ferrite in SS welds remains of scientific interest due to limitations in all the current methods, and newer methods and constitution diagrams are continuously being pro
21、posed to predict the delta ferrite content for a wider range of SS types. It was in this context that the development of a more accurate neural network based predictive tool for estimating the effect of various alloying elements on the delta ferrite content for different SS welds was taken up in this work. A potential risk associated with neural network analysis is over-fitting of the
