金屬材料專業(yè)外文翻譯--利用神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)與其他預(yù)測(cè)方法對(duì)δ鐵素體不銹焊縫的分析和比較

1、中文 中文 5518 5518 字出處： 出處：Journal of Materials Processing Technology 142 (2003) 20–28翻譯原文 翻譯原文Delta ferrite prediction in stainless steel welds using neural network analysis and comparison with other prediction methodsM. V

2、asudevan a,?, A.K. Bhaduri a, Baldev Raj a, K. Prasad Raoba Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam, Indiab Department of Metallurgy, Indian Institute of Technology, Chennai, I

3、ndiaReceived 2 May 2002; received in revised form 11 December 2002; accepted 17 February 2003AbstractThe ability to predict the delta ferrite content in stainless steel welds is important for many reasons. Depending on t

4、he 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 be

5、en 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 co

6、nstitution diagrams, Function Fit model, Feed-forward Back-propagation neural network model have been put forward for predicting the delta ferrite content in stainless steel welds. Among all the methods, neural network m

7、ethod 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. To avoid over-fitting, Mackay has developed a Bayesian fram

8、ework 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 varia

9、bles which are important in the content. Hence, the delta ferrite content estimated 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

10、[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 to ferrite number (FN). The equation used in this model to

11、determine FN is given below:FN = A[1 + 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 element

12、s and the ease 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 ea

13、sily. However, the accuracy of this method is not greater than the WRC-1992 diagram. Vitek et al. [8,9] sought to overcome the major limitation of the constitution diagram and the Function Fit method of not taking into a

14、ccount 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 neural networks, involving a feed-forward networ

15、k 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 func

16、tion of composition. However, it was 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

17、due to limitations in all the current methods, and newer methods and constitution diagrams are continuously being proposed to predict the delta ferrite content for a wider range of SS types. It was in this context that t

18、he 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