An example of the use of neural computing techniques in materials science - the modelling of fatigue thresholds in Ni-base superalloys

被引:40
作者
Schooling, JM
Brown, M
Reed, PAS [1 ]
机构
[1] Univ Southampton, Dept Mat Engn, Southampton SO17 1BJ, Hants, England
[2] Univ Cambridge, Dept Mat Sci & Met, Cambridge CB2 3QZ, England
[3] Univ Southampton, Dept Elect & Comp Sci, ISIS, Southampton SO17 1BJ, Hants, England
来源
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING | 1999年 / 260卷 / 1-2期
关键词
neural computing; superalloy; fuzzy rules; fatigue threshold;
D O I
10.1016/S0921-5093(98)00957-5
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. (C) 1999 Elsevier Science S.A. All rights reserved.
引用
收藏
页码:222 / 239
页数:18
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