Corrosion prediction in aging aircraft materials using neural networks

Bailey, R.A.; Pidaparti, Ramana M.; Jayanti, S.; Palakal, Mathew

doi:10.2514/6.2000-1762

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…The results obtained were in good agreement with the experimental data. A similar work was done by Bailey, et al 29 They developed a model using neural networks to predict the ASTM G 34 30 metal corrosion rating and the resulting material loss in aging aircrafts.…”

Section: Neural Network As a Modeling Tool To Predict Corrosionmentioning

confidence: 88%

Use of Artificial Neural Networks for Predicting Crude Oil Effect on Carbon Dioxide Corrosion of Carbon Steels

et al. 2006

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The role of crude oil on carbon dioxide (CO 2 ) corrosion has gained special attention in the last few years because of its signifi cance when predicting corrosion rates. However, the complexity and variability of crude oils makes it hard to model its effects, which can infl uence not only wettability properties but also the corrosiveness of the associated brine. This study evaluates the usefulness of artifi cial neural networks (ANN) to predict the corrosion inhibition offered by crude oils as a function of several of their properties that have been related in previous studies to the protectiveness of crude oils, i.e., nitrogen and sulfur contents, resins and asphaltenes, total acid number, nickel and vanadium content, etc. Results showed that neural networks are a powerful tool and that the validity of the results is closely linked to the amount of data available and the experience and knowledge that accompany the analysis.

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Section: Neural Network As a Modeling Tool To Predict Corrosionmentioning

confidence: 88%

Use of Artificial Neural Networks for Predicting Crude Oil Effect on Carbon Dioxide Corrosion of Carbon Steels

et al. 2006

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“…In another study by Bailey et al, 12 two neural network based models have been developed to predict the corrosion behaviour of different aluminium alloys when exposed to corrosive environments. However, these methodologies require a large number of data sets for training the network, and the trained ANN model can be applied only within that problem domain.…”

Section: Modelling Of the Rate Of Corrosion -A Brief Reviewmentioning

confidence: 99%

Cumulative damage function model for prediction of uniform corrosion rate of metals in atmospheric corrosive environment

Bhaskar¹,

Iyer²,

Rajasankar³

2004

Corrosion Engineering, Science and Technology

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The main focus of the paper is on the development of a unified analytical model for predicting the rate of uniform corrosion for commonly used engineering metals exposed to the atmosphere. A cumulative damage function model is proposed on the presumption that the corrosion is primarily due to the presence of critical corrosive agents in the atmosphere. The model incorporates sensitivity factors to account for the corrosion sensitivity of the metal with respect to the critical corrosive agents and the presence of other corrosive agents. Predictions of the uniform corrosion rate of metallic structures made of iron, mild steel, carbon steel, and zinc are presented. Results predicted using the model are compared with the values expected for mild steel using the data available in the Corrosion Map of India.CEST/2153

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“…Various conventional regression models can be developed to model this failure rate. However, much interest has recently been focused on the application of artificial neural networks (ANN) in modeling (Wu and Yen, 1992;Lu et al, 1993;Al-Garni, 1997;Al-Garni et al, 1998;Ganguli et al, 1998;Bailey et al, 2000;Pidaparti et al, 2002), and it was shown that the ANN performs better than the regression models.…”

Section: Introductionmentioning

confidence: 99%