Modeling the Evolution of Corrosion: A feature-based interacting particle model for growth prediction

نویسنده

  • Donald Brown
چکیده

The Government Accounting Office reports that the Department of Defense spends approximately $20 Billion per year on prevention and repair of corrosion. The resources required to combat corrosion problems are desperately needed for equipment and personnel in today's high operations tempo environment. Despite its significance, modeling for the spatial-temporal prediction of corrosion evolution has been largely ignored in the literature. The purpose of this research is to develop a feature-based interacting particle model for corrosion evolution incorporating features available in time sequenced images of growth. A posterior probability of growth function is incorporated into a generic interacting particle model definition to influence growth direction based on the feature values at each location. Images of filiform growth on samples of AA2024-T3 reveal heterogeneities such as intermetallic particles and surface holes which are used as features in supervised learning methods to define the posterior probability of growth. This information is used by a simple interacting particle model to simulate the growth of corrosion. The accuracy of the predicted growth is compared to results from a random model without the feature information. Results for multiple filaments from different samples validate the use of the proposed model and suggest the inclusion of the heterogeneity features in the model to improve the predictive ability.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modeling of Corrosion-Fatigue Crack Growth Rate Based on Least Square Support Vector Machine Technique

Understanding crack growth behavior in engineering components subjected to cyclic fatigue loadings is necessary for design and maintenance purpose. Fatigue crack growth (FCG) rate strongly depends on the applied loading characteristics in a nonlinear manner, and when the mechanical loadings combine with environmental attacks, this dependency will be more complicated. Since, the experimental inv...

متن کامل

Stock Price Prediction using Machine Learning and Swarm Intelligence

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...

متن کامل

Probability Approach for Prediction of Pitting Corrosion Fatigue Life of Custom 450 Steel

In this study, the pitting type of corrosion growth characteristics, fatigue crack initiation and propagation behavior; axial fatigue tests were carried out on precipitation hardened martensitic Custom 450 steel in the air and 3.5wt% NaCl solution. Using the ratio of the depth to the half-width of the pits; (a/c)= 1.5±0.2 the corrosion pit depth growth law was obtained as a function of stress a...

متن کامل

Numerical Modeling of Saline Gravity Currents Using EARSM and Buoyant k- Turbulence Closures

Gravity currents are very common in nature and may appear in rivers, lakes, oceans, and the atmosphere. They are produced by the buoyant forces interacting between fluids of different densities and may introduce sediments and pollutants into water bodies. In this study, the hydrodynamics and propagation of gravity currents are investigated using WISE (Width Integrated Stratified Environments), ...

متن کامل

Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine

Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehen...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006