scholarly journals An Instantaneous Corrosion Monitoring Technique based on Combining Modified Electrochemical Noise and Artificial Neural Network for Determination of Corrosion Type and 2014 Aluminium Alloy Corrosion Rate in NaCl and Ce(NO3)3 solutions

2022 ◽  
pp. ArticleID:220213
Author(s):  
Qiangfei Hu ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Corrosion Rate ◽  
Aluminium Alloy ◽  
Electrochemical Noise ◽  
Corrosion Monitoring ◽  
Monitoring Technique ◽  
Artificial Neural

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

scholarly journals Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Nagoor Basha Shaik ◽  
Kedar Mallik Mantrala ◽  
Balaji Bakthavatchalam ◽  
Qandeel Fatima Gillani ◽  
M. Faisal Rehman ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Corrosion Rate ◽  
Prediction Models ◽  
Test Time ◽  
Bayesian Regularization ◽  
Cobalt Chromium ◽  
Corrosion Properties ◽  
Cocrmo Alloy ◽  
Artificial Neural

AbstractThe well-known fact of metallurgy is that the lifetime of a metal structure depends on the material's corrosion rate. Therefore, applying an appropriate prediction of corrosion process for the manufactured metals or alloys trigger an extended life of the product. At present, the current prediction models for additive manufactured alloys are either complicated or built on a restricted basis towards corrosion depletion. This paper presents a novel approach to estimate the corrosion rate and corrosion potential prediction by considering significant major parameters such as solution time, aging time, aging temperature, and corrosion test time. The Laser Engineered Net Shaping (LENS), which is an additive manufacturing process used in the manufacturing of health care equipment, was investigated in the present research. All the accumulated information used to manufacture the LENS-based Cobalt-Chromium-Molybdenum (CoCrMo) alloy was considered from previous literature. They enabled to create a robust Bayesian Regularization (BR)-based Artificial Neural Network (ANN) in order to predict with accuracy the material best corrosion properties. The achieved data were validated by investigating its experimental behavior. It was found a very good agreement between the predicted values generated with the BRANN model and experimental values. The robustness of the proposed approach allows to implement the manufactured materials successfully in the biomedical implants.

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