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

The 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 predict...

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Bibliographic Details
Published in:Journal of bio- and tribo-corrosion 2021, Vol.7 (3), Article 116
Main Authors: Shaik, Nagoor Basha, Mantrala, Kedar Mallik, Bakthavatchalam, Balaji, Gillani, Qandeel Fatima, Rehman, M. Faisal, Behera, Ajit, Rajak, Dipen Kumar, Pruncu, Catalin I.
Format: Article
Language:English
Subjects:
Aging
Alloys
Artificial neural networks
Bayesian analysis
Biomaterials
Biomedical materials
Chemistry and Materials Science
Chromium
Cobalt
Corrosion
Corrosion and Coatings
Corrosion potential
Corrosion rate
Corrosion tests
Depletion
Heavy metals
Lenses
Materials Science
Mathematical models
Metallurgy
Molybdenum
Neural networks
Prediction models
Regularization
Solid Mechanics
Surgical implants
Testing time
Tribology
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Summary:The 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.
ISSN:2198-4220
2198-4239
DOI:10.1007/s40735-021-00550-3