Corrosion behavior of LENS deposited CoCrMo alloy using Bayesian regularization-based artificial neural network (BRANN)

Nagoor Basha Shaik, Kedar Mallik Mantrala, Balaji Bakthavatchalam, Qandeel Fatima Gillani, M. Faisal Rehman, Ajit Behera, Dipen Kumar Rajak, Catalin I. Pruncu

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Abstract

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.

Original languageEnglish
Article number116
Number of pages13
JournalJournal of Bio- and Tribo-Corrosion
Volume7
Issue number3
Early online date18 Jun 2021
DOIs
Publication statusE-pub ahead of print - 18 Jun 2021

Keywords

  • Laser engineered net shaping (LENS)
  • additive manufacturing
  • CoCrMo alloy
  • artificial neural networks
  • corrosion

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