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

Shaik, N.B. and Mantrala, K.M. and Bakthavatchalam, B. and Gillani, Q.F. and Rehman, M.F. and Behera, A. and Rajak, D.K. and Pruncu, C.I. (2021) Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN). Journal of Bio- and Tribo-Corrosion, 7 (3).

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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. © 2021, Crown.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:3D printers; Additives; Barium alloys; Chromium alloys; Chromium metallurgy; Cobalt alloys; Cobalt metallurgy; Corrosion rate; Corrosive effects; Forecasting; Industrial research; Molybdenum alloys; Molybdenum metallurgy; Predictive analytics, Additive manufacturing process; Bayesian regularization; Biomedical implants; Cobalt chromium molybdenums; Corrosion potentials; Corrosion test time; Experimental values; Laser engineered net shaping, Neural networks
ID Code:23950
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 13:23
Last Modified:19 Aug 2021 13:23

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