Rights statement: This is the author’s version of a work that was accepted for publication in Materials and Design. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Materials and Design, 157, 2018 DOI: 10.1016/j.matdes.2018.07.005
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Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - ANN prediction of corrosion behaviour of uncoated and biopolymers coated cp-Titanium substrates
AU - Kumari, Suman
AU - Tiyyagura, Hanuma Reddy
AU - Douglas, Timothy E.L.
AU - Mohammed, Elbeshary A.A.
AU - Adriaens, Annemie
AU - Fuchs-Godec, Regina
AU - Mohan, M.K.
AU - Skirtach, Andre G.
N1 - This is the author’s version of a work that was accepted for publication in Materials and Design. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Materials and Design, 157, 2018 DOI: 10.1016/j.matdes.2018.07.005
PY - 2018/11/5
Y1 - 2018/11/5
N2 - The present study focuses on biopolymer surface modification of cp-Titanium with Chitosan, Gelatin, and Sodium Alginate. The biopolymers were spin coated onto a cp-Titanium substrate and further subjected to Electrochemical Impedance Spectroscopic (EIS) characterization. Artificial Neural Network (ANN) was developed to predict the Open Circuit Potential (OCP) values and Nyquist plot for bare and biopolymer coated cp-Titanium substrate. The experimental data obtained was utilized for ANN training. Two input parameters, i.e., substrate condition (coated or uncoated) and time period were considered to predict the OCP values. Backpropagation Levenberg-Marquardt training algorithm was utilized in order to train ANN and to fit the model. For Nyquist plot, the network was trained to predict the imaginary impedance based on real impedance as a function of immersion periods using the Back Propagation Bayesian algorithm. The biopolymer coated cp-Titanium substrate shows the enhanced corrosion resistance compared to uncoated substrates. The ANN model exhibits excellent comparison with the experimental results in both the cases indicating that the developed model is very accurate and efficiently predicts the OCP values and Nyquist plot.
AB - The present study focuses on biopolymer surface modification of cp-Titanium with Chitosan, Gelatin, and Sodium Alginate. The biopolymers were spin coated onto a cp-Titanium substrate and further subjected to Electrochemical Impedance Spectroscopic (EIS) characterization. Artificial Neural Network (ANN) was developed to predict the Open Circuit Potential (OCP) values and Nyquist plot for bare and biopolymer coated cp-Titanium substrate. The experimental data obtained was utilized for ANN training. Two input parameters, i.e., substrate condition (coated or uncoated) and time period were considered to predict the OCP values. Backpropagation Levenberg-Marquardt training algorithm was utilized in order to train ANN and to fit the model. For Nyquist plot, the network was trained to predict the imaginary impedance based on real impedance as a function of immersion periods using the Back Propagation Bayesian algorithm. The biopolymer coated cp-Titanium substrate shows the enhanced corrosion resistance compared to uncoated substrates. The ANN model exhibits excellent comparison with the experimental results in both the cases indicating that the developed model is very accurate and efficiently predicts the OCP values and Nyquist plot.
U2 - 10.1016/j.matdes.2018.07.005
DO - 10.1016/j.matdes.2018.07.005
M3 - Journal article
VL - 157
SP - 35
EP - 51
JO - Materials and Design
JF - Materials and Design
SN - 0261-3069
ER -