Deep Neural Network as a Tool to Classify and Identify the 316L SS And AZ31BMg Metal Surface Morphology: An Empirical Study

Document Type

Article

Publication Title

Engineered Science

Abstract

Identifying the severity of corrosion is crucial in physical and biological sciences. Developing a highly accurate deep learning model capable of classifying corrosion severity across a wide metal surface, even with limited training data, represents a significant advancement over traditional investigation methods. Unlike traditional approaches such as electrochemical measurements that assess only provided inspection areas, deep learning models can classify the entire metal surface. In the realm of biomaterials, this corrosion identification approach aids researchers in selecting appropriate materials for body implants, reducing the impact of corroded metal reactions within the implanted body. This research advocates for an objective and automated examinationof metal surfaces, employing convolutional neural networks to classify corrosion intensity based on scanning electron microscope (SEM) images. Despite the limited number of samples from electrochemical laboratories, the deep learning model provides valuable insights across the entire metal plate surface, effectively distinguishing between different corrosion states. Electrochemical measurements were implemented to see the corrosion such as electrochemical impedance spectroscopy (EIS), potentiodynamic polarisation (PDP) techniques. Generative Adversarial Network (GAN) is implemented to generate synthetic images. SEM images were obtained to evaluate the changes at microlevel and CNN was used to classify the images with an efficiency of 92.7%.

DOI

10.30919/es1064

Publication Date

12-1-2023

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