Artificial neural network to predict the degraded mechanical properties of metallic materials due to the presence of hydrogen. (23rd November 2017)

• Record Type: Journal Article
• Authors: Thankachan, Titus; Prakash, K. Soorya; David Pleass, Christopher; Rammasamy, Devaraj; Prabakaran, Balasubramanian; Jothi, Sathiskumar
• Is Part Of: International journal of hydrogen energy. Volume 42:Number 47(2017)
• Page Start: 28612
• Page End: 28621
• Publication Date: 2017-11-23
• Subjects: Machine learning models -- Hydrogen -- Metallic materials -- Mechanical properties; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2017.09.149
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: Machine learning models were introduced to develop a relationship between the elemental composition and degraded mechanical properties in metallic materials due to the presence of hydrogen. Single layer and multilayer feed forward back propagation algorithm was developed as artificial neural network based machine learning models to predict the mechanical properties of hydrogen charged metallic materials. Multilayer feed forward back propagation model was used to predicts the tensile strength, had a network topology of 12-13-3-2. And the single layer feed forward back propagation model was employed to predict the percentage of elongation, has a network topology of 12-11-1. The developed models were validated and tested with unknown inputs and their capability was studied. The models were evaluated using Mean Absolute (MAE) value and represented the scatter diagram to demonstrate the efficiency of the models. The R-value for both the models seems to prove that the models are ready to be used in the practical applications. Highlights: Introduced Machine learning for degraded mechanical properties in hydrogen charged metallic materials. Developed ANN models based Single layered and multilayered feed forward back propagation algorithm. The elemental composition and degraded mechanical property relationships was predicted. Tested and validated the models and demonstrated the efficiency of the models.