Advances in neural networks and potential for their application to steel metallurgy

Abstract

This review provides a timely exploration of several novel neural network (NN) architectures and learning methods, following a concise overview of the fundamentals of NNs and some important associated challenges. There are many benefits to using NNs, including deep learning models, in scientific research and, by understanding novel techniques better suited to certain applications, this benefit can be maximised. Finally, a few developed and emerging alternative learning paradigms are surveyed for their potential benefit to future research. The reviewed literature and accompanying discussion are of generic value well beyond steel metallurgy, and there is much to be gained from assessing methods used in other areas of materials science and further afield in order to apply them to steel metallurgy.

Keywords:

• Deep learning
• steel
• metallurgy
• modelling
• machine learning
• neural network
• generative adversarial network

Acknowledgments

The author would like to thank H. K. D. H. Bhadeshia for his valuable discussions throughout the writing of this review.

Disclosure statement

No potential conflict of interest was reported by the author(s).