Aluminium Alloy Design and Discovery using Machine Learning

Autor:	Mangos, J., Birbilis, N.
Rok vydání:	2021
Předmět:	Condensed Matter - Materials Science Condensed Matter - Disordered Systems and Neural Networks
Druh dokumentu:	Working Paper
Popis:	The traditional design and development of metallic alloys has taken a hill-climbing approach to date, with incremental advances. Throughout the last century, aluminium (Al) alloy design has been essentially empirical and iterative, based on lessons learned from in service use and human experience. Incremental alloy development is costly, slow, and doesn't fully harness the data that exists in the field of Al-alloy metallurgy. In the present work, an attempt has been made to utilise a data science approach to develop a machine learning (ML) model for Al-alloy design. An objective-optimisation process has also been developed, to exploit the ML model, for user experience and practical application. A successful model was developed and presented herein, along with the open-access software. Comment: Includes links to software. Seeking feedback on work
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.14806 Zobrazit plný text záznamu View this record from Arxiv