Journal
MATERIALS TODAY COMMUNICATIONS
Volume 26, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.mtcomm.2020.101897
Keywords
Aluminum alloys; Machine learning; Hardness; Age hardening; Gradient boosted tree
Categories
Funding
- National Research Foundation (NRF) of South Korea [2020R1A2C1004720]
- National Research Foundation of Korea [2020R1A2C1004720] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ask authors/readers for more resources
Through the use of various machine learning techniques, this study accelerated the process of designing aluminum alloys and found that the model obtained by gradient boosted tree (GBT) could efficiently predict the hardness of alloys.
In this work, various machine learning (ML) techniques were employed to accelerate the designing of aluminum (Al) alloys with improved performance based on the age hardening concept. For this purpose, data of Al-Cu-Mg-x (x: Zn, Zr, etc.) alloys, including composition, aging condition (time and temperature), important physical and chemical properties, and hardness were collected from the literature to train the ML algorithms for predicting Al alloys with superior hardness. The results showed that the model obtained by the gradient boosted tree (GBT) could efficiently predict the hardness of unexplored alloys.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available