Machine learning prediction for low-alloy steel strength

Zilong Zhou

doi:10.1117/12.2672650

28 February 2023 Machine learning prediction for low-alloy steel strength

Zilong Zhou

Proceedings Volume 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022); 1259620 (2023) https://doi.org/10.1117/12.2672650
Event: International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 2022, Changsha, China

Abstract

The experimental measurement of the strength of low-alloy steel is very cumbersome, but it is also essential to knowledge its strength. In this study, two machine learning methods, random forest (RF) and support vector machine (SVM), were used to study the strength of low-alloy steels on the existing data samples of low-alloy steels, so as to make relevant predictions on their strengths and find the most influential factors. Comparing the measured results with the predicted values shows that RF outperform SVM in predicting results. And by calculating the correlation coefficient, the two features that have the greatest influence on the strength are the temperature and the content of V, respectively. This result can be used to optimize the properties of low-alloys.

Citation Download Citation

Zilong Zhou "Machine learning prediction for low-alloy steel strength", Proc. SPIE 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 1259620 (28 February 2023); https://doi.org/10.1117/12.2672650

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available