Work

Data-driven optimization of material composition and casting processes for large ferritic nodular cast iron components in wind turbines

Year: 2024

Type: conference-paper

Abstract: This study addresses the challenges of producing large-scale wind turbine components by proposing an optimization scheme for the material properties and casting processes of ductile cast iron. The study integrates experimental data with JMatPro calculations to establish a comprehensive database of 1, 139 ferritic nodular cast iron samples. Using machine learning and multi-objective optimization, an optimized composition was developed, and Pareto front solutions were achieved with performance significantly surpassing QT400-18. The AI-CAST platform was then applied to refine the casting process for a 6 MW wind turbine hub, reducing internal defects to 2, 738.52 mm³ and decreasing loose defects by 83.68%. This integrated approach not only enhances material performance and casting quality but also offers significant cost savings, suggesting broad applicability for improved production efficiency in the casting industry.

Source: Journal of Physics Conference Series

DOI: 10.1088/1742-6596/2921/1/012002

Authors: Fei Su, Donghong Wang, Jing Xue, Xiaoming Deng, Qianhai Gong, Xiaojiang Xia, Xianglin Su, Da Shu

Institutions: Shanghai University of Engineering Science, Shanghai Jiao Tong University, Zhejiang Institute of Mechanical and Electrical Engineering, China North Industries Group Corporation (China)

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4405240750

FWCI: 3.718

Cites: 8

Cited by: 2

Related to: 10