High-fidelity computational modeling of scratch damage in automotive coatings with machine learning-driven identification of fracture parameters

Yang Hanming; Zou Chenqi; Huang MinfeiZang MengyanChen Shunhua

首页> 外文期刊>Composite structures >High-fidelity computational modeling of scratch damage in automotive coatings with machine learning-driven identification of fracture parameters

【24h】

High-fidelity computational modeling of scratch damage in automotive coatings with machine learning-driven identification of fracture parameters

机译：High-fidelity computational modeling of scratch damage in automotive coatings with machine learning-driven identification of fracture parameters

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

As one of the key components of a vehicle, automotive coating systems are typical micro-size multilayer com-posites, which normally suffer from intricate scratch damage. Currently, it is a challenging task to reproduce the complex physical phenomena of automotive coatings with micron thickness using numerical simulations. The purpose of this work is to develop a computational framework for an accurate evaluation of such scratch damage. To achieve this end, a high-fidelity finite element (FE) model is proposed, where a rate-dependent cohesive zone model is developed to account for coating crack behaviors. A reverse identification approach based on the machine learning (ML) method is suggested to determine the rate-dependent cohesive parameters that are difficult to be measured via direct experiments. In the course of recognition, this work develops a two-step regional data augmentation strategy based on the existing single-shot recognition method. The developed strategy is capable of reducing the number of training data samples, while improving identification accuracy and robustness. With the identified cohesive parameters, the proposed finite element model is applied to simulate speed-dependent coating scratch behaviors. The good agreement between numerical and experimental results demonstrates the capacity of our developed computational framework for coating scratch problems.

著录项

来源
《Composite structures》 |2023年第7期|1.1-1.23|共23页
作者
Yang Hanming; Zou Chenqi; Huang MinfeiZang MengyanChen Shunhua;
展开▼
作者单位

South China Univ Technol;

Sun Yat sen Univ||Southern Marine Sci & Engn Guangdong Lab Zhuhai;

展开▼
收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类
关键词
Data -driven approach; Rate -dependent cohesive zone model; Parameter reverse identification; High-fidelity FEM model; Scratch damage;

High-fidelity computational modeling of scratch damage in automotive coatings with machine learning-driven identification of fracture parameters

摘要

著录项

相关主题

期刊订阅