| 题名 | Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes |
| 作者 | |
| 通讯作者 | Ge, Qi; Liao, Wei-Hsin |
| 发表日期 | 2024-10
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| DOI | |
| 发表期刊 | |
| ISSN | 2352-9407
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| EISSN | 2352-9415
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| 卷号 | 40 |
| 摘要 | The forward prediction and inverse design of 4D printing have primarily focused on 2D rectangular surfaces or plates, leaving the challenge of 4D printing parts with arbitrary shapes underexplored. This gap arises from the difficulty of handling varying input sizes in machine learning paradigms. To address this, we propose a novel machine learning-driven approach for forward prediction and inverse design tailored to 4D printed hierarchical architectures with arbitrary shapes. Our method encodes non-rectangular shapes with special identifiers, transforming the design domain into a format suitable for machine learning analysis. Using Residual Networks (ResNet) for forward prediction and evolutionary algorithms (EA) for inverse design, our approach achieves accurate and efficient predictions and designs. The results validate the effectiveness of our proposed method, with the forward prediction model achieving a loss below 10−2mm, and the inverse optimization model maintaining an error near 1 mm, which is low relative to the entire shape of the optimized model. These outcomes demonstrate the capability of our approach to accurately predict and design complex hierarchical structures in 4D printing applications. © 2024 Elsevier Ltd |
| 收录类别 | |
| 语种 | 英语
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| 学校署名 | 通讯
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| 资助项目 | W. L. acknowledges Research Grants Council (C4074-22G), Hong Kong Special Administrative Region, China, and The Chinese University of Hong Kong, Hong Kong (Project ID: 3110174). Q. G. acknowledges the National Natural Science Foundation of China (No. 12072142), the Key Talent Recruitment Program of Guangdong Province, China (No. 2019QN01Z438), and the support by the Science, Technology and Innovation Commission of Shenzhen Municipality, China under grant no. ZDSYS20210623092005017. M. B. acknowledges the support by the UK Engineering and Physical Sciences Research Council (EPSRC) (grant number EP/Y011457/1). For the purpose of open access, the author has applied a \u2018Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.W. L. acknowledges Research Grants Council ( C4074-22G ), Hong Kong Special Administrative Region, China , and The Chinese University of Hong Kong (Project ID: 3110174 ). Q. G. acknowledges the National Natural Science Foundation of China (No. 12072142 ), the Key Talent Recruitment Program of Guangdong Province (No. 2019QN01Z438 ), and the support by the Science, Technology and Innovation Commission of Shenzhen Municipality under grant no. ZDSYS20210623092005017 . For the purpose of open access, the author has applied a \u2018Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.
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| 出版者 | |
| EI入藏号 | 20243316869181
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| EI主题词 | Forecasting
; Inverse problems
; Learning algorithms
; Machine learning
; Network architecture
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| EI分类号 | Artificial Intelligence:723.4
; Machine Learning:723.4.2
|
| 来源库 | EV Compendex
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| 引用统计 |
被引频次[WOS]：1
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| 成果类型 | 期刊论文 |
| 条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/807047 |
| 专题 | 工学院_机械与能源工程系 南方科技大学 |
| 作者单位 | 1.Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong 2.Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Southern University of Science and Technology, Shenzhen; 518055, China 3.Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen; 518055, China 4.Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong 5.School of Mathematical Sciences, University of Science and Technology of China, Hefei; 230026, China 6.Department of Engineering, University of Exeter, Exeter, United Kingdom 7.School of Mechanical Engineering, Tongji University, Shanghai; 200092, China 8.Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham; NG11 8NS, United Kingdom 9.Institute of Intelligent Design and Manufacturing, The Chinese University of Hong Kong, Hong Kong |
| 第一作者单位 | 南方科技大学; 机械与能源工程系 |
| 通讯作者单位 | 南方科技大学; 机械与能源工程系 |
| 推荐引用方式 GB/T 7714 |
Jin, Liuchao,Yu, Shouyi,Cheng, Jianxiang,et al. Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes[J]. Applied Materials Today,2024,40.
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| APA |
Jin, Liuchao.,Yu, Shouyi.,Cheng, Jianxiang.,Ye, Haitao.,Zhai, Xiaoya.,...&Liao, Wei-Hsin.(2024).Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes.Applied Materials Today,40.
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| MLA |
Jin, Liuchao,et al."Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes".Applied Materials Today 40(2024).
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| 条目包含的文件 | 条目无相关文件。 | |||||
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