复合数
计算机科学
质量(理念)
固化(化学)
可靠性工程
材料科学
工程类
算法
复合材料
认识论
哲学
作者
Yu-Cheng Wang,Fei Tao,Ying Zuo,Meng Zhang,Qinglin Qi
出处
期刊:Engineering
[Elsevier BV]
日期:2023-01-11
卷期号:22: 23-33
被引量:20
标识
DOI:10.1016/j.eng.2022.08.019
摘要
Composite materials are widely used in many fields due to their excellent properties. Quality defects in composite materials can lead to lower quality components, creating potential risk of accidents. Experimental and simulation methods are commonly used to predict the quality of composite materials. However, it is difficult to predict the quality of composite materials accurately due to the uncertain curing environment and incomplete feature space. To address this problem, a digital twin (DT) visual model of a composite material is first constructed. Then, a static autoclave DT virtual model is coupled with a variable composite material DT virtual model to construct a model of the curing process. Features are added to the proposed model by generating simulated data to enhance the quality prediction. An extreme learning machine (ELM) for quality prediction is trained with the generated data. Finally, the effectiveness of the proposed method is verified through result analysis.
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