A deep learning approach to impact localization and uncertainty assessment in CFRP composites using sparse PZTs: Integrating experiments and simulations

IF 5.7 1区 工程技术 Q1 ENGINEERING, CIVIL Thin-Walled Structures Pub Date : 2025-03-10 DOI:10.1016/j.tws.2025.113143
Huai Yan, Weihua Xie, Bo Gao, Fan Yang, Songhe Meng
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Abstract

The propagation of elastic waves in CFRP composites is dispersive and multimodal, making impact localization using wave signals challenging. An end-to-end deep learning model with an encoder-decoder architecture was developed for impact localization in CFRP composites, using sparse piezoelectric ceramic transducer (PZT) arrays and a data-driven approach to enable online impact sensing. The model used a segmented training strategy and transfer learning to capture shared features between experimental and simulated data. According to the piezoelectric equation, it links experimental piezoelectric signals with simulated stress responses. The results show that the feature encoder trained can extract their shared features effectively. Meanwhile, the model successfully applied the laws from simulation data to the localization of experimental impacts based on the fine-tuning strategy, alleviating the challenge of limited experimental data. The prediction results in the test set show that good generalization, and impact localization can be achieved in milliseconds during inference. The average error in localization is only 3.42 mm over a 100 mm × 100 mm monitoring area. Compared to the traditional transfer strategy, the three-stage training proposed shows better generalization by constraining the encoded features. In addition, the Monte-Carlo dropout strategy is used to assess prediction uncertainty, analyzing the effects of dropout rate and repeated predictions on confidence intervals. The study provides a prospective solution for large CFRP structures to achieve fast localization of impacts under sparse PZT arrays.
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利用稀疏 PZT 对 CFRP 复合材料进行冲击定位和不确定性评估的深度学习方法:实验与模拟相结合
CFRP 复合材料中弹性波的传播具有分散性和多模态性,因此使用波信号进行冲击定位具有挑战性。利用稀疏压电陶瓷传感器(PZT)阵列和数据驱动方法,开发了一种端到端的深度学习模型,采用编码器-解码器架构,用于 CFRP 复合材料中的冲击定位,以实现在线冲击传感。该模型采用分段训练策略和迁移学习来捕捉实验数据和模拟数据之间的共享特征。根据压电方程,它将实验压电信号与模拟应力响应联系起来。结果表明,所训练的特征编码器能有效提取它们的共享特征。同时,基于微调策略,该模型成功地将模拟数据中的规律应用于实验冲击的定位,缓解了实验数据有限的难题。测试集的预测结果表明,推理过程中可以在几毫秒内实现良好的泛化和撞击定位。在 100 毫米 × 100 毫米的监测区域内,定位的平均误差仅为 3.42 毫米。与传统的转移策略相比,所提出的三阶段训练通过对编码特征的限制,显示出更好的泛化效果。此外,该研究还使用蒙特卡洛辍学策略来评估预测的不确定性,分析辍学率和重复预测对置信区间的影响。该研究为大型 CFRP 结构提供了一种前瞻性解决方案,可在稀疏 PZT 阵列下实现冲击的快速定位。
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来源期刊
Thin-Walled Structures
Thin-Walled Structures 工程技术-工程:土木
CiteScore
9.60
自引率
20.30%
发文量
801
审稿时长
66 days
期刊介绍: Thin-walled structures comprises an important and growing proportion of engineering construction with areas of application becoming increasingly diverse, ranging from aircraft, bridges, ships and oil rigs to storage vessels, industrial buildings and warehouses. Many factors, including cost and weight economy, new materials and processes and the growth of powerful methods of analysis have contributed to this growth, and led to the need for a journal which concentrates specifically on structures in which problems arise due to the thinness of the walls. This field includes cold– formed sections, plate and shell structures, reinforced plastics structures and aluminium structures, and is of importance in many branches of engineering. The primary criterion for consideration of papers in Thin–Walled Structures is that they must be concerned with thin–walled structures or the basic problems inherent in thin–walled structures. Provided this criterion is satisfied no restriction is placed on the type of construction, material or field of application. Papers on theory, experiment, design, etc., are published and it is expected that many papers will contain aspects of all three.
期刊最新文献
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