点击次数：
影响因子：2.1
DOI码：10.2514/1.J062417
发表刊物：AIAA Journal
摘要：Adhesive bonding is widely adopted in aeronautic structures to join composite materials or to repair damaged substrates. However, one of the most common failure modes for this type of joint is debonding under fatigue loading. In the past years, it has been proven that deboning quantification is feasible, given that abundant experimental data are available. In this context, using domain adaptation to assist diagnostic tasks based on labeled data from similar structures or simulations would be thoroughly beneficial. However, most domain adaptation methods are designed for classifications and cannot efficiently address regressions. A fuzzy-set-based joint distribution adaptation for regression method has been developed by the authors, tackling regression problems but being limited to single outputs. The novelty presented in this paper exploits clustering techniques to approach multi-output problems, adopting a modified multikernel maximum mean discrepancy to improve the domain discrepancy metric. The proposed method is applied to cracked lap shear specimens to assist debonding quantification. Several domain adaptations are investigated: from simulations to experiments, and from one specimen to another, proving that the accuracy of damage quantification can be improved significantly in realistic environments. It is envisioned that the proposed approach could be integrated into fleet-level digital twins for nominally identical but heterogeneous systems.
合写作者：Dario Poloni,Daniele Oboe,Claudio Sbarufatti*,董雷霆*,Marco Giglio
第一作者：周轩
论文类型：期刊论文
一级学科：航空宇航科学与技术
文献类型：期刊
卷号：61
期号：2
页面范围：831–842
是否译文：否
发表时间：2023-02-01
收录刊物：SCI
发布期刊链接：https://arc.aiaa.org/doi/10.2514/1.J062417