A machine-learning architecture with two strategies for low-speed impact localization of composite laminates

Shen, Junhe, Ye, Junjie, Qu, Zhiqiang, Liu, Lu, Yang, Wenhu, Zhang, Yong, Chen, Yixin and Liu, Dianzi (2024) A machine-learning architecture with two strategies for low-speed impact localization of composite laminates. Measurement, 237. ISSN 1873-412X

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In this paper, a machine-learning architecture with the integration of two strategies including data enhancement and adaptive generation scheme for Impact Localization (IL) are developed to address the aforementioned issues for location identification of impacts on composite laminates. Two main contributions are included in this research: First, response signals collected from low-speed impact experiments under various working conditions are denoised using Adaptive Sparse Noise Reduction Algorithm (ASNRA), which aims at maximizing the preservation of the original signal amplitude, thereby avoiding the underestimation of pulse features during denoising. Then a RIME-optimized Dual-layer Support Vector Regression (RDSVR) method for the real-time update of hyperparameters is implemented in the machine-learning architecture to realize IL. The superior performances of the IL architecture over different IL models are validated throughout the numerical examples in terms of stability and efficiency. Results demonstrate that proposed architecture has the ability to realize the accurate and robust IL of composite laminates.

Item Type: Article
Additional Information: Data availability statement: Data will be made available on request. Funding information: This work was supported by the National Natural Science Foundation of China, China (No. 52175112, 52075406). Fundamental Research Funds for the Central Universities (No. JB210421). The 111 Project, China (No. B14042). The Shaanxi Key Laboratory Open Project (Grant No. 300102253508).
Faculty \ School: Faculty of Science > School of Engineering
Depositing User: LivePure Connector
Date Deposited: 01 Jul 2024 11:34
Last Modified: 10 Jul 2024 15:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/95723
DOI: 10.1016/j.measurement.2024.115213

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