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

Junhe Shen, Junjie Ye, Zhiqiang Qu, Lu Liu, Wenhu Yang, Yong Zhang, Yixin Chen, Dianzi Liu

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Article number115213
JournalMeasurement
Volume237
Early online date30 Jun 2024
DOIs
Publication statusE-pub ahead of print - 30 Jun 2024

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