Zhiyong Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Zhiyong Li has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Shuxia Tian
Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
2 shared publications - Jiangdong Ma
Henan Boiler and Pressure Vessel Inspection Technology Research Institute, Zhengzhou 450016, China
1 shared publication Luyang Zhang
Zhengzhou University of Light Industry
1 shared publication- Luyang Zhang
Henan Provincial Key Laboratory of Intelligent Manufacturing of High-End Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China
1 shared publication Pengpeng Liu
Henan Research Institute of Boiler and Pressure Vessel Inspection Technology
1 shared publicationShunqiang Wang
Zhengzhou University of Light Industry
1 shared publication
Latest Publications
- Open Access
Baseline-Free Damage Localization for Layered Composites Based on Nonlinear Damage Coefficient and Whale Optimization Algorithm
Layered carbon fiber composite materials are widely employed in key load-bearing components such as aircraft wings and fuselages in the aerospace industry owing to their high strength-to-weight ratio. However, they are prone to hidden internal damage including delamination and debonding during service, which poses a severe threat to the operational safety and structural integrity of aircraft structures. Traditional probabilistic imaging algorithms for ultrasonic guided wave inspection of layered composites depend on intact baseline signals. Tiny damage features are easily masked by noise, which results in weak imaging convergence and unsatisfactory localization accuracy. This paper proposes a baseline-free damage localization method based on the Nonlinear Damage Coefficient (NDC). The energy-normalized NDC is constructed from the nonlinear scattering responses under dual-amplitude excitations, and a power adjustment factor is introduced to distinguish damage signatures from background noise, thus improving imaging contrast. To resolve the subjectivity of manual parameter selection, the Whale Optimization Algorithm (WOA) is applied to realize joint adaptive optimization of the NDC power exponent and probabilistic imaging shape factors and . Numerical simulations and physical experiments are conducted on carbon fiber-reinforced polymer (CFRP) laminates with central single damage, offset single damage and double damage to validate the proposed method. The experimental results demonstrate that single and multiple damage sites can be identified without baseline signals. After WOA optimization, remarkable localization-error reduction is observed in numerical simulation cases, and imaging artifacts were effectively suppressed. Especially for offset and double damage cases, the relative localization error was maintained below 0.5%. Validated under well-controlled laboratory experimental conditions, the proposed method achieves high localization precision and provides feasible technical support for non-destructive testing and structural health monitoring of layered composite structures.Open AccessBaseline-Free Damage Localization for Layered Composites Based on Nonlinear Damage Coefficient and Whale Optimization Algorithm
Layered carbon fiber composite materials are widely employed in key load-bearing components such as aircraft wings and fuselages in the aerospace industry owing to their high strength-to-weight ratio. However, they are prone to hidden internal damage including delamination and debonding during service, which poses a severe threat to the operational safety and structural integrity of aircraft structures. Traditional probabilistic imaging algorithms for ultrasonic guided wave inspection of layered composites depend on intact baseline signals. Tiny damage features are easily masked by noise, which results in weak imaging convergence and unsatisfactory localization accuracy. This paper proposes a baseline-free damage localization method based on the Nonlinear Damage Coefficient (NDC). The energy-normalized NDC is constructed from the nonlinear scattering responses under dual-amplitude excitations, and a power adjustment factor is introduced to distinguish damage signatures from background noise, thus improving imaging contrast. To resolve the subjectivity of manual parameter selection, the Whale Optimization Algorithm (WOA) is applied to realize joint adaptive optimization of the NDC power exponent and probabilistic imaging shape factors and . Numerical simulations and physical experiments are conducted on carbon fiber-reinforced polymer (CFRP) laminates with central single damage, offset single damage and double damage to validate the proposed method. The experimental results demonstrate that single and multiple damage sites can be identified without baseline signals. After WOA optimization, remarkable localization-error reduction is observed in numerical simulation cases, and imaging artifacts were effectively suppressed. Especially for offset and double damage cases, the relative localization error was maintained below 0.5%. Validated under well-controlled laboratory experimental conditions, the proposed method achieves high localization precision and provides feasible technical support for non-destructive testing and structural health monitoring of layered composite structures. - 16 September 2026
- Published by SAGE Publications in International Journal of Applied Electromagnetics and Mechanics
- Vol. 80 (9) , 359-372
Defect detection of composite laminated plates based on ultrasonic lamb waves and deep learning algorithms
To address the defect detection problem of composite laminate, this study proposes a defect defection detection method based on ultrasonic Lamb waves and deep learning algorithms. Taking an anisotropic healthy composite laminate as the base model, simulation models with different defects were generated through batch modifications via code, and the displacement-time data of ultrasonic guided wave propagation in these structures were obtained through simulation analysis. The data were resampled to construct a training dataset with consistent compression lengths while preserving the waveform features of different parameters. Based on this dataset, an active learning strategy was employed to iteratively train an artificial neural network, thereby establishing a surrogate model capable of predicting the corresponding defect waveform from the input defect parameters. By combining this surrogate model with approximate Bayesian inference and subset simulation algorithms, the posterior distribution of defect parameters was inverted from the real waveform signals, and a set of possible defect parameters was obtained through posterior distribution sampling. Analysis of multiple groups of defect inversion data and comparative verification with existing literature methods demonstrate that the proposed method can efficiently and accurately predict the defect parameters of composite laminate, and exhibit significant advantages over other methods in terms of the portability of measurement operations and detection accuracy.
- 16 September 2026
- Published by SAGE Publications in International Journal of Applied Electromagnetics and Mechanics
- Vol. 80 (9) , 359-372
Defect detection of composite laminated plates based on ultrasonic lamb waves and deep learning algorithms
To address the defect detection problem of composite laminate, this study proposes a defect defection detection method based on ultrasonic Lamb waves and deep learning algorithms. Taking an anisotropic healthy composite laminate as the base model, simulation models with different defects were generated through batch modifications via code, and the displacement-time data of ultrasonic guided wave propagation in these structures were obtained through simulation analysis. The data were resampled to construct a training dataset with consistent compression lengths while preserving the waveform features of different parameters. Based on this dataset, an active learning strategy was employed to iteratively train an artificial neural network, thereby establishing a surrogate model capable of predicting the corresponding defect waveform from the input defect parameters. By combining this surrogate model with approximate Bayesian inference and subset simulation algorithms, the posterior distribution of defect parameters was inverted from the real waveform signals, and a set of possible defect parameters was obtained through posterior distribution sampling. Analysis of multiple groups of defect inversion data and comparative verification with existing literature methods demonstrate that the proposed method can efficiently and accurately predict the defect parameters of composite laminate, and exhibit significant advantages over other methods in terms of the portability of measurement operations and detection accuracy.