Tian S X, Ma J D, Li G K, et al.Damage quantitative identification of laminated composite materials based on improved particle swarm optimization algorithm[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(6):147-156.

doi:10.16186/j.cnki.1673-9787.2025090026

Received:2025/09/18

Revised:2026/03/20

Published:2026/10/09

Damage quantitative identification of laminated composite materials based on improved particle swarm optimization algorithm

Tian Shuxia1, Ma Jiangdong2, Li Guangke1, Du Wenliao1, Qin Zhihui1, Wang Shunqiang1

1.Henan Provincial Key Laboratory of Intelligent Manufacturing of High-End Equipment， Zhengzhou University of Light Industry， Zhengzhou  450002， Henan， China；2.Henan Research Institute of Boiler and Pressure Vessel Inspection Technology， Zhengzhou  450016， Henan， China

Abstract: Objectives To address the bottlenecks in damage identification of laminated composite materials， such as low accuracy， poor efficiency， and dependence on reference models， two quantitative damage identification methods based on improved particle swarm optimization （IPSO） are proposed.  Methods Method Ⅰ. A progressive two-stage strategy is adopted. First， a differential-based approach is used to rapidly locate potential damaged elements， reducing the optimization variable dimension by over 90% and significantly decreasing computational complexity. Then， an improved PSO（IPSO） algorithm with adaptive inertia weight adjustment and dynamic learning factor mechanisms is adopted to enhance local search capability and convergence stability， enabling accurate damage localization and quantification. Method Ⅱ. addressing the limitation of Method Ⅰ which relying on a reference model， a self contained single algorithm detection system by introducing a population initialization strategy tailored to discretized structures it constructed， making the initial population closer to the true damage solution space and accelerating convergence. A boundary rebound strategy is also incorporated to dynamically focus on high-probability damage regions， avoiding resource waste from full-model search. This allows damage characterization to be decoupled using only data from the damaged structure.  Results Numerical simulations and experimental tests demonstrate that both methods achieve accurate damage localization and quantification， with simulation errors of damage extent maintained below 0.5%. In experimental tests， Method Ⅰ yields relatively stable results across multiple detections， while Method Ⅱ shows noticeable fluctuations in damage quantification due to factors such as measurement noise and discrepancies between the simulation model and the actual structure.  Conclusions Comparison of the two methods reveals that as the number of variables increases， the convergence iterations of Method Ⅱ are significantly higher than those of Method Ⅰ. Therefore， Method Ⅰ is more suitable for rapid detection scenarios with complete reference data， whereas Method Ⅱ offers greater practical applicability when reference models are unavailable.

Key words: damage identification; laminated composite materials; particle swarm optimization; vibration response parameters; quantitative detection