Temporal Prediction of Laser Damage in 7075 Aluminum Alloy Under Tangential Airflow Based on YOLO26 Real-Time Vision Model and Piecewise Exponential Method

To address the difficulty in continuously and accurately quantifying the time-dependent damage size of targets under coupled laser–tangential airflow action, this paper proposes a piecewise exponential damage inversion modeling method combining deep learning and physical driving. A synchronous laser–tangential airflow experimental platform is built, and 0–5 s damage time-series images are collected to construct a dataset. The YOLO26 real-time visual detection network is selected to realize automatic identification of ablation molten spot regions, with model precision of 0.984, mAP@0.5:0.95 = 0.966, and single-frame inference time of 27.6 ms. Relying on a self-developed visual interactive program, batch time-series image automatic recognition and size conversion are completed, and 1.6–5.0 s is determined as the reliable quantitative interval. Based on the effective data in this interval, the competitive coupling effect between airflow shear-induced melting enhancement and convective cooling is analyzed. A piecewise exponential evolution model containing a quadratic modulation term of tangential airflow is constructed, which well describes the three-stage nonlinear law of damage “initiation–expansion–stabilization”. A genetic algorithm is used to complete global parameter fitting under multiple working conditions. The average relative errors of the model for damage length and width are both lower than 2.1%, RMSE is less than 0.7 mm, MAE is less than 0.52 mm, and R2 is higher than 0.994. This work can provide a theoretical basis and technical scheme for quantitative time-series analysis of damage in metal targets irradiated by high-energy lasers.