Ultrasonic non-destructive testing (UT) is a fundamental quality-control tool in critical industries such as aerospace and petrochemicals, yet practical defect data suffer from limited sample sizes, class imbalance, and high annotation costs, hindering deep-learning-based intelligent detection. This paper proposes a Physics-Constrained Denoising Diffusion Probabilistic Model (PC-DDPM) for high-fidelity ultrasonic defect-signal synthesis, improving generation quality through three aspects.
