Multimodal algorithm-optimized chirped ultrasound thermography for enhanced quantitative detection of metal fatigue cracks

Metal fatigue cracks under cyclic loading conditions cannot be ignored. Ultrasound-induced thermography has been emerged as an effective technique for crack detection, however, single-frequency ultrasound excitation often produces weak thermal signals, especially for small or shallow cracks, limiting identification accuracy. To overcome these challenges, Multimodal Algorithm-enhanced Chirped Ultrasound-Induced Thermography (MA-CUIT) was proposed for quantitative crack characterization. Firstly, metal cracks three-dimensional thermal-wave model under chirp excitation was established to analyze temperature distribution and thermal diffusion. Subsequently, a comprehensive feature extraction framework integrating frequency-domain, time-domain, and machine learning methods was developed under chirp ultrasound excitation, with systematic optimization of frequency modulation parameters to determine optimal excitation ranges. MA-CUIT experimental platform was established for quantification of real crack defects. The results indicate that MA-CUIT, achieved exceptional crack sizing accuracy with relative errors of 5.9% (FFT phase optimized), 6.7% (PLSR 1st coefficient optimized), and 6.3% (DOD phase optimized), establishing MA-CUIT optimized by FFT phase analysis as the most precise quantification method.