Speaker
Description
Compared with conventional optical excitation thermography techniques, such as pulsed thermography (PT), lock-in thermography (LIT), step-pulse thermography (SPT) and linear-chirp thermography, chirped-pulse thermal-wave radar thermography has been demonstrated to provide a higher signal-to-noise ratio (SNR) and more effective depth-profiling capability. This enhanced performance originates from the use of short chirped pulses, which maintain a nearly flat power spectrum over a broad bandwidth, even in the presence of diffusive attenuation. However, conventional laser-based signal-modulated heating schemes require both spatial and temporal modulation – typically generating a rectangular heating area via projection or lensing, followed by digital waveform modulation. This leads to localized heating areas and prolonged heating durations, which significantly limit their applicability in practical industrial inspections. In addition, industrial samples, such as cast components, often exhibit large-scale and complex geometries. To perform infrared thermography on such samples, they must typically be divided into multiple sub-areas for sequential heating and recording, introducing pronounced boundary effects and complex data concatenation issue. In this work, we propose a spatially modulated heating strategy combined with a robotic arm moving at a constant speed. In details, the heating source consists of multiple linear heating strips arranged with different spatial intervals, while the heat source and infrared camera remain fixed. The robotic arm moves the sample at a constant speed across the heating and recording area. Using a dynamic-to-static reconstruction algorithm, the chirped-pulse thermal-wave radar signal can be generated for each pixel. This approach enables continuous chirp-pulse thermal-wave radar thermography by replacing temporal modulation with spatial modulation, thereby not only simplifying the system and reducing overall cost but also allowing for uniform heating of large and complex samples. Experiments and simulations are conducted to optimize key parameter, and various image processing algorithms - including principal component thermography (PCT), pulsed phase thermography (PPT), and partial least square regression (PLSR) - are applied to further enhance detectability. Image quality is quantitatively evaluated using signal-to-noise ratio metrics. The results demonstrate that this method holds strong potential for real-world industrial inspection applications. Moreover, the strategy of replacing temporal modulation with spatial modulation is expected to be extended to other emerging applications, such as random-coded and adaptive thermography, broadening its versatility in non-destructive testing.
