Automated High-Precision Thermographic Inspection of Irregular Objects via Heat-Flux Isobaric Surface Feedback

Optically excited infrared thermography generates an instantaneous heat source on a composite’s surface via light pulses; the resulting heat diffusion is recorded by an infrared camera as temporal temperature data and then processed to reconstruct internal thermal features for surface and subsurface defect detection. However, irregular specimens often exhibit nonuniform heat flux distribution, high miss rates, and low automation, which limit performance. This paper presents a sensing–feedback 3D thermography method based on Heat Flux Density Isobaric Surfaces (HFDIS): it combines the system’s physical model with the specimen’s 3D geometry to construct HFDIS and dynamically adjusts a robotic arm’s pose to uniformize heat conduction. Finite element simulations and experimental validation show a heat flux density estimation error below 17%, a 12.3% improvement in defect detection over manual inspection, and a 27.7% gain over existing automated methods, with infrared images demonstrating enhanced thermal uniformity, reduced noise, and higher defect contrast.