Two-dimensional cross-correlation for defect detection in composite materials inspected by lock-in thermography

Non-destructive testing is essential for the thorough assessment of production processes of complex materials, such as composites. This paper presents a complete algorithm to detect subsurface defects, e.g. extended delaminations or local resin pockets, by comparing the outputs produced by lock-in thermography for the inspection of master pristine samples and the current ones under testing. The use of lock-in thermography produces amplitude and phase maps. Focusing on amplitudes, dataset are first made comparable in both magnitude spans and spatial positions exploiting image normalization and alignment. Then local patches in actual correspondence are cross-correlated to further improve their alignment and estimate a similarity measurement. Differences in thermal behaviors detected by the proposed processing underlie subsurface defects. These outcomes have been also proven by experimental investigations performed on a carbon fiber reinforced polymer (CFRP) T-joint.