Automated damage detection and characterization of polymer composite images using Tsallis-particle swarm optimization-based multilevel threshold and multifractals

Automatic detection and quantification of damage in the composite structure are a vital requirement in the assessment of the overall structural integrity of modern aerospace systems. In this work, the indentation-induced damage in the glass fiber-reinforced polymer composite (GFRP) laminates is investigated using multilevel threshold-based particle swarm optimization (PSO) and multifractals. Initially, the digital images are acquired after the composite laminates are subjected to 5 mm, 6 mm, and 7 mm indentation displacements. The indentation-induced damage images are filtered using anisotropic diffusion filter and damage regions are segmented using Tsallis-PSO method. The magnitude of the damage is analyzed using multifractal spectrum features. Results show that Tsallis-PSO with the four-level thresholds is optimal for the segmentation of the damage. Tsallis-PSO is also able to segment the boundaries of the damage precisely. The damage due to different levels of indentation depth is well differentiated by the multifractal spectrum features. Multifractal spectrum constructed using the scale, ranges from q = -1 to +1, discriminates the damage with a high statistical significance of P < .001. The damage dimension linearly progresses (R = .993) with increasing the level of indentation depth. Even though, the proposed process pipeline is an initial step of investigation of damages in GFRP, this framework can be used in the industries for the quantification of damage in GFRP composite laminates.