High strength and low density make epoxy-based CFRP a highly interesting construction material for the aerospace manufacturing industry. Porosity represents an unavoidable defect and significantly weakens strength values dominated by the matrix. To evaluate the quality of safety-relevant components, non-destructive evaluation and thus the characterization of porous structures is indispensable. Pulsed thermography represents a fast, large-area and non-contact testing method that enables efficient estimation of material parameters. In this work, the authors demonstrate the quantitative estimation of porosity by pulsed thermography on a multi-axial laminate fabricated from unidirectional Prepregs for the first time. The characteristic, extensive expansion of the pores in fiber direction, is addressed by the 3D microstructure characterization of Cone beam X-ray computed tomography data. Hence, the application of effective medium theories and thus the model based porosity estimation is enabled. After the investigation of the effect of pore expansion on the effective thermal diffusivity in 3D finite element simulations, the quantitative photothermal porosity estimation on a sample with a global volume porosity of $$\Phi =1.51\%$$Φ = 1.51 % is demonstrated. The accuracy of this fast and non-contact method for porosity estimation with pulsed thermography ( $$\Delta \Phi =0.63\%$$Δ Φ = 0.63 % ) is comparable to the standard ultrasonic method. Consequently, an efficient estimation of porosity for large, complex shaped UD/Epoxy composite components is enabled.

This paper presents a method to determine thermal parameters, which can neither be calculated analytically nor measured directly. Specifically, the convective heat transfer coefficient is discussed, which is usually determined using empirical models, namely the Nusselt correlations. To overcome the lack of information about the relation between temperature and parameters, the methodology of data assimilation is applied. Therefore, a dynamic model is combined with measurement data from a thermography experiment avoiding interference with the actual process enabling inline parameter identification. The method is first applied on artificially created simulation data and second on real measurement data. This paper shows that the developed method estimates the heat transfer coefficient in agreement with the well-known Nusselt correlations. Moreover, the present work compares different estimation strategies and gives a recommendation regarding state-parameter, pure parameter or combined estimation, including a detailed analysis of the variation of a smoothing parameter.