Soft-sensing reconstruction of in-depth defect geometry from active IR-thermography data

This paper proposes a new approach for processing measured data from active Infra Red (IR) thermography, where a soft sensing algorithm is exploited for in depth defect reconstruction. This is achieved by propagating the information gathered at the wall surface to the inner layers. Correlating the experimental 2D measurements to a Finite Element (FE) model of the tested specimen it is possible to update the model with the measured data and change the geometry of the simulated inner defect, until the surface temperature distribution calculated corresponds to the measured one. Following that strategy, the unknown defect geometry can be determined. The method developed and presented in this paper consists of an optimization problem based on the minimization of the difference between the surface temperature distribution measured on the sample subjected to an active thermography test and the one resulting from the FE model. The optimization variables are the geometrical parameters (depth, width, thickness and position) characterizing the defect which will be fully determined at the complete convergence, within a given tolerance, of the optimization problem. The method includes also a preprocessing algorithm, based on the same experimental data and FE model, which allows to determine thermal and mechanical properties of the object under test, like surface emissivity, heat capacity and material conductivity and density, which are often unknown especially in the case of works of art. This soft-sensing procedure has been applied to a virtual experiment to estimate the accuracy of the reconstructed geometry and to a simulacrum of ancient fresco including defects realized on purpose.