Estimation of Thermal Resistance Field in Layered Materials by Analytical Asymptotic Method

Abstract

In this paper, the problem of the quantitative characterization of thermal resistance fields in a multilayer sample is addressed by using the classical front face flash method as the thermal excitation and infrared thermography (IRT) as the monitoring sensor. In this challenging problem, the complete inverse processing of a multilayer analytical model is difficult due to the lack of sensitivity of some parameters (layer thickness, depth of thermal resistance, etc.) and the expansive computational iterative processing. For these reasons, the proposed strategy is to use a simple multilayer problem where only one resistive layer is estimated. Moreover, to simplify the inverse processing often based on iterative methods, an asymptotic development method is proposed here. Regarding the thermal signal reconstruction (TSR) methods, the drawback of these methods is the inability to be quantitative. To overcome this problem, the method incorporates a calibration process originating from the complete analytical quadrupole solution to the thermal problem. This analytical knowledge allows self-calibration of the asymptotic method. From this calibration, the quantitative thermal resistance field of a sample can be retrieved with a reasonable accuracy lower than 5%.

Thermal resistance field estimations from IR thermography using multiscale Bayesian inference

“…To extend the applicability of these approaches, we propose the implementation of Bayesian-type methods. comparing this study with a prior study [1]based on asymptotic method, we can highlight that in this sample case, the Bayesian approach allows for the estimation of more parameters. In fact, the knowledge of delamination and more particularly of Rth is of crucial interest for material health as well as the possibility of calculating a heat balance on materials subjected to high flows such as composite materials but more recently materials from additive manufacturing processes for example.…”

“…This work is a continuity study by our team on the development of quantitative methods for thermal resistance field characterization. Thus, as presented in [1], in the field of Quantitative InfraRed Thermography (QIRT), Non Destructive Testing (NDT) methods are very widespread for the detection of defaults, whereas few techniques are completely quantitative. In fact, many authors have developed techniques for qualitative in-depth defect detection using flash thermography, the Thermal Signal Reconstruction (TSR) technique [2,3] or the lock-in technique [4].…”

“…Thus, our main objective is to develop rapid and quantitative methods. [1] was based on an asymptotic development with a quantitative and fast computational approach that does not allow for the estimation of several resistive layers. To extend the applicability of these approaches, we propose the implementation of Bayesian-type methods.…”

“…In this work, a flash setup is used, and then, the methods, including several components, are developed to introduce the direct modeling of multilayer samples using a quadrupole formulation. Then, in the inverse processing section, the Bayesian inference is presented, and finally, in the results and discussion section, the method is validated on the same sample used in [1] to compare both approaches.…”

Infrared Imaging and NDT

“…In [3], an application of active thermography is presented. In this case, it is used to estimate the thermal resistance field quantitatively.…”