ABSTRACT

The main goal of this paper is the estimation of thermal resistive fields in multilayer samples using the classical front face flash method as excitation and InfRared Thermography (IRT) as a monitoring sensor. The complete inverse processing of a multilayer analytical model can be difficult due to a lack of sensitivity to certain parameters (layer thickness, depth of thermal resistance, etc.) or processing time. For these reasons, our present strategy proposes a Bayesian inference approach. Using the analytical quadrupole method, a reference model can be calculated for a set of parameters. Then, the Bayesian probabilistic method is used to determine the maximum likelihood probability between the measured data and the reference model. To keep the processing method robust and fast, an automatic selection of the calculation range is proposed. Finally, in the case of a bilayer sample, both the thickness and resistive 3D layers are estimated in less than 2 min for a space and time matrix of 50,000 pixels by 4000 time steps with a reasonable relative error of less than 5%.

摘要

本文的主要目标是使用经典的正面闪光方法作为激发和红外热成像 (IRT) 作为监测传感器来估计多层样品中的热阻场。由于对某些参数（层厚度、热阻深度等）或处理时间缺乏敏感性，多层分析模型的完整逆处理可能很困难。由于这些原因，我们目前的策略提出了贝叶斯推理方法。使用分析四极杆方法，可以计算一组参数的参考模型。然后，使用贝叶斯概率方法确定测量数据与参考模型之间的最大似然概率。为了保持处理方法的鲁棒性和快速性，提出了计算范围的自动选择。最后，