Experimental validation of the diffusion function model for accuracy-enhanced thermoreflectometry. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Javaudin, Benjamin; Gilblas, Rémi; Sentenac, Thierry; Le Maoult, Yannick
• Is Part Of: Quantitative infrared thermography. Volume 18:Issue 1(2021)
• Page Start: 18
• Page End: 33
• Publication Date: 2021-01-01
• Subjects: BRDF -- modelling -- thermoreflectometry -- emissivity -- active thermography; Infrared technology -- Periodicals; Thermography -- Periodicals; Infrared technology; Thermography; Periodicals; 621.362
• DOI: 10.1080/17686733.2019.1665297
• Languages: English
• ISSNs: 1768-6733

Abstract

ABSTRACT: In the field of thermography, the main problem in measuring true temperature lies in determining the emissivity. Thermoreflectometry overcomes this problem by an in-situ emissivity determination method based on bidirectional reflectivity measurement. This method introduces a diffusion function model which links the bidirectional reflectivity measured to the emissivity. The true temperature and the model parameters are then found with the solution of a system of radiometric equations. The first contribution of this article is the modelling of the diffusion function using physical models of the Bidirectional Reflectivity Distribution Function (BRDF). The suggested model depends on two parameters: the roughness and the geometrical setting of the measurement. For a given configuration, the identification of the roughness parameter provides a knowledge of the evolution of the surface state according to the operating conditions of the material (deformation, damage, etc.). The second contribution is the validation of the roughness parameter identified by the thermoreflectometry method. Validation is done on a reference sample in platinum with a measured roughness. Another validation is performed on the BRDF model by measurements on a Fourier transform spectrometer. It validates the shape of the model and gives a second validation of the roughness parameter by using curve-fitting identification.