Identifying the Geometry of an Object Using Lock-In Thermography

TL;DR

This paper presents two novel techniques to estimate the 3D geometry of objects using Lock-In Thermography by relating LIT parameters to thickness, enabling non-destructive subsurface analysis.

Contribution

It introduces two methods for estimating object thickness from LIT data, including a numerical model inversion and a database retrieval approach with PCA enhancement.

Findings

Both techniques accurately estimate thickness with low RMS deviation.

The database method benefits from PCA for improved accuracy.

Stochastic Gradient Descent optimizes data collection timing.

Abstract

Lock-in Thermography (LIT) is a type of Infrared Thermography (IRT) that can be used as a useful non-destructive testing (NDT) technique for the detection of subsurface anomalies in objects. Currently, LIT fails to estimate the thickness at a point on the tested object. This makes LIT unable to figure out the 3-dimensional geometry of an object. In this project, two techniques of identifying the geometry of an object using LIT are discussed. The main idea of both techniques is to find a relationship between the parameters obtained from LIT and the thickness at each data point. Technique I builds a numerical function that models the relationship between thickness, Lock-In phase, and other parameters. The function is then inverted for thickness estimation. Technique II is a quantitative method, in which a database is created with six dimensions - thickness, Lock-In phase, Lock-In amplitude and three other parameters, based on data obtained from LIT experiments or simulations. Estimated thickness is obtained by retrieving data from the database. The database can be improved based on Principal Component Analysis. Evaluation of the techniques is done by measuring root-mean-square deviation, and calculating successful rate with different tolerances. Moreover, during the application of the techniques, Stochastic Gradient Descent can be used to determine the time when sufficient data have been collected from LIT measurement to generate the estimated geometry accurately.