Active Thermography Data-Processing Algorithm for Nondestructive Testing of Materials

Abstract

This article presents the development of an active thermography algorithm capable of detecting defects in materials, based on the techniques of Thermographic Signal Reconstruction (TSR), Thermal Contrast (TC) and the physical principles of heat transfer. The results obtained from this algorithm are compared to the TSR technique and the raw thermogram obtained by stepped thermography inspection. Experimentally, a short thermal pulse is used and the surface temperature of the sample is monitored over time with an infrared camera. Due to the volume of data, the first step is data compression. Newton's law of cooling was used to store the normalized temperature data pixel-by-pixel over time and a compression ratio of 99% was obtained. The main contributions of the developed algorithm are: only four parameters for data compression and the concept of change in the direction of the heat flow to delimit the edges of the defects, where the borders are identified with a remarkable accuracy. Some well known image processing technique are also integrated to improve the thermal analysis: edge detection/interface between the sample and the image background; consolidation in a single image by aggregating the indicators referring to the concept of cooling/heating time constant, maximum thermal amplitude and contrast. INDEX TERMS active thermography, thermal analysis, infrared imaging, data compression, change detection algorithms, image processing.

Compression of Pulsed Infrared Thermography Data With Unsupervised Learning for Nondestructive Evaluation of Additively Manufactured Metals

“…A data-processing algorithm for stepped thermography was developed to detect subsurface defects of carbon fiber reinforced polymer (CFRP) [33]. This algorithm outperformed TSR in data compression by using fewer fit parameters.…”

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

“…The drawback of these methods was that fidelity of the impulse response reconstruction was affected by numerical noise. In addition, experimental measurement of background was required, which increased the complexity of method implementation A data-processing algorithm for stepped thermography was developed to detect subsurface defects of carbon fiber reinforced polymer (CFRP) using fitting parameters based on Newton's law of cooling [26]. Fitting transient temperature data was accomplished using Gauss-Newton algorithm, and storing the estimated polynomial coefficients for each temperature signal.…”

Report on PTT Imaging of Defects in AM Metallic Materials-Part 2

“…However, this method requires analyzing a large number of thermography frames, which is time-consuming. Recently, a dataprocessing method has been introduced and uses fewer polynomial fitting coefficients than TSR by employing the Gauss-Newton algorithm [9]. However, this reduced detection accuracy.…”