Modified Differential Absolute Contrast using Thermal Quadrupoles for the Nondestructive Testing of Finite Thickness Specimens by Infrared Thermography

Abstract

Infrared thermography is a nondestructive evaluation technique in which the specimen surface is thermally stimulated to produce a temperature difference between "sound" (free of defects) areas and eventual defective regions. It is well known that the thermographic methods based on the thermal contrast are strongly affected by non-uniform heating at the surface. Hence, thermal contrast-based results considerably depend on the chosen reference point. The differential absolute contrast (DAC) method was developed to eliminate the need of determining a reference point by defining the thermal contrast with respect to an "ideal" sound area. The DAC technique is based on the 1D solution of the Fourier diffusion equation for homogeneous and semi-infinite materials stimulated with a Dirac heat pulse. Although very useful at early times, this assumption considerably decreases DAC accuracy when the heat front approaches the sample rear face. We propose a modified DAC version by explicitly introducing the sample thickness using the thermal quadrupoles theory. We demonstrate that taking into account the sample thickness, the DAC validity range considerably extends for long times after excitation while preserving its performance for short times.

IRT for NDT: 2050 Horizon

“…There are many data processing algorithms and the selection of the method depends on the objectives of the research. Some of the most useful techniques are [54][55][56][57][58][59][60][61][62][63][64][65][66][67]: Thermal signal reconstruction (TSR), Differential absolute contrast (DAC), Pulsed phase thermography (PPT), Principal component thermography (PCT), Partial least square thermography (PLST) and Supervised principal component analysis (SPCA). Thermal infrared NDT data processing techniques have some advantages and limitations such as defect detection enhancement on one hand, but sometimes exhibit slow computing or require interactions with an operator to select algorithm parameters on the other hand (ex: selection of a non-defect area which could affect final results).…”

Defect detection in CFRP structures using pulsed thermographic data enhanced by penalized least squares methods

“…Hence, different types of thermographic image analysis methods have been proposed for signal enhancement, e.g. thermographic signal reconstruction (TSR) [19,20], differential absolute contrast (DAC) [21,22], pulsed phase thermography (PPT) [23,24], principal component thermography (PCT) [25], etc., where TSR is frequently used for its performance in data compressing and noise reduction. Based on the Fourier diffusion equation, TSR applies polynomial filters to eliminate the noise contained in thermographic data.…”

Content Based Thermal Images Retrieval

“…All these contrast definitions require the use of the temperature in a sound area whose definition is a critical issue. In a wide sense, its locations are not precisely identified since it may not be known in advance where the defects are, if present at all [7]. The modified Differential Absolute Contrast (m-DAC) was developed to perform a more convenient computation of the sound area temperature by using the thermal quadrupoles theory [8], [9].…”