Abstract
This study is focused on the quantitative estimation of defect depth by applying pulsed thermal nondestructive testing. The majority of known defect characterization techniques are based on 1D heat conduction solutions, thus being inappropriate for evaluating defects with low aspect ratios. A novel method for estimating defect depth is proposed by taking into account the phenomenon of 3D heat diffusion, finite lateral size of defects and the thermal reflection coefficient at the boundary between a host material and defects. The method is based on the combination of a known analytical model and a non-linear fitting (NLF) procedure. The algorithm was verified both numerically and experimentally on 3D-printed polylactic acid plastic samples. The accuracy of depth prediction using the proposed method was compared with the reference characterization technique based on thermographic signal reconstruction to demonstrate the efficiency of the proposed NLF method.
Highlights
Past decades demonstrate a growing popularity of infrared (IR) thermography as an established nondestructive testing (NDT) technique, as used in the aerospace industry [1,2,3], power production [4,5], civil engineering [6,7,8] and manufacturing of composite and hybrid materials [9,10,11], thanks to its high inspection productivity, illustrative character of data presentation and sensitivity to defects of various origins
Known limitations of this method are related to the detection of deep- and small-size defects, and difficulties of defect characterization that is conditioned by heat diffusion in bulk materials
Unlike the above-mentioned approaches, the proposed methodology considers the finite size of defects and introduces an effective thermal reflection coefficient, which is less than unity, taking into account thermal properties of both a tested material and defects
Summary
Past decades demonstrate a growing popularity of infrared (IR) thermography as an established nondestructive testing (NDT) technique, as used in the aerospace industry [1,2,3], power production [4,5], civil engineering [6,7,8] and manufacturing of composite and hybrid materials [9,10,11], thanks to its high inspection productivity, illustrative character of data presentation and sensitivity to defects of various origins. All above-mentioned methods are based on a 1D heat conduction model, being suitable for evaluating laterally-extended defects characterized by a high size/depth aspect ratio, i.e., the so-called 1D defects. The model suggested in [22] shows that R values affect both the amplitude and time of peak contrasts, as well as the shape of temperature evolution curves and, correspondingly, the values of other proposed SCTs. Since the SCT methods are not fully appropriate for characterizing deeper defects with low size/depth aspect ratio, a non-linear (least-square) fitting (NLF) method was developed [23,24]. Unlike the above-mentioned approaches, the proposed methodology considers the finite size of defects and introduces an effective thermal reflection coefficient, which is less than unity, taking into account thermal properties of both a tested material and defects. Defects of different geometrical shape, such as disks and spheres (air bubbles), were analyzed in comparison to commonly used flat bottom holes (FBHs)
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