Frequency-modulated thermography and clustering analysis for defect detection in acrylic glass
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abstract
The frequency-modulated thermal wave imaging (FMTWI) technique has shown its potential as a substitute for traditional lock-in thermography in non-destructive testing (NDT) as it can scan the entire material thickness in a single cycle and a relatively short amount of time. However, using FMTWI for the inspection of defects still requires tremendous human effort. In this work, multi-dimensional cluster analysis was proposed to post-process phase images within a frequency band altogether, without prior knowledge of the defect location and depth. Three clustering methods, k-means, fuzzy c-means and Gaussian mixture model (GMM), were applied to segment the defect area from a series of phase images and reconstruct the defect dimension and their performances were compared. It was found that for the shallow buried defects with 6 mm diameter, all three methods could estimate the dimension with less than 10% error, whereas for other detectable defects, GMM could maintain less than 25% error while the other two algorithms reached an error as large as 244.78%. The results demonstrated that the proposed method could automatically segment the defect area from a series of phase images and GMM would be more suitable for processing FMTWI experimental data than the other two methods investigated.Grant: This material was partly supported by the National Science Foundation's Research Experiences for Undergraduates (REU) Programme (Award No 1263293) and Texas A&M University-CONACYT: Collaborative Research Grant Program Grant No 246073. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Natural Science Foundation, Texas A&M University or CONACYT Mexico.
