Fuzzy C-based Automatic Defect Detection using Barker Coded Thermal Wave Imaging

doi:10.23940/ijpe.21.05.p8.484490

Abstract

Abstract: Non-Destructive Testing & Evaluation (NDT&E) is used to test the object for detection of voids/cracks that are generated due to various circumstances at the time of production and making of various materials. Among several NDT&E techniques, Non-stationary thermal wave imaging (NSTWI) is an attractive and significant method for testing those materials without changing their serviceability. NSTWI is a whole filed, non-contact and non-invasive testing modality. The present research work is focused on detecting subsurface irregularities/defects that arose during the manufacturing phase by employing a clustering based post-processing approach using Barker Coded Thermal Wave Imaging (BCTWI). The Fuzzy c-means (FCM) & K-means clustering algorithms were intended to classify and detect the defects in the test samples. Fuzzy c-means clustering enhances a better detectability compared to that of the K-means clustering by eliminating the initial counter problems that arise during detection. The obtained experimental results using Fuzzy c-means & K-means clustering algorithms are validated using Signal to Noise Ratio (SNR) as a performance metric. Fuzzy c-means provide an enhanced detection when compared with that of k-means. The obtained SNR values prove that the FCM provides the very nearer value of the defects compared to that of K-means. Thus, fuzzy c-means with Barker coded Thermal Wave Imaging can provide effective detection of the anomaly.

Key words: clustering algorithm, infrared thermography, K-means, fuzzy c-means, signal to noise ratio (SNR), non-stationary thermal wave imaging (NSTWI), barker coded thermal wave imaging (BCTWI)

M. Muzammil Parvez, J. Shanmugam, and V.S. Ghali. Fuzzy C-based Automatic Defect Detection using Barker Coded Thermal Wave Imaging [J]. Int J Performability Eng, 2021, 17(5): 484-490.

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