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Weld defect segmentation and feature extraction from the acquired phased array scan images

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Abstract

In the recent past, Non-Destructive Testing (NDT) methods play a significant role in welding defect detection. Phased array ultrasonic testing is the advanced NDT testing method that is used for accessing the weld integrity. But qualified personnel are required for measuring the geometrical features of the defects in welds. A full-fledged computer-based measuring system is very much required IN ORDER To avoid Manual interpretation, since, it always leads to some errors in inferring the Phased Array (PA) signals and images. This work proposes an artificial intelligence- based measuring approach to enhance the features of the image. The 2D Adaptive Anisotropic Diffusion Filter (2D AADF) is used to remove noise and also scattered pixels are corrected by applying a hexagonal sampled grid. The adaptive mean adjustment (AMA) algorithm would be used for enhancing features of the image in terms of contrast and brightness. The hybrid clustering segmentation incorporates non overlapping K means improved Fuzzy C Means (FCM) pixels clustering. The automatic seed point is selected by K means clustering, and high-intensity gradient is estimated by applying Gradient Cluster Fuzzy C Means (GCFCM) segmentation method. After segmenting the Region of Interest (ROI), the features are estimated due to Gray Level Co-Occurrence Matrix (GLCM). The computed segmented image features are further given to deep learning to classify different stages of welding defect. The investigational results are proven that the proposed algorithm would be high professional and accurate. The above technique is also implemented on the images acquired from the Omniscan MX2 instrument with a suitable linear array probe with 64 transducer elements in it. The proposed work aims to propose an automatic measurement technique for identifying the defect and characterizing it.

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Authors and Affiliations

  1. Faculty of Electronics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

    J.C. Jayasudha & S. Lalithakumari

  2. Department of EIE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

    J.C. Jayasudha & S. Lalithakumari

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  1. J.C. Jayasudha
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  2. S. Lalithakumari
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Contributions

In this research paper the C-scan images where acquired from the MX2 Olympus equipment for validation purpose. The obtained images are analyzed and the results are evaluated to show the effectiveness of the research carried out.

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Correspondence to J.C. Jayasudha.

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Jayasudha, J., Lalithakumari, S. Weld defect segmentation and feature extraction from the acquired phased array scan images. Multimed Tools Appl 81, 31061–31074 (2022). https://doi.org/10.1007/s11042-022-13030-8

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  • DOI: https://doi.org/10.1007/s11042-022-13030-8

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