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Artificial Neural Network Based Sub-surface Defect Detection in Glass Fiber Reinforced Polymers: Nondestructive Evaluation 4.0

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Abstract

With the rapid development of Industry 4.0 and the expansion of its application fields, it has been successfully applied in various industrial applications like aerospace, defense, material manufacturing, etc. For quality control, nondestructive testing and evaluation (NDT&E) will become nondestructive testing and evaluation (NDE) 4.0 to seamlessly connect with Industry 4.0. NDE 4.0 focuses on deploying artificial intelligence in the quality inspection of different industrial products, including composites, steel slabs, polycrystalline solar wafers, etc. This paper proposes an artificial neural network (ANN) based sub-surface defect detection modality for exploring subsurface defects using Gabor filter features with improved resolution and enhanced detectability. Considering the desirable characteristics of spatial locality and orientation selectivities of the Gabor filter, we design filters for extracting sample features from the local image. The effectiveness of the proposed method is demonstrated by the experimental results on glass fiber reinforced polymer (GFRP) composite sample using digitized frequency modulated thermal wave imaging. We experimentally evaluate the proposed model on a benchmark and achieve a fast detection result with high accuracy, surpassing the state-of-the-art methods. For quantification, signal to noise ratio (SNR) is considered as a figure of merit.

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Acknowledgements

Authors acknowledges for the support provided through his constructive suggestions and continuous encouragement by Mr. Mulaveesala Venkata Jagannadharao, Chukkavanipalem, Dharmavaram, Vizaianagaram, Andhra Pradesh, India.

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No funding available for this work presented in this manuscript.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Bhadson Rd, Adarsh Nagar, Prem Nagar, Patiala, Punjab, 147004, India

    Geetika Dua

  2. InfraRed Vision & Automation Pvt. Ltd, Ward NO 8, Sun City, Rupnagar, Punjab, 140001, India

    Vanita Arora

  3. InfraRed Imaging Laborotory (IRIL), Centre for Sensors, iNstrumentation and Cyber-Physical Systems Engineering (SeNSE), Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India

    Ravibabu Mulaveesala

Authors
  1. Geetika Dua
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  2. Vanita Arora
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  3. Ravibabu Mulaveesala
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Contributions

GD contributed writing of the manuscript and part of the post-processing mainly for pulse compression analysis. VA contributed by conducting experimentation and post-processing, RM contributed for the idea of the depth resolvability and the suggestions in processing and analysis.

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Correspondence to Ravibabu Mulaveesala.

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Dua, G., Arora, V. & Mulaveesala, R. Artificial Neural Network Based Sub-surface Defect Detection in Glass Fiber Reinforced Polymers: Nondestructive Evaluation 4.0. Sens Imaging 24, 38 (2023). https://doi.org/10.1007/s11220-023-00445-2

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