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A Comparative Study on Ultrasonic Propagation Characteristics and Defect Detection of Metal Material Additive Manufacturing Using Deep Learning Algorithm

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Advances in Engineering Design (FLAME 2022)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

The method of measuring the thickness of the specimen using bulk waves is an effective technique for measuring the thickness of the structure in the ultrasonic non-destructive technique. The principle of measuring the bulk wave thickness can be used to determine the thickness of the structure using the correlation between the reciprocating time when the ultrasonic waves pass through the internal material of the specimen and return from the bottom. As interest in 3D printers has increased recently, this paper studied how to process ultrasound A-scan signals reflected from the bottom when measuring the thickness of a structure using the above method. For the experiment, the step wedge manufactured in units of 5 mm from 5 to 25 mm with a 1018 steel material was repeatedly tested using volumetric waves, a result value was derived, and data was generated by imaging it, and then the neural network was trained using MATLAB’s Pre-trained Deep Neural Networks. Afterward, the A-scan image of step wedge produced by 3D printer was added to the learned network and used as test data, indicating that the classification according to thickness was very good. Finally, the results were confirmed by training to classify the image results of the defective step wedge produced by the 3D printer. This study is believed to be effective as a basic study in comparing specimens produced with 3D printers through Deep Learning and observing defects.

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Acknowledgements

This work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20214000000480, Development of R&D engineers for combined cycle power plant technologies).

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Authors and Affiliations

  1. School of Mechanical Engineering, Changwon National University, Changwon, 51140, Korea

    Hyeonsu Song & Jaesun Lee

  2. Extreme Environment Design and Manufacturing Innovation Center, Changwon National University, Changwon, 51140, Korea

    Junpil Park

  3. School of Engineering and Applied Science, National Rail and Transportation Institute, Vadodara, Gujarat, 390004, India

    Sunil Kumar Sharma

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  1. Hyeonsu Song
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  3. Sunil Kumar Sharma
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  4. Jaesun Lee
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Correspondence to Jaesun Lee .

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Editors and Affiliations

  1. Department of Mechanical Engineering, Amity University, Noida, Uttar Pradesh, India

    Rohit Sharma

  2. Department of Mechanical Engineering, Amity University, Noida, Uttar Pradesh, India

    Ravindra Kannojiya

  3. Acoustics and Vibration Standards, CSIR-National Physical Laboratory, New Delhi, Delhi, India

    Naveen Garg

  4. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Sachin S. Gautam

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Song, H., Park, J., Sharma, S.K., Lee, J. (2023). A Comparative Study on Ultrasonic Propagation Characteristics and Defect Detection of Metal Material Additive Manufacturing Using Deep Learning Algorithm. In: Sharma, R., Kannojiya, R., Garg, N., Gautam, S.S. (eds) Advances in Engineering Design. FLAME 2022. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-3033-3_15

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