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Surface Defects Detection on Pressure Die Castings by Machine Learning Exploiting Machine Vision Features

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Advances in Design, Simulation and Manufacturing V (DSMIE 2022)

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

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Abstract

Detection of surface defects in high-pressure aluminum die castings is of paramount importance for maintaining product quality. Visual inspection by humans is time-consuming and subject to errors and oversights. A machine vision system has been set up to capture part surface images in this work. Afterimage quality enhancement using standard transformations and filtering Regions of Interest were defined in the areas where defects are expected to appear. Noise elimination extended edge extraction followed. Corresponding descriptors were employed to identify statistical features associated with defective parts. An advanced learning process has been developed to classify parts as defective or normal, based on Feedforward Artificial Neural Networks (ANNs), which were compared to typically used Support Vector Machines. Different combinations of descriptors were tried as input to determine the best four ANNS, which were used as an ensemble to enhance robustness at overall positive recognition rates of the order of 90% despite the restricted dataset.

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Acknowledgment

Vioral SA is gratefully acknowledged for providing castings and consultation.

Author information

Authors and Affiliations

  1. National Technical University of Athens, 9, Iroon Polytechniou St., 15780, Athens, Greece

    Zoe Papagianni & George-Christopher Vosniakos

Authors
  1. Zoe Papagianni
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  2. George-Christopher Vosniakos
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Corresponding author

Correspondence to George-Christopher Vosniakos .

Editor information

Editors and Affiliations

  1. Sumy State University, Sumy, Ukraine

    Vitalii Ivanov

  2. Poznan University of Technology, Poznan, Poland

    Justyna Trojanowska

  3. Sumy State University, Sumy, Ukraine

    Ivan Pavlenko

  4. Free University of Bozen-Bolzano, Bolzano, Bolzano, Italy

    Erwin Rauch

  5. University of Zagreb, Zagreb, Croatia

    Dragan Peraković

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Papagianni, Z., Vosniakos, GC. (2022). Surface Defects Detection on Pressure Die Castings by Machine Learning Exploiting Machine Vision Features. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Rauch, E., Peraković, D. (eds) Advances in Design, Simulation and Manufacturing V. DSMIE 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-06025-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-06025-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06024-3

  • Online ISBN: 978-3-031-06025-0

  • eBook Packages: EngineeringEngineering (R0)

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