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Measurement and inspection of electrical discharge machined steel surfaces using deep neural networks

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Abstract

We propose an industrial measurement and inspection system for steel workpieces eroded by electrical discharge machining, which uses deep neural networks for surface roughness estimation and defect detection. Specifically, a convolutional neural network (CNN) is used as a regressor in order to obtain steel surface roughness and a CNN based on spatial pooling pyramid is applied for defect classification. In addition, a new method for the region of interest selection based on morphological reconstruction and mean shift filtering is proposed for defect detection and localization. The regressor and classifier based on deep neural networks proposed here outperform state-of-the-art methods using handcrafted feature extraction. We achieve a mean absolute percentage error of 7.32% on roughness estimation; on defect detection, our approach yields an accuracy of 97.26% and an area under the ROC curve metric of 99.09%.

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Acknowledgements

This work has been supported by the Commission for Technology and Innovation (Project 27359.1 PFIW-IW) and the Canton of Ticino (Switzerland) through the SUPSI EDM Competence Centre. The authors would like to thank the anonymous reviewers for their helpful and constructive comments that significantly contributed to improving this paper.

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Authors and Affiliations

  1. Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), Lugano, Switzerland

    Jamal Saeedi, Luca Maria Gambardella & Alessandro Giusti

  2. Institute for Mechanical Engineering and Material Technology (MEMTi SUPSI), Lugano, Switzerland

    Matteo Dotta, Andrea Galli & Adriano Nasciuti

  3. Agie Charmilles SA, Losone, Switzerland

    Umang Maradia & Marco Boccadoro

Authors
  1. Jamal Saeedi
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  2. Matteo Dotta
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  3. Andrea Galli
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  4. Adriano Nasciuti
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  5. Umang Maradia
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  6. Marco Boccadoro
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  7. Luca Maria Gambardella
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  8. Alessandro Giusti
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Correspondence to Jamal Saeedi or Alessandro Giusti.

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Saeedi, J., Dotta, M., Galli, A. et al. Measurement and inspection of electrical discharge machined steel surfaces using deep neural networks. Machine Vision and Applications 32, 21 (2021). https://doi.org/10.1007/s00138-020-01142-w

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