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Machine Vision and Applications
Published in: Machine Vision and Applications 1/2021

01-02-2021 | Original Paper

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

Authors: Jamal Saeedi, Matteo Dotta, Andrea Galli, Adriano Nasciuti, Umang Maradia, Marco Boccadoro, Luca Maria Gambardella, Alessandro Giusti

Published in: Machine Vision and Applications | Issue 1/2021

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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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Metadata
Title
Measurement and inspection of electrical discharge machined steel surfaces using deep neural networks
Authors
Jamal Saeedi
Matteo Dotta
Andrea Galli
Adriano Nasciuti
Umang Maradia
Marco Boccadoro
Luca Maria Gambardella
Alessandro Giusti
Publication date
01-02-2021
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 1/2021
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-020-01142-w

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