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

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 obtai...

Full description

Saved in:
Bibliographic Details
Published in:Machine vision and applications 2021-02, Vol.32 (1), Article 21
Main Authors: Saeedi, Jamal, Dotta, Matteo, Galli, Andrea, Nasciuti, Adriano, Maradia, Umang, Boccadoro, Marco, Gambardella, Luca Maria, Giusti, Alessandro
Format: Article
Language:English
Subjects:
Artificial neural networks
Communications Engineering
Computer Science
Electric discharges
Feature extraction
Image Processing and Computer Vision
Inspection
Machining
Networks
Neural networks
Original Paper
Pattern Recognition
Surface roughness
Vision systems
Workpieces
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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%.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-020-01142-w