Sheared edge defect segmentation using a convolutional U-Net for quantified quality assessment of fine blanked workpieces-Reference-Cited by-同舟云学术

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Sheared edge defect segmentation using a convolutional U-Net for quantified quality assessment of fine blanked workpieces

Published:2022-05 Issue: Volume:75 Page:129-141
ISSN:0141-6359
Container-title:Precision Engineering
language:en
Short-container-title:Precision Engineering

Author:

Wolfschläger Dominik^ORCID,Woltersmann Jan-Henrik^ORCID,Montavon Benjamin^ORCID,Schmitt Robert H.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Elsevier BV

Subject

General Engineering

Reference51 articles.

1. Handbuch Umformen, edition Handbuch der Fertigungstechnik;Hoffmann,2012

2. Blechumformung: Verfahren, Werkzeuge und Maschinen, VDI-Buch,2015

3. A characterization of quality of sheared edge in fine blanking using edge-computing approach;Trauth;Procedia Manuf,2018

4. Industry 4.0 implications in machine vision metrology: an overview;Alonso;Procedia Manuf,2019

5. A machine vision algorithm for quality control inspection of gears;Moru;Int J Adv Manuf Technol,2020

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1. Nondestructive testing method of Si3N4 ceramic bearing cylindrical roller surface defects based on depthwise separable convolution coupled with identity block ASPP;Journal of Materials Science: Materials in Electronics;2024-06

2. A technology maturity assessment framework for Industry 5.0 machine vision systems based on systematic literature review in automotive manufacturing;International Journal of Production Research;2023-10-17

3. An industrial defect detection algorithm based on CPU-GPU parallel call;Multimedia Tools and Applications;2023-05-01

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