Tool wear segmentation in blanking processes with fully convolutional networks based digital image processing-Reference-Cited by-同舟云学术

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Tool wear segmentation in blanking processes with fully convolutional networks based digital image processing

Published:2024-03 Issue: Volume:324 Page:118270
ISSN:0924-0136
Container-title:Journal of Materials Processing Technology
language:en
Short-container-title:Journal of Materials Processing Technology

Author:

Schlegel Clemens,Molitor Dirk Alexander^ORCID,Kubik Christian,Martin Daniel Michael^ORCID,Groche Peter

Funder

Bundesministerium fur Bildung und Forschung Dienststelle Berlin

Bundesministerium für Bildung und Forschung

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Metals and Alloys,Computer Science Applications,Modeling and Simulation,Ceramics and Composites

Reference27 articles.

1. A novel approach of combined edge detection and segmentation for tool wear measurement in machining;Bagga;Prod. Eng.,2021

2. Digital image processing with deep learning for automated cutting tool wear detection;Bergs;Procedia Manuf.,2020

3. 3D U-net: Learning dense volumetric segmentation from sparse annotation;Çiçek,2016

4. Application of digital image processing in tool condition monitoring: A review;Dutta;CIRP J. Manuf. Sci. Technol.,2013

5. Machine learning applications of convolutional neural networks and unet architecture to predict and classify demosponge behavior;Harrison;Water,2021

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