Recognition of drilling-induced defects in Fiber Reinforced Polymers using Machine Learning
(2023) 19th CIRP Conference on Modeling of Machining Operations, CMMO 2023 In Procedia CIRP 117. p.384-389- Abstract
The machining of Fiber Reinforced Polymers (FRP) is accompanied by specific defects such as delamination, uncut fibers, and others. Such defects are unique in their shape and size for different FRP types, used tools, and applied cutting conditions. Therefore, defect recognition and quantification remain a central challenge in the quality control of FRP components from an accuracy and time balance perspective. The study presents the implementation of Machine Learning techniques for automated recognition of the hole defects resulting from drilling Flax/PLA biocomposites using HSS drills with various cutting data. The paper discusses the effectiveness and stability of the developed solution.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2a053ed3-f73d-4276-95a3-da1d3a31dc14
- author
- Hrechuk, Andrii LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- biocomposites, defects, drilling, Machine Learning, metrics, U-Net
- host publication
- 19th CIRP Conference on Modeling of Machining Operations
- series title
- Procedia CIRP
- editor
- Schulze, Volker and Biermann, Dirk
- volume
- 117
- pages
- 6 pages
- publisher
- Elsevier
- conference name
- 19th CIRP Conference on Modeling of Machining Operations, CMMO 2023
- conference location
- Karlsruhe, Germany
- conference dates
- 2023-05-31 - 2023-06-02
- external identifiers
-
- scopus:85163078675
- ISSN
- 2212-8271
- DOI
- 10.1016/j.procir.2023.03.065
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2023 Elsevier B.V.. All rights reserved.
- id
- 2a053ed3-f73d-4276-95a3-da1d3a31dc14
- date added to LUP
- 2023-07-31 10:25:49
- date last changed
- 2024-03-08 04:09:54
@inproceedings{2a053ed3-f73d-4276-95a3-da1d3a31dc14, abstract = {{<p>The machining of Fiber Reinforced Polymers (FRP) is accompanied by specific defects such as delamination, uncut fibers, and others. Such defects are unique in their shape and size for different FRP types, used tools, and applied cutting conditions. Therefore, defect recognition and quantification remain a central challenge in the quality control of FRP components from an accuracy and time balance perspective. The study presents the implementation of Machine Learning techniques for automated recognition of the hole defects resulting from drilling Flax/PLA biocomposites using HSS drills with various cutting data. The paper discusses the effectiveness and stability of the developed solution.</p>}}, author = {{Hrechuk, Andrii}}, booktitle = {{19th CIRP Conference on Modeling of Machining Operations}}, editor = {{Schulze, Volker and Biermann, Dirk}}, issn = {{2212-8271}}, keywords = {{biocomposites; defects; drilling; Machine Learning; metrics; U-Net}}, language = {{eng}}, pages = {{384--389}}, publisher = {{Elsevier}}, series = {{Procedia CIRP}}, title = {{Recognition of drilling-induced defects in Fiber Reinforced Polymers using Machine Learning}}, url = {{http://dx.doi.org/10.1016/j.procir.2023.03.065}}, doi = {{10.1016/j.procir.2023.03.065}}, volume = {{117}}, year = {{2023}}, }