Automated evaluation of continuous and segmented chip geometries based on image processing methods and a convolutional neural network

• Autorzy: Klippel Hagen , Pflaum Samuel , Kuffa Michal , Wegener Konrad

Identyfikatory

DOI

10.36897/jme/156091

• Języki publikacji: EN

Abstrakty

EN

The aim of this work is to present a new methodology for the automated analysis of the cross-sections of experimental chip shapes. It enables, based on image processing methods, the determination of average chip thicknesses, chip curling radii and for segmented chips the extraction of chip segmentation lengths, as well as minimum and maximum chip thicknesses. To automatically decide whether a chip at hand should be evaluated using the proposed methods for continuous or segmented chips, a convolutional neural network is proposed, which is trained using supervised learning with available images from embedded chip cross-sections. Data from manual measurements are used for comparison and validation purposes.

Słowa kluczowe

EN

machining; AI; computer vision; image processing; chip; chip shape; chip segmentation

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2022
• Tom: Vol. 22, No. 4
• Strony: 115--132
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Klippel Hagen

• IWF, ETH Zürich, Switzerland

• https://orcid.org/0000-0002-3348-1168

autor

Pflaum Samuel

• DTDS, Bühler AG, Switzerland

autor

Kuffa Michal

• IWF, ETH Zürich, Switzerland

• https://orcid.org/0000-0002-5326-1269

autor

Wegener Konrad

• IWF, ETH Zürich, Switzerland

• https://orcid.org/0000-0002-5921-1422

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).