Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network
The aim of this work is to present a new methodology for the automated analysis of the cross-sections of experimental chip shapes from orthogonal cutting experiments. It enables, based on image processing methods, the determination of average chip thicknesses, chip curling radii and for segmented ch...
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| Format: | Article |
| Language: | English |
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Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT
2022-11-01
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| Series: | Journal of Machine Engineering |
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| Online Access: | http://jmacheng.not.pl/Automated-Evaluation-of-Continuous-and-Segmented-Chip-Geometries-Based-on-Image-Processing,156091,0,2.html |
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| _version_ | 1850039253062385664 |
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| author | Hagen Klippel Samuel Pflaum Michal Kuffa Konrad Wegener |
| author_facet | Hagen Klippel Samuel Pflaum Michal Kuffa Konrad Wegener |
| author_sort | Hagen Klippel |
| collection | DOAJ |
| description | The aim of this work is to present a new methodology for the automated analysis of the cross-sections of experimental chip shapes from orthogonal cutting experiments. 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. |
| format | Article |
| id | doaj-art-7c4ae499786643a8a31f0cf7011f85c4 |
| institution | DOAJ |
| issn | 1895-7595 2391-8071 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT |
| record_format | Article |
| series | Journal of Machine Engineering |
| spelling | doaj-art-7c4ae499786643a8a31f0cf7011f85c42025-08-20T02:56:24ZengPublishing House of Wrocław Board of Scientific Technical Societies Federation NOTJournal of Machine Engineering1895-75952391-80712022-11-0122411513210.36897/jme/156091156091Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural NetworkHagen Klippel0https://orcid.org/0000-0002-3348-1168Samuel Pflaum1Michal Kuffa2https://orcid.org/0000-0002-5326-1269Konrad Wegener3https://orcid.org/0000-0002-5921-1422IWF, ETH Zürich, SwitzerlandDTDS, Bühler AG, SwitzerlandIWF, ETH Zürich, SwitzerlandIWF, ETH Zürich, SwitzerlandThe aim of this work is to present a new methodology for the automated analysis of the cross-sections of experimental chip shapes from orthogonal cutting experiments. 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.http://jmacheng.not.pl/Automated-Evaluation-of-Continuous-and-Segmented-Chip-Geometries-Based-on-Image-Processing,156091,0,2.htmlimage processingmachiningchip shapeartificial neural networks |
| spellingShingle | Hagen Klippel Samuel Pflaum Michal Kuffa Konrad Wegener Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network Journal of Machine Engineering image processing machining chip shape artificial neural networks |
| title | Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network |
| title_full | Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network |
| title_fullStr | Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network |
| title_full_unstemmed | Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network |
| title_short | Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network |
| title_sort | automated evaluation of continuous and segmented chip geometries based on image processing methods and a convolutional neural network |
| topic | image processing machining chip shape artificial neural networks |
| url | http://jmacheng.not.pl/Automated-Evaluation-of-Continuous-and-Segmented-Chip-Geometries-Based-on-Image-Processing,156091,0,2.html |
| work_keys_str_mv | AT hagenklippel automatedevaluationofcontinuousandsegmentedchipgeometriesbasedonimageprocessingmethodsandaconvolutionalneuralnetwork AT samuelpflaum automatedevaluationofcontinuousandsegmentedchipgeometriesbasedonimageprocessingmethodsandaconvolutionalneuralnetwork AT michalkuffa automatedevaluationofcontinuousandsegmentedchipgeometriesbasedonimageprocessingmethodsandaconvolutionalneuralnetwork AT konradwegener automatedevaluationofcontinuousandsegmentedchipgeometriesbasedonimageprocessingmethodsandaconvolutionalneuralnetwork |