Multiple Input CNN Architecture for Tool State Recognition in the Milling Process Based on Time Series Signals
The study presents a tailored application of a multiple-input convolutional neural network (CNN) for tool state recognition in the milling process. Our approach uniquely applies an 11-input CNN to classify tool wear in chipboard milling, utilizing scalogram images derived from time-series signals. T...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wrocław University of Science and Technology
2024-01-01
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| Series: | Operations Research and Decisions |
| Online Access: | https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no3_3.pdf |
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| _version_ | 1849428294260228096 |
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| author | Michał Bukowski Izabella Antoniuk Karol Szymanowski Artur Krupa Jarosław Kurek |
| author_facet | Michał Bukowski Izabella Antoniuk Karol Szymanowski Artur Krupa Jarosław Kurek |
| author_sort | Michał Bukowski |
| collection | DOAJ |
| description | The study presents a tailored application of a multiple-input convolutional neural network (CNN) for tool state recognition in the milling process. Our approach uniquely applies an 11-input CNN to classify tool wear in chipboard milling, utilizing scalogram images derived from time-series signals. The primary objective was to categorize tool wear into three classes: green, yellow, and red, signifying the progression of wear. The study involved 75 samples (25 samples per class), each comprising 11 signals transformed into scalograms via continuous wavelet transform. The dataset of 825 scalogram images enabled the development of a CNN-based diagnostic model, achieving a notable accuracy of 96.00%, which is an improvement over a previous methods (93.33%). (original abstract) |
| format | Article |
| id | doaj-art-7117448766c844e09cfe30ec0fae946e |
| institution | Kabale University |
| issn | 2081-8858 2391-6060 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wrocław University of Science and Technology |
| record_format | Article |
| series | Operations Research and Decisions |
| spelling | doaj-art-7117448766c844e09cfe30ec0fae946e2025-08-20T03:28:44ZengWrocław University of Science and TechnologyOperations Research and Decisions2081-88582391-60602024-01-01vol. 34no. 34160171700322Multiple Input CNN Architecture for Tool State Recognition in the Milling Process Based on Time Series SignalsMichał Bukowski0Izabella Antoniuk1Karol Szymanowski2Artur Krupa3Jarosław Kurek4Warsaw University of Life Sciences, Warsaw, PolandWarsaw University of Life Sciences, Warsaw, PolandWarsaw University of Life Sciences, Warsaw, PolandWarsaw University of Life Sciences, Warsaw, PolandWarsaw University of Life Sciences, Warsaw, PolandThe study presents a tailored application of a multiple-input convolutional neural network (CNN) for tool state recognition in the milling process. Our approach uniquely applies an 11-input CNN to classify tool wear in chipboard milling, utilizing scalogram images derived from time-series signals. The primary objective was to categorize tool wear into three classes: green, yellow, and red, signifying the progression of wear. The study involved 75 samples (25 samples per class), each comprising 11 signals transformed into scalograms via continuous wavelet transform. The dataset of 825 scalogram images enabled the development of a CNN-based diagnostic model, achieving a notable accuracy of 96.00%, which is an improvement over a previous methods (93.33%). (original abstract)https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no3_3.pdf |
| spellingShingle | Michał Bukowski Izabella Antoniuk Karol Szymanowski Artur Krupa Jarosław Kurek Multiple Input CNN Architecture for Tool State Recognition in the Milling Process Based on Time Series Signals Operations Research and Decisions |
| title | Multiple Input CNN Architecture for Tool State Recognition in the Milling Process Based on Time Series Signals |
| title_full | Multiple Input CNN Architecture for Tool State Recognition in the Milling Process Based on Time Series Signals |
| title_fullStr | Multiple Input CNN Architecture for Tool State Recognition in the Milling Process Based on Time Series Signals |
| title_full_unstemmed | Multiple Input CNN Architecture for Tool State Recognition in the Milling Process Based on Time Series Signals |
| title_short | Multiple Input CNN Architecture for Tool State Recognition in the Milling Process Based on Time Series Signals |
| title_sort | multiple input cnn architecture for tool state recognition in the milling process based on time series signals |
| url | https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no3_3.pdf |
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