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- W4383825777 abstract "Abstract Tool wear and the decision when to replace tools is a universal challenge in the metal cutting industry. While the tool wear state can be accurately determined using optical measuring methods, the tool wear of milling tools is often examined by the CNC-machine operators, especially in small and medium enterprises. In order to increase the accuracy with which tool wear can be correctly classified, it is advisable to use an assistance system that automatically removes the tools from a buffer, examines the tool wear state based on visual sensor data and sorts them into separate boxes according to the classification result. In this context, the accurate classification of tool wear is a key capability that can be enabled using methods of machine learning, based on image data that was labeled by human experts. In this paper different machine learning models are examined based on their ability to classify images of milling tools into the categories worn and not worn. The EfficientNet_b0 model achieves an accuracy of 91.47% and outperforms human experts that classified similar images by 22.87%." @default.
- W4383825777 created "2023-07-11" @default.
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- W4383825777 date "2023-01-01" @default.
- W4383825777 modified "2023-09-27" @default.
- W4383825777 title "Machine Learning as an Enabler for Automated Assistance Systems for the Classification of Tool Wear on Milling Tools" @default.
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- W4383825777 doi "https://doi.org/10.1007/978-3-031-10071-0_3" @default.
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