Automatized end mill wear inspection using a novel illumination unit and convolutional neural network

Ensuring cutting tools are in optimal condition is essential for achieving peak machining performance, given their direct impact on both workpiece quality and process efficiency. However, accurately assessing wear on end mills, especially those with complex geometries, pose a significant challenge due to their reflective surfaces and varied wear patterns. Presented here is a novel method that addresses this challenge by employing a customized illumination unit in conjunction with a convolutional neural network (CNN) for end mill wear analysis. This innovative approach involves utilizing the specially designed illumination unit to capture high-quality images, enabling precise examination of material wear on helically shaped end mills. Notably, this method is tailored to illuminate reflective surfaces and represents a pioneering application in the realm of wear testing.We validate the viability of this approach by employing CNN-based models to segment wear on complex-shaped end mills coated with titanium carbonitride (TiCN) and titanium nitride (TiN). We achieved remarkable mean Intersection over Union (mIoU) results in wear detection on a test dataset: 0.99 for tool segmentation, 0.78 for abnormal wear, and 0.71 for normal wear segmentation.

Subjects

convolutional neural network

Cutting tools

end mills

helical geometries

illumination source

machining performance

material wear

reflective surfaces

wear analysis

wear segmentation

DDC Class

621: Applied Physics

004: Computer Sciences

Lizenz

https://creativecommons.org/licenses/by-nc-nd/4.0/

Name

Automatized_End_Mill_Wear_Inspection_Using_a_Novel_Illumination_Unit_and_Convolutional_Neural_Network.pdf

Type

Main Article

Size

4.87 MB

Format

Adobe PDF

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Automatized end mill wear inspection using a novel illumination unit and convolutional neural network