Automatized End Mill Wear Inspection Using a Novel Illumination Unit and Convolutional Neural Network

• Published in: IEEE access Vol. 12; pp. 124282 - 124297
• Main Authors: Bilal, Muhenad, Podishetti, Ranadheer, Koval, Leonid, Gaafar, Mahmoud A., Grossmann, Daniel, Bregulla, Markus
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; convolutional neural network; Convolutional neural networks; Cutting tools; Cutting wear; end mills; helical geometries; Illumination; illumination source; Image quality; Inspection; Lighting; Machining; machining performance; material wear; Milling; Mills; Neural networks; Optical imaging; Optical sensors; Performance evaluation; Reflection; reflective surfaces; Titanium carbonitride; Titanium nitride; Tool wear; wear analysis; wear segmentation; Workpieces
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3454692

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.