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

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 d...