TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Koval, Leonid A1 - Gaafar, Mahmoud A. A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - The Effect of Annotation Quality on Wear Semantic Segmentation by CNN JF - Sensors N2 - In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and customized CNN architecture, we found that domain expertise significantly affects model performance. Annotator 1 achieved maximum mIoU scores of 0.8153 for abnormal wear and 0.7120 for normal wear on TiN datasets, whereas Annotator 3 with the lowest expertise achieved significantly lower scores. Sensitivity to annotation inconsistencies and model hyperparameters were examined, revealing that models for TiCN datasets showed a higher coefficient of variation (CV) of 16.32% compared to 8.6% for TiN due to the subtle wear characteristics, highlighting the need for optimized annotation policies and high-quality images to improve wear segmentation. UR - https://doi.org/10.3390/s24154777 Y1 - 2024 UR - https://doi.org/10.3390/s24154777 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49551 SN - 1424-8220 VL - 24 IS - 15 PB - MDPI CY - Basel ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Koval, Leonid A1 - Gaafar, Mahmoud A. A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - Automatized End Mill Wear Inspection Using a Novel Illumination Unit and Convolutional Neural Network JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2024.3454692 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3454692 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58160 SN - 2169-3536 VL - 12 SP - 124282 EP - 124297 PB - IEEE CY - New York ER -