@inproceedings{KovalPfallerBilaletal.2021, author = {Koval, Leonid and Pfaller, Daniel and Bilal, M{\"u}henad and Bregulla, Markus and Cupek, Rafal}, title = {An analysis of convolutional neural network models for classifying machine tools}, booktitle = {Advances in Computational Collective Intelligence}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-88112-2}, issn = {1865-0929}, doi = {https://doi.org/10.1007/978-3-030-88113-9_37}, pages = {461 -- 473}, year = {2021}, language = {en} } @inproceedings{BilalMayerKancharanaetal.2022, author = {Bilal, M{\"u}henad and Mayer, Christian and Kancharana, Sunil and Bregulla, Markus and Cupek, Rafal and Ziębiński, Adam}, title = {Damage Detection of Coated Milling Tools Using Images Captured by Cylindrical Shaped Enclosure Measurement Setup}, booktitle = {Advances in Computational Collective Intelligence. 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28-30, 2022, Proceedings}, editor = {Bădică, Costin and Treur, Jan and Benslimane, Djamal and Hnatkowska, Bogumiła and Kr{\´o}tkiewicz, Marek}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-16209-1}, doi = {https://doi.org/10.1007/978-3-031-16210-7_21}, pages = {264 -- 272}, year = {2022}, language = {en} } @inproceedings{BilalKancharanaMayeretal.2022, author = {Bilal, M{\"u}henad and Kancharana, Sunil and Mayer, Christian and Bregulla, Markus and Ziębiński, Adam and Cupek, Rafal}, title = {Image-based damage detection on TiN-coated milling tools by using a multi-light scattering illumination technique}, booktitle = {Fourteenth International Conference on Machine Vision (ICMV 2021)}, editor = {Osten, Wolfgang and Nikolaev, Dmitry and Zhou, Jianhong}, publisher = {SPIE}, address = {Bellingham}, isbn = {978-1-5106-5045-9}, doi = {https://doi.org/10.1117/12.2623140}, year = {2022}, language = {en} } @article{BilalPodishettiKovaletal.2024, author = {Bilal, M{\"u}henad and Podishetti, Ranadheer and Koval, Leonid and Gaafar, Mahmoud A. and Großmann, Daniel and Bregulla, Markus}, title = {The Effect of Annotation Quality on Wear Semantic Segmentation by CNN}, volume = {24}, pages = {4777}, journal = {Sensors}, number = {15}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s24154777}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{BilalKancharanaMayeretal.2022, author = {Bilal, M{\"u}henad and Kancharana, Sunil and Mayer, Christian and Pfaller, Daniel and Koval, Leonid and Bregulla, Markus and Cupek, Rafal and Ziębiński, Adam}, title = {High Resolution Mask R-CNN-based Damage Detection on Titanium Nitride Coated Milling Tools for Condition Monitoring by using a New Illumination Technique}, volume = {vol. 5: VISAPP}, booktitle = {Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}, editor = {Farinella, Giovanni Maria and Radeva, Petia and Bouatouch, Kadi}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-555-5}, issn = {2184-4321}, doi = {https://doi.org/10.5220/0010781800003124}, pages = {305 -- 314}, year = {2022}, abstract = {The implementation of intelligent software in the manufacturing industry is a technology of growing importance and has highlighted the need for improvement in automatization, production, inspection, and quality assurance. An automated inspection system based on deep learning methods can help to enhance inspection and provide a consistent overview of the production line. Camera-based imaging systems are among the most widely used tools, replacing manual industrial quality control tasks. Moreover, an automatized damage detection system on milling tools can be employed in quality control during the coating process and to simplify measuring tool life. Deep Convolutional Neural Networks (DCNNs) are state-of-the-art methods used to extract visual features and classify objects. Hence, there is great interest in applying DCNN in damage detection and classification. However, training a DCNN model on Titanium-Nitride coated (TiN) milling tools is extremely challenging. Due to the coating, the optical properties such as reflection and light scattering on the milling tool surface make image capturing for computer vision tasks quite challenging. In addition to the reflection and scattering, the helical-shaped surface of the cutting tools creates shadows, preventing the neural network from efficient training and damage detection. Here, in the context of applying an automatized deep learning-based method to detect damages on coated milling tools for quality control, the light has been shed on a novel illumination technique that allows capturing high-quality images which makes efficient damage detection for condition monitoring and quality control reliable. The method is outlined along with results obtained in training a ResNet 50 and ResNet 101 model reaching an overall accuracy of 83\% from a dataset containing bounding box annotated damages. For instance and semantic segmentation, the state-of-the-art framework Mask R-CNN is employed.}, language = {en} } @article{BilalPodishettiTangiralaetal.2025, author = {Bilal, M{\"u}henad and Podishetti, Ranadheer and Tangirala, Sri Girish and Großmann, Daniel and Bregulla, Markus}, title = {CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks}, volume = {25}, pages = {1575}, journal = {Sensors}, number = {5}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s25051575}, year = {2025}, abstract = {Sustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the repair and regrinding of these tools have become more essential. The geometric differences among machining tools determine their specific applications: twist drills have spiral flutes and pointed cutting edges designed for drilling, while end mills feature multiple sharp edges around the shank, making them suitable for milling. Taps and form cutters exhibit unique geometries and cutting-edge shapes, enabling the creation of complex profiles. However, measuring and classifying these tools for repair or regrinding is challenging due to their optical properties and coatings. This research investigates how lighting conditions affect the classification of tools for regrinding, addressing the shortage of skilled workers and the increasing need for automation. This paper compares different training strategies on two unique tool-specific datasets, each containing 36 distinct tools recorded under two lighting conditions—direct diffuse ring lighting and normal daylight. Furthermore, Grad-CAM heatmap analysis provides new insights into relevant classification features.}, language = {en} } @article{BilalPodishettiGrossmannetal.2025, author = {Bilal, M{\"u}henad and Podishetti, Ranadheer and Großmann, Daniel and Bregulla, Markus}, title = {Benchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System}, volume = {13}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2025.3574785}, pages = {96400 -- 96422}, year = {2025}, abstract = {In this study, we introduce the ToolSurface-144 dataset, which is presented here for the first time. It comprises four subsets - Full R, Full S, Top R, and Top S - each containing 144 tool classes captured under varying illumination conditions and fields of view. A newly developed, patented imaging approach was employed to acquire the data. It is compared with conventional diffuse ring illumination to assess its effectiveness in evaluating state-of-the-art convolutional neural networks. This enabled a more targeted investigation of the role of global shape characteristics such as silhouettes versus localized features like the tool face, cutting edges, and delicate geometrical structures under different training strategies. In this study, we evaluate six state-of-the-art convolutional neural networks—AlexNet, DenseNet161, EfficientNet-B0, ResNet152, ResNet50, and VGG16—using three training strategies: fine-tuning, freezing of pre-trained layers, and training from scratch. The results show that EfficientNet-B0 consistently achieved the highest classification accuracy in nearly all experiments and data sets. Especially using the fine-tuning training strategy, the model achieved 99\% accuracy in tool classification. ResNet50 benefited greatly from fine-tuning and freezing, achieving a significant increase in performance compared to training from scratch. In contrast, ResNet152, AlexNet, and VGG16 consistently showed poor classification performance, indicating difficulties regarding learning and generalisation. The results show that diffuse illumination and complete tool views provide the best classification conditions, while restricted image sections with homogeneous illumination negatively affect model performance. Among the evaluated training strategies, fine-tuning proved the most efficient training method for developing CNN models for tool classification.}, language = {en} } @article{BilalPodishettiKovaletal.2024, author = {Bilal, M{\"u}henad and Podishetti, Ranadheer and Koval, Leonid and Gaafar, Mahmoud A. and Großmann, Daniel and Bregulla, Markus}, title = {Automatized End Mill Wear Inspection Using a Novel Illumination Unit and Convolutional Neural Network}, volume = {12}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2024.3454692}, pages = {124282 -- 124297}, year = {2024}, abstract = {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.}, language = {en} }