TY - CHAP A1 - Koval, Leonid A1 - Pfaller, Daniel A1 - Bilal, Mühenad A1 - Bregulla, Markus A1 - Cupek, Rafal T1 - An analysis of convolutional neural network models for classifying machine tools T2 - Advances in Computational Collective Intelligence UR - https://doi.org/10.1007/978-3-030-88113-9_37 KW - image classification KW - domain relevance KW - F1-score KW - confusion-matrix KW - machine tools Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-88113-9_37 SN - 978-3-030-88112-2 SN - 978-3-030-88113-9 SN - 1865-0929 SN - 1865-0937 SP - 461 EP - 473 PB - Springer CY - Cham ER - TY - CHAP A1 - Bilal, Mühenad A1 - Mayer, Christian A1 - Kancharana, Sunil A1 - Bregulla, Markus A1 - Cupek, Rafal A1 - Ziębiński, Adam ED - Bădică, Costin ED - Treur, Jan ED - Benslimane, Djamal ED - Hnatkowska, Bogumiła ED - Krótkiewicz, Marek T1 - Damage Detection of Coated Milling Tools Using Images Captured by Cylindrical Shaped Enclosure Measurement Setup T2 - Advances in Computational Collective Intelligence. 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28–30, 2022, Proceedings UR - https://doi.org/10.1007/978-3-031-16210-7_21 KW - quality inspection KW - damage detection KW - image processing KW - tool regrinding KW - measurement setup Y1 - 2022 UR - https://doi.org/10.1007/978-3-031-16210-7_21 SN - 978-3-031-16209-1 SN - 978-3-031-16210-7 SP - 264 EP - 272 PB - Springer CY - Cham ER - TY - CHAP A1 - Bilal, Mühenad A1 - Kancharana, Sunil A1 - Mayer, Christian A1 - Bregulla, Markus A1 - Ziębiński, Adam A1 - Cupek, Rafal ED - Osten, Wolfgang ED - Nikolaev, Dmitry ED - Zhou, Jianhong T1 - Image-based damage detection on TiN-coated milling tools by using a multi-light scattering illumination technique T2 - Fourteenth International Conference on Machine Vision (ICMV 2021) UR - https://doi.org/10.1117/12.2623140 Y1 - 2022 UR - https://doi.org/10.1117/12.2623140 SN - 978-1-5106-5045-9 SN - 978-1-5106-5044-2 PB - SPIE CY - Bellingham ER - 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 - CHAP A1 - Bilal, Mühenad A1 - Kancharana, Sunil A1 - Mayer, Christian A1 - Pfaller, Daniel A1 - Koval, Leonid A1 - Bregulla, Markus A1 - Cupek, Rafal A1 - Ziębiński, Adam ED - Farinella, Giovanni Maria ED - Radeva, Petia ED - Bouatouch, Kadi T1 - High Resolution Mask R-CNN-based Damage Detection on Titanium Nitride Coated Milling Tools for Condition Monitoring by using a New Illumination Technique T2 - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications N2 - 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. UR - https://doi.org/10.5220/0010781800003124 KW - Predictive Maintenance KW - Machine Learning KW - Damage Detection KW - Illumination Source KW - Mask R-CNN Y1 - 2022 UR - https://doi.org/10.5220/0010781800003124 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27498 SN - 978-989-758-555-5 SN - 2184-4321 VL - vol. 5: VISAPP SP - 305 EP - 314 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Tangirala, Sri Girish A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks JF - Sensors N2 - 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. UR - https://doi.org/10.3390/s25051575 Y1 - 2025 UR - https://doi.org/10.3390/s25051575 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58058 SN - 1424-8220 VL - 25 IS - 5 PB - MDPI CY - Basel ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - Benchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System JF - IEEE Access N2 - 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. UR - https://doi.org/10.1109/ACCESS.2025.3574785 Y1 - 2025 UR - https://doi.org/10.1109/ACCESS.2025.3574785 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59669 SN - 2169-3536 VL - 13 SP - 96400 EP - 96422 PB - IEEE CY - New York 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 - TY - THES A1 - Bilal, Mühenad T1 - Entwicklung eines reflexionsmindernden Bildgebungsverfahrens mit homogener Ausleuchtung und konvolutionalen neuronalen Netzen zur Verschleißerkennung und Klassifizierung komplexer, spiegelnder Zerspanungswerkzeuge N2 - Im Zuge der Digitalisierung stehen Unternehmen, insbesondere Klein- und Mittelständische Unternehmen (KMUs), bei der Etablierung von Künstliche Intelligenz (KI) in industriellen Fertigungsprozessen zunehmend vor großen Herausforderungen. Im letzten Jahrhundert hat die klassische Automatisierung in der Fertigung maßgeblich zur Produktivitätssteigerung und damit auch zur Wettbewerbsfähigkeit von Unternehmen beigetragen. Die Entwicklung und Etablierung von KIgestützter Automatisierung in Unternehmen stellen die nächste Stufe der Produktivitätssteigerung dar und werden die bisherige regelbasierte, klassische Automatisierung zunehmend ablösen. Der Bedarf nach KI-gestützter Automatisierung in der Zerspanungsindustrie, insbesondere in den Bereichen der Werkzeugfertigung und -instandsetzung, gewinnt zunehmend an Bedeutung. Das liegt an den steigenden Kundenanforderungen und am demografischen Wandel, aber auch am Fachkräftemangel, an kostenintensiven Messverfahren sowie an zeit- und ressourcenaufwendigen Schulungen, die zur Bedienung komplexer Messverfahren relevant sind. Das stellt eine Gefahr für die Wettbewerbsfähigkeit der KMUs dar. Klassische Messverfahren wie die Fokusvariation oder triangulationsbasierte Messtechniken liefern zwar eine hohe Genauigkeit, sind jedoch mit erheblichem Zeit- und Kostenaufwand verbunden. Daher eignen sie sich nur bedingt für den Serieneinsatz in der Qualitätskontrolle, insbesondere bei der Vermessung von Schneidwerkzeugen im Rahmen stichprobenbasierter Prüfungen. Um diese Herausforderungen entgegenzuwirken, wurde im Rahmen dieser Dissertation ein vollständiges KI-gestütztes System zur automatisierten Werkzeuginspektion und -Klassifizierung entwickelt und fundierte Convolutional Neural Networks (CNNs)-basierte Forschung betrieben. Eine der zentralen Kerninnovationen dieser Arbeit liegt in der Entwicklung einer werkzeugspezifischen Lichtquelle, die gezielt auf die optischen Eigenschaften der zylindrischen Schneidwerkzeuge wie Schaftfräser abgestimmt ist und neue Möglichkeiten in der bildbasierten Qualitätskontrolle von glänzenden Oberflächen eröffnet. Die dabei erzeugten hochauflösenden Aufnahmen zeichnen sich durch eine hohe Tiefenschärfe, Farbtreue und gleichmäßige Ausleuchtung aus und ermöglichen eine zuverlässige Verschleißanalyse. Um die Funktionalität dieses neuen Bildgebungsverfahrens zu demonstrieren, wurde zunächst mit regelbasierten Verfahren wie adaptiver Schwellenwertbildung ein Verschleißdetektionsalgorithmus entwickelt und in Gegenüberstellung mit standardisierten Bildaufnahmen aus der Fertigung erforscht. Die ersten vielversprechenden Ergebnisse führten zur Anwendung bestehender Architekturen wie Mask Region-Based Convolutional Neural Network (Mask R-CNN). Diese wurden für die Lokalisierung und Detektion von Verschleiß anhand weniger Bilder genutzt. Außerdem wurde ein CNN-basiertes Verfahren für die semantische Segmentierung entwickelt, das in der Lage ist, normalen und abnormalen Verschleiß an hochreflektierenden Schaftfräsern mit komplexen Freiformflächen zu detektieren und mit hoher Genauigkeit trennscharf voneinander zu unterscheiden. Bei der semantischen Segmentierung wurden unterschiedliche Ansätze für die Modelloptimierung verfolgt, darunter Variationen der Ankerboxen und des Intersection over Union (IoU)-Schwellenwerts bei Mask R-CNN, um die bestmögliche Modellperformance zu erhalten. Weitere CNN-basierte Ansätze folgten. Hierbei wurden bewährte Architekturen wie U-Net Architecture (UNet) für die Verschleißdetektion modifiziert, aber auch eine kompakte, dimensionsreduzierte Form entwickelt, um normalen von abnormalem Verschleiß mit hoher Genauigkeit zu detektieren und Aussagen über das Verschleißverhalten zu treffen. Dabei wurden unterschiedliche Regularisierungstechniken und Hyperparameteroptimierung angewandt, um die beste Modellleistung an zwei unterschiedlichen hochreflektierenden Werkzeugtypen zu erreichen. Es wurden im Rahmen dieser Arbeit auch die State of the Art (SOTA) CNNs-Architekturen für die Klassifizierung von Werkzeugen erforscht. Hierbei wurde die Auswirkung unterschiedlicher Beleuchtungsbedingungen, der Bildqualität sowie der unterschiedlichen Trainingsstrategien untersucht, um die am besten geeignete Architektur und Trainingsstrategie für eine KI-basierte Werkzeugklassifizierung zu identifizieren. N2 - In the context of digitalization, companies, especially Small and Medium-Sized Enterprises (SMEs), are increasingly facing major challenges in establishing Artificial Intelligence (AI) in industrial manufacturing processes. In the last century, classic automation in manufacturing contributed significantly to productivity increases and thus also to the competitiveness of companies. Developing and establishing AI-supported automation in companies represents the next stage of increasing productivity and will increasingly replace the existing rule-based, classic automation. Developing and setting up AI-based automation in companies is the next step in boosting productivity and will gradually replace the traditional rule-based automation we’ve seen so far. There’s a growing need for AI-based automation in the machining industry, especially in tool manufacturing and restoration. This is because of increasing customer demands and demographic change, but also because of a shortage of skilled workers, costly measurement methods, and time-consuming and resource-intensive training that’s needed to use complex measurement methods. The classic measurement methods such as focus variation or triangulation-based measurement techniques provide high accuracy, but they are associated with considerable time and cost. Therefore, they are only suitable for limited use in quality control, especially when measuring cutting tools in sample-based tests. The AI-based measurement method is a promising alternative to traditional measurement methods. To address these challenges, a complete AI-based system for automated tool inspection and classification was developed as part of this dissertation, and in-depth research based on CNNs was carried out. One of the central core innovations of this work lies in the development of a tool-specific light source that is specifically tuned to the optical properties of cylindrical cutting tools such as end mills, opening up new possibilities in image-based quality control of shiny surfaces. High-resolution images generated in this way are characterized by high depth of field, color fidelity, and uniform illumination, enabling reliable wear analysis. To demonstrate the functionality of this new imaging method, a wear detection algorithm was first developed using rule-based methods such as adaptive thresholding and researched in comparison with standardized images from production. The first promising results led to the implementation of existing architectures such as Mask R-CNN. These were used to localize and detect wear based on a small number of images. In addition, a CNN-based method for semantic segmentation was developed that is capable of detecting normal and abnormal wear on highly reflective end mills with complex free-form surfaces and distinguishing between them with high accuracy. In semantic segmentation, different approaches were pursued for model optimization, including variations of anchor boxes and the IoU threshold in Mask R-CNN, in order to achieve the best possible model performance. Further CNN-based approaches followed. Here, proven architectures such as UNet were modified for wear detection, but a compact, reduced-dimension form was also developed to detect normal and abnormal wear with high accuracy and to make statements about wear behavior. Different regularization techniques and hyperparameter optimization were applied to achieve the best model performance on two different highly reflective tool types. As part of this work, SOTA CNNs architectures for tool classification were also investigated. The effect of different lighting conditions, image quality, and different training strategies was studied to identify the most suitable architecture and training strategy for AI-based tool classification. In semantic segmentation, different approaches were pursued for model optimization, including variations of anchor boxes and the intersection-over-union (IoU) threshold in Mask-R-CNN, in order to achieve the best possible model performance. Further CNN-based approaches followed. Here, proven architectures such as U-Net4 were modified for wear detection, but a compact, reduceddimension form was also developed to detect normal and abnormal wear with high accuracy and to make statements about wear behavior. Different regularization techniques and hyperparameter optimization were applied to achieve the best model performance on two different highly reflective tool types. As part of this work, state-of-the-art (SOTA) convolutional neural network (CNN) architectures for tool classification were also investigated. The effect of different lighting conditions, image quality, and different training strategies was studied to identify the most suitable architecture and training strategy for AI-based tool classification. Y1 - 2025 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64997 PB - Technische Hochschule Ingolstadt CY - Ingolstadt ER -