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Open Die Innovation: Manufacturing Metallic Weldable Composite Profiles for the Future of Hybrid Lightweight Design
No full textUsing hybrid lightweight structures based on fiber-reinforced plastics (FRP) is a promising approach to meeting the growing performance demands placed on structural components. However, this field faces significant challenges, primarily regarding the cost-effective production of composite materials and the development of efficient joining technologies. This presentation introduces an innovative hybrid design concept combining pultruded FRP profiles and metallic components. A key feature of the Fraunhofer IWU solution is the integration of a protruding metal tongue designed to enable conventional welding techniques, such as arc welding. This approach allows hybrid components to be seamlessly incorporated into existing manufacturing processes using established joining methods. A modular die system was developed to enable the production of hybrid profiles and allows for the construction of two distinct die variants. One die variant equipped with integrated pressure sensors was used for preliminary investigations. These tests analyzed cavity pressure during pultrusion as it relates to key process parameters, such as fiber volume content and process speed. The second patented die concept features an open-die configuration in which the two die halves are fastened together on only one side, leaving a lateral opening on the other side. This opening is sealed during processing by a continuous metal tongue that is directly bonded to the FRP. A custom sealing solution was developed for high-pressure processing of low-viscosity resins, such as epoxy. This results in a self-sealing die system with adjustable clamping forces. This ensures the formation of a bare metal profile that protrudes from the FRP base structure. Thus, the need for time-consuming post-processing steps, such as exposing the metal surface prior to joining, is eliminated. The functionality of the open die system was validated across different material combinations and process parameters. Mechanical testing, including tensile tests, was performed to evaluate the bond strength of the hybrid components. Additionally, welding trials were performed to evaluate the weldability of the exposed metal tongues. Then, tensile testing was conducted on welded specimens to determine residual joint strength
Low-effort Iterative Dataset Generation Pipeline for Unknown Object Instance Segmentation
No full text1861218619Robots operating in everyday environments encounter a wide variety of previously unseen objects. Deep Learning methods simplify unknown object and scene segmentation by structuring inherent real-world complexities, improving visual scene understanding. However, they need vast amounts of labeled high-variance data for training. Acquiring these labels for rich real-world data requires significant manual effort, especially for segmentation masks. Although interactive segmentation accelerates this process, these methods still require substantial manual interaction, and the creation of large datasets remains labor-intensive. Consequently, there is a lack of diverse, high-quality datasets for unknown object instance segmentation in everyday environments. This research proposes a semi-automatic, RGB-only algorithmic pipeline for annotating novel objects, reducing manual effort to iteratively placing objects in the scene. We investigate several change detection-based approaches, including remote sensing change detection methods (TTP model), the DeepBackgroundMattingV2 image matting model, and the Segment Anything Model (SAM1 + SAM2) prompted with automatically extracted change regions. We propose the novel ILIS dataset to evaluate these methods in challenging everyday scenes, displaying reliable automatic mask proposal performance of up to 0.9549 mIoU and 0.9565 boundary F1 score. This highlights the potential of this method to accelerate large-scale dataset creation, saving at least 27.27 hours per 1,000 images by eliminating manual annotations
Investigating Balance Responsible Party Mismanagement through Comprehensive Schedule Analysis: Day-Ahead to Day-After
No full textEffective balancing group management is vital for ensuring a stable electricity supply. However, significant deviations often occur without clear explanations, challenging grid reliability. This study introduces a comprehensive methodology that integrates correlation analysis, feature-based analysis, and linear regression to examine forecast discrepancies across day-ahead, intraday, and day-after timeframes. Utilizing daily and monthly Key Performance Indicators (KPIs), we analyzed over two million schedule notifications from 928 balance responsible parties within the 50Hertz Transmission System Operator (TSO) region. Our rigorously validated approach provides a sophisticated top-down evaluation of management behaviors, identifying underlying patterns and anomalies with enhanced accuracy. Aligning with the latest advancements in data analytics, this methodology aims to improve energy balance management by effectively detecting anomalies in daily forecast schedules for both production and consumption
Surface Roughness Prediction in Hard Turning (Finishing) of 16MnCr5 Using a Model Ensemble Approach
No full text504507This paper investigates the predictive quality in finishing turning processes of 16MnCr5, specifically focusing on the use of model ensemble methods to predict surface roughness. Surface roughness plays a critical role in determining the quality and functionality of machined components. The objective of this study is to develop an accurate and robust predictive model for surface roughness after finishing turning operations. The proposed approach employs a model ensemble method, which combines multiple predictive models to enhance the accuracy and reliability of the predictions. The experimental setup includes collecting data using various types of in-situ sensors along with corresponding surface roughness measurements. The collected dataset is used to train and test the ensemble model, which integrates different machine learning algorithms and statistical techniques. The results demonstrate that the model ensemble method yields superior predictive performance compared to individual models, effectively capturing the complex relationships between process signals and surface roughness
A Performance Cost/Benefit Analysis of Adaptive Computing in the Tactical Edge
No full textTactical Edge Computing is a promising solution to address the challenges of processing and managing large volumes of data collected by sensors deployed at the tactical edge. Tactical edge networks often lack sufficient bandwidth to transfer data at high rates. Much of the sensor raw data might be uninteresting and would waste networking resources to transmit. Edge computing solves these problems by staging the processing capabilities for sensor data at the edge, close to the data source. Local data processing reduces bandwidth needs in disadvantaged tactical networks. Furthermore, it could reduce the latency of data processing and avoid congestion in the tactical network. However, static solutions that deploy edge services could also be problematic because of the unpredictability of the tactical edge - the source / location of the data may not be known a priori, and there could be significant changes, such as sensors and nodes going offline. Therefore, tactical edge computing solution must be adaptive, where services are deployed on demand based on the sensors tasked and the mission requirements. This paper presents the work of the NATO IST-193 work on adaptive tactical edge computing and its analysis on the adaptivity benefits and overhead costs involved
Kernel-Based Signal Processing for Simultaneous Transmit-Receive Systems
No full text2731The purpose of the paper is to present an experimental investigation of digital self-interference cancellation (DSIC) in the presence of hardware impairments of a wireless transceiver operating in in-band full-duplex mode. The experiment will present a passive polarization-based antenna suppression followed by DSIC utilizing kernel-based methods such as the adaptive projected subgradient method (APSM) and support vector regression (SVR) as well as a combination of those. They employ various positive-definite kernels to estimate the linear and nonlinear parts of the effective self-interference (SI) channel collectively. These data-driven algorithms provide us with sparse representation of the SI signal, which not only cancels out the interference but also may be utilized to extract information from dynamic wireless channels. Besides, the adoption of these methods for classification tasks is straightforward
In symptomatic patients on as-needed inhaled corticosteroids-formoterol, VAS asthma is associated with small airways resistance
No full text132139Objectives: Impulse oscillometry (IOS) can demonstrate small airways disease even when spirometry values are normal. However, it is unknown if the absence of symptoms excludes increased small airways resistance in asthma patients. We aimed to correlate symptoms (assessed through visual analogue scales) with measures of small airways resistance in patients with asthma and to determine whether less symptomatic patients have increased small airways resistance. Methods: We conducted a single center, prospective cohort study. We included controlled asthma patients on as-needed inhaled corticosteroids-formoterol. Patients were evaluated on their symptom VASs, Spirometry and IOS (with R5-R20% measuring small airways resistance) which were measured both in periods when they were less symptomatic and symptomatic. Symptoms were assessed using MASK-air®, an mHealth app that includes a daily monitoring questionnaire with validated VASs. We correlated MASK-air VASs with small airways resistance. Results: We assessed 29 patients. There was a significant correlation between VAS asthma and R5-R20% in symptomatic periods (r = 0.43; 95% CI = 0.13;0.68, p = 0.019), but not in less symptomatic periods (0.04; 95% CI-0.40;0.46; p = 0.825). In less symptomatic periods, patients presenting with low VAS asthma (VAS < 30) displayed a lower median R5-R20% than the remainder (0.26 versus 0.35), as well as a lower R5% (0.13 versus 0.15) (p < 0.001). In 68.9% of less symptomatic patients, R5-R20 values remained higher than normal values. Conclusion: In symptomatic patients on as-needed inhaled corticosteroids-formoterol, VAS asthma was associated with small airways resistance. However, even if these patients are less symptomatic, small airways resistance may be higher than normal. Since SAD significantly affects asthma control, patients should be carefully followed-up, even in less symptomatic periods.61
19.2 THz S+C+L Transmission in a Field Deployed, Randomly-Coupled, Multicore Fiber
No full textWe report the highest throughput on any field-deployed fiber of 927.7 Tb/s (GMI-based) using a record wide 19.2 THz signal on a randomly-coupled 4-core-fiber, supported by the first reported Raman amplification in this class of fibers
Einfluss der Dehnrate auf das Umformpotenzial von 5xxx-Aluminiumblechlegierungen. Schlussbericht
No full textFür eine vermehrte Verwendung von 5xxx-AlMg(Mn)-Blechlegierungen im Karosseriebau als Ersatz für 6xxx-AlMgSi-Legierungen sprechen die kostengünstigere Halbzeugherstellung, stabilere Werkstoffzustände, die bessere Umformbarkeit bei Raumtemperatur (RT) ohne zusätzliche Wärmebehandlung und das gute Verformungsvermögen im Crashfall. Dennoch werden 5xxx-Aluminiumlegierungen aufgrund ihrer Neigung zur Fließfigurenbildung (PLC-Effekt) während des Umformens heute nicht für die Fertigung designprägender Außenhautteile im Karosseriebau eingesetzt. Aktuell verfügbare Veröffentlichungen weisen aber darauf hin, dass der PLC-Effekt durch eine Erhöhung der Umformgeschwindigkeit unterdrückt werden kann. In diesem Projekt werden daher fließfigurenfreie Prozessfenster in Abhängigkeit von der Dehnrate ermittelt, um das Leichtbaupotential der 5xxx-Aluminiumlegierungen für die Herstellung von sowohl Struktur- als auch dekorativen Bauteilen in Zukunft besser nutzen zu können. Darüber hinaus wird das dehnratenabhängige Werkstoffverhalten der untersuchten Aluminiumlegierungen beim Umformen experimentell und numerisch analysiert. Hierfür wird das Verformungsverhalten bei ein- und mehrachsiger Zug- und Scherbelastung und bei Dehnraten von quasistatisch bis dynamisch untersucht. Weiterhin werden neben FLC-Kurven vor allem neuartige PLC-Kennkurven in Abhängigkeit der Dehnrate definiert und in die Umformsimulation bzw. einen kommerziellen FE-Code implementiert. Die Simulationsergebnisse werden dann anhand von Musterbauteilen validiert, die mit unterschiedlichen, produktionsrelevanten Umformgeschwindigkeiten erzeugt werden. Die Forschungsergebnisse dieses Vorhabens sind für KMU der metallverarbeitenden Industrie von besonderem Interesse, da sie z. B. durch Kosteneinsparungen beim Materialeinkauf, sortenreines Recycling durch Vermeidung von Materialmix, günstigere Fertigung von Aluminiumblechbauteilen bei RT und von zuverlässigeren Kennwerten profitieren können
Atlas-Augmented Semantic Segmentation for Robust Ultra-Low-Field Pediatric Brain Imaging
No full text8697Low-field MRI offers a portable, cost-effective alternative to conventional high-field scanners but suffers from reduced signal-to-noise ratio and spatial inhomogeneity, which compromise the accuracy and consistency of automated brain structure segmentation. In this work, we introduce atlas-augmented deep learning models that integrate probabilistic anatomical priors to enhance the delineation of pediatric hippocampus and basal ganglia in ultra-low-field MRI (0.064 T). We evaluate seven pipelines on the LISA 2025 dataset (79 T2-weighted scans): baseline VNet, nnU-Net, and MedSAM2 variants (2D and 3D decoders), as well as atlas-augmented VNet, atlas-augmented nnU-Net, and atlas-augmented MedSAM2-3D. For VNet and MedSAM2-3D, probabilistic maps from the Pauli and Harvard-Oxford atlases are encoded and fused with intermediate feature maps, while nnU-Net ingests priors as additional input channels. Baseline nnU-Net attains mean DSCs of 0.71 for hippocampus and 0.86 for basal ganglia; atlas augmentation yields modest hippocampal gains (HD95 ↓0.05, ASSD ↓0.06) and more pronounced improvements in basal ganglia segmentation, reflecting richer prior information for larger structures. VNet and MedSAM2 variants exhibit limited hippocampal benefit, highlighting the strength of nnU-Net’s adaptive framework. Our findings establish atlas-augmented nnU-Net as a new benchmark for robust segmentation in resource-constrained, low-field imaging environments. The code for our methods will be publicly accessible after the successful publication of the paper here: https://github.com/mackostya/deepatlas-ulf-seg