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Experimental and numerical study on grain refinement in electromagnetic assisted laser beam welding of 5754 Al alloy
No full textThrough experimental observation and auxiliary numerical simulation, this investigation studies the different types of grain refinement of 5754 aluminum alloy laser beam welding by applying a transverse oscillating magnetic field. Scanning electron microscope results have proved that the application of a magnetic field can reduce the average crystal branch width and increase its number. The interaction between the induced eddy current generated by the Seebeck effect and the applied external magnetic field produces a Lorentz force, which is important for the increase in the number of crystal branches. Based on the theory of dendrite fragmentation and the magnetic field-induced branches increment, the grain size reduction caused by the magnetic field is studied. Furthermore, the effects of the magnetic field are analyzed by combining a phase field method model and simulations of nucleation and grain growth. The grain distribution and average grain size after welding verify the reliability of the model. In addition, the introduction of a magnetic field can increase the number of periodic three-dimensional solidification patterns. In the intersection of two periods of solidification patterns, the metal can be re-melted and then re-solidified, which prevents the grains, that have been solidified and formed previously, from further growth and generates some small cellular grains in the new fusion line. The magnetic field increases the building frequency of these solidification structures and thus promotes this kind of grain refinement.35
CAD-Based Product Partitioning for Automated Disassembly Sequence Planning with Community Detection
No full text570577Remanufacturing valuable components, like lithium-ion batteries from an automotive battery pack, is crucial in turning our society more sustainable. For scalable remanufacturing, disassembly planning with a minimum number of assembly steps is required. CAD-based assembly-by-disassembly method can be utilized to generate disassembly sequence plans automatically. However, this approach is computationally expensive due to graphical collision analysis. For assembly step minimization, the computing time can be reduced by identifying dismantlable subassemblies. Therefore, we propose an automated partitioning of assemblies into feasible subassemblies, which is then fed into the final step of the assembly-by-disassembly method to produce a precedence graph. A comparison of the two popular community detection algorithms - the Louvain algorithm and the Girvan–Newman algorithm - have been applied to exemplary CAD models. The Louvain algorithm showed the best results with a distance-weighted contact matrix. The results show that subassemblies can be proposed for disassembly planning. Nevertheless, the uncertainty in clustering quality is still too high to utilize the approach in a fully automated planning pipeline
Reliability of AlScN/GaN HEMTs Under Pulsed Measurements and HTRB Step-Stress Tests: Experimental and TCAD Insights
No full text19This work investigates the reliability of AlScN/GaN High-Electron-Mobility Transistors (HEMTs) by integrating experimental analyzes with Technology Computer-Aided Design (TCAD) simulations. The study focuses on pulsed I-V measurements and High-Temperature Reverse Bias (HTRB) step-stress tests. The former have been performed under different quiescent conditions highlight short-term transient charge trapping, while the latter reveals long-term threshold voltage (Vth), transconductance (gm), saturation drain current (ID.ss) and gate leakage (IG) shifts. A TCAD model calibrated on experiments is employed to deeply understand the interplay of the different sources of degradation. In pulsed analyzes, iron traps are identified as the primary degradation contributors. In HTRB step-stress regime, trapped charges under the gate at the 2DEG interface are modeled to reproduce the Vth shift, while the decreased gm is mostly ascribed to donor-trap detrapping at the SiN passivation interface. The relative ID.ss[%] shift and IG are used to validate the proposed approach. Such insights also provide a net comparison of the degradation phenomena in AlScN-based HEMTs with respect to AlGaN-based counterparts, paving the way for improved technology and device designs.1
Exploring Purposes of Using Taxonomies
No full textTaxonomies are artifacts that can be used for numerous purposes, including gap spotting, decision-making, and theory building. Despite the variety of usage purposes, we can observe that designers state that their taxonomies help to ‘classify something’; leaving the full potential of taxonomies rather untapped. In order to lay attention on questions of for what taxonomies can be used, this short paper (1) raises awareness of the actual problem space and motivate the relevance of an overview of taxonomy use purposes, (2) outlines the overall project’s research design to identify and structure the set of use purposes, and (3) proposes preliminary purposes extracted from analyzing a corpus of articles that built upon—and use—previously published taxonomies. In doing this, we seek to complement available methodological guidance to make more informed decisions in terms of a taxonomy’s usage potential
Bidirectional coupling of Building Information Modeling and Building Simulation using ontologies
No full text207217Building performance simulation can contribute to necessary energy savings in the buildings sector when applied early in the design phase. A major obstacle to put that into operation is the cumbersome task of model preparation, although significant simplification is anticipated by the adoption of BIM in the design phase. In this paper a workflow is shown for the bidirectional coupling of IFC and Modelica simulation models based on semantic tools (also facilitating the BRICK ontology). Bidirectionality allows for better integration in the building design workflow as it enables iterative approaches. A show case is made for an air handling system
Deep Learning Based Real-Time Spectrum Analysis for Wireless Networks
No full text813Wireless networks are indispensable in today’s production and automation. In an industrial environment, they are used in particular for networking moving or inaccessible parts of a factory. Due to difficult signal propagation conditions as well as coexisting wireless networks, transmission errors that can lead to malfunctions in the application are often very difficult to identify. Wireless remote monitoring systems are essential tools for diagnosing such faults. This work is intended to contribute to the improvement of failure analysis tools for wireless networks. The fundamental approach relies on an extension of standard protocol analysis tools by sensitive measurements of a spectrum analyser. Using Machine Learning-based image processing algorithms, individual frames of different radio technologies are detected in real time and classified according to their communication standard. A subsequent statistical processing or anomaly detection provides an abstract view to the spectrogram and enables a cross connection to the protocol analysis. Using IEEE 802.11 as an example, it can be shown that frames and frame collisions can be detected with a high degree of accuracy. In conclusion, the performance of the developed method is evaluated and compared with a protocol-based monitoring system
Transformer-based lossy hyperspectral satellite data compression
No full textHyperspectral satellite sensors generate vast amounts of data, making efficient compression crucial for storage, transmission, and downstream analysis. We propose SpectralNet-X, a hybrid convolution-Transformer autoencoder that operates purely in the spectral domain. A 1D convolutional projection captures local spectral smoothness, while transformer layers with Rotary Positional Embeddings model long-range dependencies. A small set of learnable queries performs cross-Attention pooling, yielding a compact latent space that serves as the compression bottleneck. To improve stability and reconstruction accuracy, we pretrain the encoder using a masked spectral reconstruction objective before fine-Tuning the full autoencoder. Experiments on PRISMA hyperspectral data demonstrate that SpectralNet-X consistently outperforms a pure transformer baseline (HyCoT) and achieves competitive spectral fidelity, although a convolutional baseline (A1D-CAE) remains superior. These findings highlight the potential of hybrid CNN-Transformer architectures for hyperspectral compression and motivate future research on scaling data, refining loss functions, and extending evaluation
A Reference Architecture for Digital Twin-Based Supply Chain Information Exchange
No full text15931610In this design science research project, based on the Catena-X automotive data space, we built a novel reference architecture for digital twin-based supply chain information sharing. The artifact aims at increasing supply reliability by sharing short-term information that is commonly not shared within a network of actors due to its sensitivity. Our architecture addresses these issues by providing measures for sensitive and compliant information sharing. Besides data sovereignty and compliance, we derived and fulfilled the design objectives of interoperability, up-to-date information, flexible information sharing and adoption. Our design project ran through two major design iterations of building and evaluating the architecture through the instantiation of a reference implementation that has been published as free open-source software
Techno-Economic Optimization of Solar Tower Systems: Comparison of Different Sites in Chile
No full textChile presents a very suitable scenario for CSP technology, since the DNI exceeds 2000 kWh/m2/year [1] in more than half of the country, from the capital to the north. The objective of this study is to analyze the potential LCOE of solar tower power plants in Chile and to make a comparative site assessment for locations best suited for CSP in Chile. Additionally, also presents a novel method for scaling low accuracy weather data. For the techno-economic optimization, three sites at different latitudes are selected, for which a techno-economic optimization of the solar tower system is performed. The optimum thermal storage capacity is identified, together with two factors introduced in this study: the thermal multiple and the optical multiple. The results show that the introduction of the thermal and optical multiples provides a powerful method for comparative site assessment and plant predesign. The low LCOE obtained for the three locations (0.081-0.057 /kWh), yields a very favorable scenario for solar thermal plants in Chile, not only in the northern parts of the country, but also in regions close to Santiago