Search Results (50)

Laser-cladding directed energy deposition enables both the repair and fabrication of complex metallic components with curved surfaces. However, during multi-axis deposition on curved substrates, sharp transient feed-rate fluctuations at corner segments—together with an approximately constant powder feed rate—readily cause local over-deposition and geometric defects (e.g., nodules and humps). These defects compromise surface-profile fidelity, thereby creating a major barrier to practical deployment. To overcome this limitation, we propose a corner-oriented path-optimization strategy based on geometric code parsing. By operating directly on the toolpath without modifying the Computer-Aided Design model or slicing workflow, the proposed method suppresses corner overbuild and associated morphological distortion in curved-surface directed energy deposition, substantially improving dimensional consistency and surface quality. Overall, this strategy provides a scalable and broadly applicable route toward high-precision, high-reliability, industrial-scale curved-surface additive manufacturing. Full article

Material tracking and localization are key applications of Industry 4.0 in manufacturing process control. Traditional approaches—such as barcode or QR code identification and RTLS-based localization using RF/UWB, 5G or GPS–require a large and complex infrastructure. As an alternative, this paper proposes an IoT-based solution that combines short-range Bluetooth Low Energy (BLE) communication with LPWAN LoRaWAN networks. Hybrid solutions using LoRaWAN and BLE technologies already exist, but pure localization based on BLE tags can lead to ambiguous asset identification in geometrically dense scenarios. Our paper aims to solve this problem with an alternative concept called Associated LoRaWAN Sensors (ALSs). An ALS enables logical grouping and integration of heterogeneous LoRaWAN sensors, providing their own data or directly scanning BLE tags. Sensor data can be combined and supplemented with new information, data, and events, supported by application logic (use case). Although ALS represents a general concept that could be applicable to various use cases (such as warehouse monitoring, object tracking), our paper will focus mainly on material tracking and validation in manufacturing. For this purpose, we designed a specific ALS model that integrates a classic LoRaWAN BLE sensor with an additional LoRaWAN magnetic contact sensor. The magnetic contact switch can provide validation of exact position, in addition to localization by BLE tag. Experimental validation using BLE tags (Trax 10229) and LoRaWAN sensors (IoTracker3, Milesight WS301) demonstrates the usability of the ALS model in typical industrial scenarios. We also measured RSSI and evaluated the accuracy of tag localization (3 × 25 = 75 tests) for the worst-case scenario: material validation on a machine with a BLE tag distance of ~0.5 m. While the traditional approach showed up to a 20% failure rate, our ALS model avoided the issue of incorrect accuracy. An additional magnetic switch in ALS confirmed that the correct carrier with the associated tag is attached to the machine and eliminated incorrect localization. The results confirm that a hybrid model based on BLE and LoRaWAN scanning can reliably support material localization and validation without the need for dense RTLS infrastructures. Full article

In medical 3D printing, accuracy is critical for fabricating patient-specific implants and anatomical models. Although printer performance has been widely examined, the influence of slicing software on geometric fidelity is less frequently quantified. The slicing step, which converts STL files into printer-readable G-code, may introduce deviations that affect the final printed object. To quantify slicer-induced G-code deviations by comparing G-code-derived geometries with their reference STL modelsTwenty mandibular models were processed using five slicers (PrusaSlicer (version 2.9.1.), Cura (version 5.2.2.), Simplify3D (version 4.1.2.), Slic3r (version 1.3.0.) and Fusion 360 (version 2.0.19725)). A custom Python workflow converted the G-code into point clouds and reconstructed STL meshes through XY and Z corrections, marching cubes surface extraction, and volumetric extrusion. A calibration object enabled coordinate normalization across slicers. Accuracy was assessed using Mean Surface Distance (MSD), Root Mean Square (RMS) deviation, and Volume Difference. MSD ranged from 0.071 to 0.095 mm, and RMS deviation from 0.084 to 0.113 mm, depending on the slicer. Volumetric differences were slicer-dependent. PrusaSlicer yielded the highest surface accuracy; Simplify3D and Slic3r showed best repeatability. Fusion 360 produced the largest deviations. The slicers introduced geometric deviations below 0.1 mm that represent a substantial proportion of the overall error in the FDM workflow. Full article

Sparse code multiple access (SCMA) and reconfigurable intelligent surfaces (RISs) are two promising techniques in the forthcoming 6G communication networks to provide massive connectivity and enhance the spectral efficiency. To our best knowledge, the phase optimization for the reflecting elements and multi-user detection for the RIS-assisted downlink MIMO-SCMA system is still an open issue. In this way, we first formulate the RIS-assisted downlink MIMO-SCMA model with respect to the phases of the reflecting elements for the RIS. Next, a closed-form solution to these phases is found by solving the geometric median optimization. The iterative symbol detection steps are also provided for the RIS-assisted downlink MIMO-SCMA system. Simulation results illustrate that the proposed RIS-assisted downlink MIMO-SCMA system can significantly enhance the bit error ratio performance; e.g., the RIS-SCMA system with the proposed Gmedian-optimized phases can achieve a 1.5dB SNR gain as compared to the random phases with 10 reflecting elements. Full article

Efficient spatial indexing is critical for processing large-scale remote sensing datasets (e.g., LiDAR point clouds, orthophotos, hyperspectral imagery). We present a bidirectional, hierarchical index based on the Polish PL-2000 coordinate reference system for (1) direct computation of a map-sheet identifier from metric coordinates (forward encoder) and (2) reconstruction of the sheet extent from the identifier alone (inverse decoder). By replacing geometric point-in-polygon tests with closed-form arithmetic, the method achieves constant-time assignment O(1), eliminates boundary-geometry loading, and enables multi-scale aggregation via simple code truncation. Unlike global spatial indices (e.g., H3, S2), a CRS-native, aligned with cartographic map sheets in PL-2000 implementation, removes reprojection overhead and preserves the legal sheet semantics, enabling the direct use of deterministic O(1) numeric keys for remote-sensing data and Polish archives. We detail the algorithms, formalize their complexity and boundary rules across all PL-2000 zones, and analyze memory trade-offs, including a compact 26-bit packing of numeric keys for nationwide single-table indexing. We also discuss integration patterns with the OGC Tile Matrix Set (TMS), ETL pipelines, and GeoAI workflows, showing how bidirectional indexing accelerates ingest, training and inference, and national-scale visualization. Although demonstrated for PL-2000, the approach is transferable to other national coordinate reference systems, illustrating how statutory map-sheet identification schemes can be transformed into high-performance indices for modern remote sensing and AI data pipelines. Full article

Accurate identification of rear light signals in preceding vehicles is pivotal for Advanced Driver Assistance Systems (ADAS), enabling early detection of driver intentions and thereby improving road safety. In this work, we present a novel approach that leverages a meta-learning-enhanced YOLOv8 model to detect left and right turn indicators, as well as brake signals. Traditional radar and LiDAR provide robust geometry, range, and motion cues that can indirectly suggest driver intent (e.g., deceleration or lane drift). However, they do not directly interpret color-coded rear signals, which limits early intent recognition from the taillights. We therefore focus on a camera-based approach that complements ranging sensors by decoding color and spatial patterns in rear lights. This approach to detecting vehicle signals poses additional challenges due to factors such as high reflectivity and the subtle visual differences between directional indicators. We address these by training a YOLOv8 model with a meta-learning strategy, thus enhancing its capability to learn from minimal data and rapidly adapt to new scenarios. Furthermore, we developed a post-processing layer that classifies signals by the geometric properties of detected objects, employing mathematical principles such as distance, area calculation, and Intersection over Union (IoU) metrics. Our approach increases adaptability and performance compared to traditional deep learning techniques, supporting the conclusion that integrating meta-learning into real-time object detection frameworks provides a scalable and robust solution for intelligent vehicle perception, significantly enhancing situational awareness and road safety through reliable prediction of vehicular behavior. Full article

Modular steel buildings represent a structural system distinguished by rapid construction and environmental sustainability. The modular units and steel components of modular steel structures can be recycled, making this approach an important technology for sustainable development. Glass curtain walls, commonly used as facade systems in modern architecture, have recently appeared in several modular steel buildings. In this study, a seven-story model steel building is designed with a geometric scale factor of 1/9 to investigate its global and local safety in terms of seismic responses. Two glass curtain walls are installed on the seventh story of the model structure. A series of shaking table tests is conducted under varying seismic intensity levels (PGA = 0.035 g, 0.1 g, 0.22 g, 0.31 g). The results show the acceleration responses at the top story are predominantly governed by the fundamental translational modes (first mode and second mode). A slight stiffness degradation of a ratio less than 8.0% appears after the tests. The modular steel structure exhibits a significant acceleration amplification effect under almost all examined load cases. The measured peak structural accelerations (PSAs) notably exceed the limitations specified in current codes. The finite element simulation has validated such amplification. In addition, compared to these global responses, the glass curtain walls exhibit even higher PSAs, making them more vulnerable than the main steel frame. Therefore, the unfavorable seismic performance of modular steel buildings is manifested, and more attention needs to be paid to their design principles. Full article

By imposing finite order constraints on Fibonacci anyon braid relations, we construct the finite quotient $G={\mathbb{Z}}_5⋊2I$, where $2I$ is the binary icosahedral group. The Gröbner basis decomposition of its $SL(2,\mathbb{C})$ character variety yields elliptic curves whose L-function derivatives $L^{\prime }(E,1)$ remarkably match fundamental biological structural ratios. Specifically, we demonstrate that the Birch–Swinnerton-Dyer conjecture’s central quantity: the derivative $L^{\prime }(E,1)$ of the L-function at 1 encodes critical cellular geometries: the crystalline B-DNA pitch-to-diameter ratio ($L^{\prime }(E,1)=1.730$ matching $34\AA /20\AA =1.70$), the B-DNA pitch to major groove width ($L^{\prime }=1.58$) and, additionally, the fundamental cytoskeletal scaling relationship where $L^{\prime }(E,1)=3.570\approx 25/7$, precisely matching the microtubule-to-actin diameter ratio. This pattern extends across the hierarchy ${\mathbb{Z}}_5⋊2P$ with $2P\in \{2O,2T,2I\}$ (binary octahedral, tetrahedral, icosahedral groups), where character tables of $2O$ explain genetic code degeneracies while $2T$ yields microtubule ratios. The convergence of multiple independent mathematical pathways on identical biological values suggests that evolutionary optimization operates under deep arithmetic-geometric constraints encoded in elliptic curve L-functions. Our results position the BSD conjecture not merely as abstract number theory, but as encoding fundamental organizational principles governing cellular architecture. The correspondence reveals arithmetic geometry as the mathematical blueprint underlying major biological structural systems, with Gross–Zagier theory providing the theoretical framework connecting quantum topology to the helical geometries that are essential for life. Full article

This study presents a newly developed program that seamlessly converts G-code into formats compatible with Abaqus, enabling precise finite element simulations for FDM 3D printing. The tool operates on a two-pronged framework: a mathematical model incorporating key print parameters (layer thickness, extrusion temperature, print speed, and raster width) and a shape generator managing geometric parameters (fill density, pattern, and raster orientation). Initially, a predefined virtual section, based on predetermined dimensions, enhanced the correlation between experimental results and simulations. Subsequently, a corrected virtual section, derived from the mathematical model using the Box–Behnken methodology, improves accuracy, achieving a virtual thickness error of 1.06% and a width error of 8%. The model is validated through tensile testing of ASTM D638 specimens at 0°, 45°, and 90° orientations, using adaptive C3D4 mesh elements (0.35–0.6 mm). Results demonstrate that the corrected cross-section significantly improved simulation accuracy, reaching correlations above 95% in the elastic zone and 90% in the elastoplastic zone across all orientations. By optimizing the workflow from design to manufacturing, this program offers substantial benefits for the aerospace, medical, and automotive sectors, enhancing both the efficiency of the printing process and the reliability of simulations. Full article

While material extrusion via fused filament fabrication (FFF) offers design flexibility and rapid prototyping, its practical use in engineering is limited by mechanical challenges, including residual stresses, geometric distortions, and potential interlayer debonding. These issues arise from the dynamic thermal profiles during FFF, including temperature gradients, non-uniform hardening, and rapid thermal cycling, which lead to uneven internal stress development depending on fabrication parameters and object topology. These problems can compromise the structural integrity and mechanical properties of FFF parts, especially when the load-bearing capacity and geometric accuracy are critical. This study focuses on polylactic acid (PLA) due to its widespread application in engineering. It introduces a computational framework for coupled thermo-mechanical simulations of the FFF process using ABAQUS (Version 2020) finite element software. A key innovation is an automated subroutine that converts G-code into a time-resolved event series for finite element activation. The simulation framework explicitly models the sequential stages of printing, cooling, and detachment, enabling prediction of adhesive loss and post-process warpage. A transient thermal model evaluates the temperature distribution during FFF, providing boundary conditions for a mechanical simulation that predicts residual stresses and warping. Uniquely, the proposed model incorporates the detachment stage, enabling a more realistic and experimentally validated prediction of warpage and residual stress release in FFF-fabricated components. Although the average deviation between predicted and measured displacements is about 10.6%, the simulation adequately reflects the spatial distribution and magnitude of warpage, confirming its practical usefulness for process optimization and design validation. Full article

Furniture is an integral part of daily life. Its comfort and usability are key factors that define its success. In recent years, there has been increasing demand for applications that drive businesses toward Industry 4.0. These applications aim to improve productivity through greater automation in both 3D modeling and fabrication processes. This research aims to develop a Computer Aided Design (CAD) platform that automates the design and manufacturing of furniture. The platform is based on visual programming using Grasshopper 3D™ and provides a solid foundation for processing different geometric shapes. These shapes can be customized according to the user’s preferences. The platform’s innovation lies in its ability to process complex geometries with a fully automated algorithm. Once the initial parameters are set, the algorithm generates the results. The input data includes an initial geometry, which can be highly complex. Additionally, a set of construction parameters is introduced, leading to multiple alternative design solutions based on the same initial geometry. The designer and user can select their final choice, and all resulting design and manufacturing outcomes are automatically generated. These outcomes include 3D part models, 3D assembly files, Bill of Materials, G-code for CNC machining, and nesting capabilities for improved material efficiency. The platform ensures high-quality performance. The results of the study show that the platform successfully works with different geometries. Moreover, the study is significant as the Industry 4.0 transformation moves toward more automated design processes. Full article

This study provides an in-depth structural analysis of UNESCO World Heritage Apulian trulli, considering the three-layer dry-stone structure of their characteristic conical roofs. An integrated approach involving laser scanning, ground-penetrating radar, endoscopic investigation, and laboratory materials testing is used to identify and characterize the multi-wythe masonry system. A detailed finite element model is created in ANSYS to analyze seismic performance on Italian building codes. The model is validated through ambient vibration testing using accelerometric measurements. The diagnostic survey identified a three-layer system including exterior stone wythe, interior wythe, and rubble core, with compressive strength of stone averaging 2.5 MPa and mortar strength of 0.8 MPa. The seismic assessment will allow the examination of displacement patterns and stress distribution under design load conditions (ag = 0.15 g). The structural analysis demonstrates adequate performance under design loading conditions, with maximum stress levels remaining within acceptable limits for historic masonry construction. The experimental validation confirmed the finite element model predictions, with good correlation between numerical and experimental frequencies. The improvement of the overall seismic performance with the multi-wythe configuration and the role of wall thickness and geometric proportions will be taken into account. The methodology aims to provide technical evidence supporting the continued use of vernacular buildings while contributing to scientifically informed conservation practices throughout the region. Full article

This study proposes a unified span–depth ratio model aimed at optimizing the minimum thickness of reinforced concrete flat plate slabs, addressing the limitations of the simplified span-to-depth ratio provisions in ACI 318. The existing code does not fully consider critical parameters such as panel aspect ratio, reinforcement ratio, support conditions, concrete strength, and long-term deflections due to creep and shrinkage. To overcome these shortcomings, a generalized analytical model is developed based on fundamental deflection theory, incorporating both immediate and time-dependent behaviors. The model is validated through numerical simulations applied to interior, edge, and corner slab panels subjected to various geometric configurations, loading scenarios, and reinforcement levels. Results from the parametric study indicate that deflection control improves significantly with higher reinforcement ratios and lower aspect ratios, leading to more efficient slab designs. Comparisons with ACI 318 guidelines reveal that the proposed model provides enhanced accuracy, particularly for irregular slab geometries and stringent deflection limits (e.g., L/480). The findings highlight that conventional code-based thickness limits may underestimate slab depth requirements in many practical scenarios. The study advocates for integrating deflection-based considerations into the preliminary design stage, offering structural engineers a more robust and practical tool to ensure serviceability while optimizing material use. Full article

The roughness of the Earth’s surface dictates the nature of air flow across it. Detailed meteorological data that are necessary to access the aerodynamic roughness ($z_0$) are not widely collected and, as such, the geometry of a surface can be used to estimate $z_0$. Here, we present a novel formulation, and the corresponding computer code, to compute $z_0$ based on the Lettau (1969) geometric approach. The new code produces a mean $z_0$, as well as a histogram of all $z_0$ values for each individual roughness element (e.g., 10 s of thousand for the 1000 × 1000 grids) discretized using watersheds, as well as directional $z_0$ diagrams, which can be matches with the wind rose for the location. The formulation includes two parameters that may optionally be applied to smooth the surface before calculating $z_0$. By calculating $z_0$ as a function of these two parameters, we demonstrate the sensitivity of the $z_0$ value to these parameter choices. Since a large portion of the Earth’s surface is snow covered during some parts of the year, and the roughness of the snow surface varies over the snow season and over space, we apply the code to three snow surface datasets. Each surface is during a different phases of the snowpack. Each surface is evaluated at two resolutions). These surfaces are: fresh snow accumulation (1 m² at 1 and 10 mm), peak accumulation (1 km² at 1 and 10 m) and ablation sun cups (25 m² at 5 and 50 mm). Full article

Both commercial and research applications of wire arc additive manufacturing (WAAM) have seen considerable growth in the additive manufacturing of metallic components. However, there remains a clear lack of a unified paradigm for toolpath generation when slicing parts for WAAM deposition. Existing toolpath generation options typically lack the appropriate features to account for all complexities of the WAAM process. This manuscript explores the key slicing challenges specific to toolpaths for WAAM geometry and pairs each consideration with multiple solutions to mitigate most negative effects on completed components. These challenges must be addressed to minimize voids, prevent bead collapse, and ensure deposited components accurately approximate the desired geometry. Slicing considerations are grouped into four general categories: geometric, process, thermal, and productivity. Geometric considerations are addressed with overhang compensation, corner-sharpening, and toolpath-smoothing features. Process considerations are addressed with start point configuration and controls for the bead lengths and end points. Thermal and productivity considerations are addressed with island optimization, multi-material printing, and connected insets. Finally, tools for the post-processing of generated G-code are explored. Overall, these solutions represent a critical set of slicing features used to improve generated toolpaths and the quality of the components deposited with those toolpaths. Full article