Search Results (127)

This study investigates the fatigue behavior of 3D-printed ABS subjected to cyclic torsional loads, with a focus on the effects of infill density and raster angle on torsional fatigue performance. A total of 50 test specimens representing 25 unique combinations of infill density (20%, 40%, 60%, 80%, 100%) and raster angle (25°/−65°, 45°/−45°, 75°/−15°, 0°/90°) were fabricated and tested using the cyclic torsion system. Fatigue failure was defined as a 75% reduction in torsional strength, recorded through cycle-by-cycle torque monitoring. The twist angle was cyclically varied between ±10° at a frequency of 5 Hz until failure occurred. The results indicate that increasing infill density significantly improves fatigue life by reducing internal porosity and enhancing load transfer, with the greatest gains observed at high infill levels (≥80%). Raster angle has a minimal effect at low infill densities but becomes critical at higher densities, where optimized filament orientations substantially extend fatigue life. Intermediate raster angles, particularly 25° and 75°, outperform orthogonal layouts by enabling better stress redistribution and inter-layer load sharing, while a 90° orientation leads to premature failure due to stress concentration and inter-layer debonding. When normalized by mass, specimens with 100% infill and intermediate raster angles achieve the highest fatigue endurance, highlighting the synergistic role of infill density and raster orientation in optimizing the durability and mass efficiency of 3D-printed components under cyclic torsional loading. Full article

In the field of printed circuit board (PCB) manufacturing, surface defect detection serves as a critical process in the production line, directly impacting the quality and safety of subsequent electronic products. However, accurately detecting tiny surface defects in real time remains a significant challenge given the complex layouts of PCBs. To address this issue, this study proposes a novel Ghost-BiFPN-Efficient-YOLOv8 (GBE-YOLOv8) model architecture for PCB defect detection based on an improved YOLOv8n. The backbone network of the model employs lightweight Ghost Conv to partially replace regular convolutions, thereby reducing computational complexity and parameter count. The neck network incorporates a multi-stage feature fusion module named G-C2f and a dynamic weighting module named BiFPN-Concat to enhance the model’s ability to characterize PCB defects. The model’s head network employs an Efficient Head that combines mixed depthwise convolution and partial convolution, further optimizing detection accuracy and computational efficiency. Simultaneously, a comprehensive evaluation of model performance was conducted using publicly available datasets. And the working mechanisms of each improved method were analyzed through class activation heatmaps to further enhance the interpretability of the model. Experimental results demonstrate that compared to the baseline model and several other state-of-the-art object detection algorithms, the proposed method exhibits significant advantages across various evaluation metrics, and its mAP@0.5, mAP@0.5:0.95, parameters, GFLOPs and FPS achieve 98.9%, 61.4%, 2.6 M, 7.5 and 252, respectively. Furthermore, each optimization method achieves the expected design purpose, and the combined application of all optimization methods enables the model to strike an optimal balance between detection accuracy and computational complexity. Consequently, this research can provide a reliable technical solution for high-precision real-time detection of surface defects on PCBs in industrial production lines. Full article

Automated optical inspection (AOI) for printed circuit boards (PCBs) requires localizing small, sparse defects under illumination drift and minor placement misalignment, while supporting fast, auditable pass/fail decisions. This paper presents a training-free, reference-based digital image processing framework with no learning/training stage that compares each defective query image with a small library of defect-free reference templates (for the same PCB layout/revision) using a small set of interpretable control parameters. A reference is selected by coarse-to-fine matching (fast pre-screening followed by SSIM refinement on a central region), and an optional global alignment is applied only when it increases SSIM to limit defect-driven over-correction. Defects are highlighted by a defect-likelihood field that fuses an SSIM-derived structural dissimilarity map with a normalized absolute-difference map, followed by connected-component extraction to produce confidence-ranked bounding boxes. The method achieves Precision = 0.9663, Recall = 0.9987, and F1 = 0.9822 at the best-F1 operating point (0.149 false positives per image). Under the adopted box-matching protocol, average precision reaches 0.984. Precision–recall and FROC curves are reported to support threshold selection under different false-alarm budgets. Full article

Continuous plantar-pressure monitoring is important for objective gait analysis and early detection of abnormal loading; however, many existing solutions remain laboratory-bound (force plates and instrumented walkways) or rely on costly in-shoe multilayer sensor arrays. Here, we developed and optimized a fully screen-printed pressure-sensing insole based on carbon–polymer nanocomposite layers, with an emphasis on manufacturability and process control to bridge the gap between proof-of-concept force-sensitive resistor (FSR)-based insoles and scalable printed-electronics manufacturing workflows. Composite pastes containing carbon fillers (graphene nanoplatelets, carbon black, and graphite) were formulated to improve sensor repeatability and sensitivity. Sensors were characterized under compression loads from 100 N to 1300 N, showing a sensitivity of 10.5 ± 2.8 Ω per 100 N and a sheet-to-sheet coefficient of variation of 22.1% in resistance response. The effects of paste composition, screen mesh density, electrode layout, and lamination on sensitivity and repeatability were systematically evaluated. In addition, correlation analysis of resistance values from integrated quality-control meanders proved useful for monitoring screen-printing process stability. The final insole integrates printed carbon sensing pads and contacts, a dielectric spacer, and an adhesive layer in a thin, flexible format suitable for integration with wearable electronics. In practical static-load tests, repeated manual placement of weights yielded coefficients of variation as low as 4% at 500 g and a detection limit of ~0.1 N, comparable to a very light finger touch. These results demonstrate that low-cost screen-printed electronics can provide robust pressure sensing for wearable plantar-pressure monitoring. Full article

Waste printed circuit boards (WPCBs) contain valuable metals such as gold, palladium, and silver, which are typically recovered through non-ferrous metal smelting. Currently, WPCBs are manually classified by workers, who visually compare board colors and component layouts with previously processed boards. This approach is time-consuming and prone to human error. To address these limitations, we propose an image-based algorithm for automated WPCB similarity assessment. The method extracts visual features from board images and computes similarity scores, incorporating classification strategies based on board-specific characteristics. Key features identified as effective for similarity evaluation include the hue value, coefficient of variation in terminal regions, number of line elements in terminal regions, structural complexity, and number of integrated circuits. Weighted feature contributions further improve accuracy. Our experimental results demonstrate that the proposed approach achieves 88.0% accuracy for the targeted PCB types, outperforming a comparative self-supervised contrastive learning method. This image-driven solution can significantly streamline WPCB recycling by reducing reliance on manual inspection and improving operational efficiency. Full article

Firm requirements on electromagnetic compatibility (EMC) of electronic devices demand low electromagnetic emissions (EMI) of high-speed circuits, especially in the automotive industry. To be able to apply cost-effective anti-perturbative measures that reduce noise emission, critical signal integrity and power integrity (SI/PI) tools are needed for developing high-speed printed circuit board (PCB) designs. This paper presents an efficient method for modeling and analyzing the current drawn by digital ICs based on SPICE modeling data. The profile of the current drawn by the ICs from the power supply is composed of the static supply current and the dynamic supply current. This method enables power integrity engineers, in particular, PhD students and researchers who aim to develop an intuitive understanding of PI phenomena during the pre-layout phase, to see the hidden impact of the supply current on the power rail noise through time domain simulations, using a complex simulation model that integrates the Finite-Difference Time-Domain (FDTD) method of modeling the power and ground plane, with Voltage Regulator Modules (VRMs) and decoupling capacitors. A comparison of simulation results between the proposed models and SPICE IC models is also included to validate the proposed model. Full article

Electric Water Pumps (EWPs) are being adopted more widely to improve thermal management in internal combustion engines and electrified powertrain systems. In this context, the drive motor must deliver high efficiency and reliability despite a strict volume constraint. This paper addresses a key drawback of coreless printed circuit board (PCB) stator axial-flux permanent-magnet machines for EWP use: the PCB traces are directly exposed to the magnet flux, which increases AC loss, while the required phase resistance also leads to non-negligible DC copper loss. To mitigate both loss components within the same conductor design space, a pyramid trace concept is introduced. A magnetic equivalent circuit (MEC) based model is first used to estimate the baseline performance as the number of PCB stator modules changes, and the resulting scalability is examined in terms of module commonality. The final design then applies the pyramid trace layout with a layer-dependent trace width that is narrower on the layers closer to the magnets and wider on the layers farther away—the trade-off between AC loss and DC loss is optimized using 3D finite element analysis. Torque predictions from the simplified MEC model are cross-checked against 3D finite element analysis (FEA), and finally, a prototype is built to validate the analysis with experimental measurements; for the final selected model, the torque prediction error is 2.37% compared with the validation result. Full article

This work describes the design and experimental testing of a low-noise amplifier (LNA) fabricated on a printed circuit board (PCB) and operating near 2 GHz. The amplifier uses a discrete bipolar junction transistor (BJT) together with fully distributed microstrip matching networks without relying on lumped matching components. The main design goal is to obtain stable operation with low noise figure and moderate gain over a wide frequency range while keeping the circuit tolerant to layout parasitics and fabrication variations. Circuit-level simulations are performed using AWR Microwave Office and are followed by full-wave electromagnetic simulations in Sonnet Software to account for layout-dependent effects. A prototype is fabricated on a 60-mil Rogers RO4003C substrate and characterized through S-parameter, noise-figure, and linearity measurements. Measured results show a gain of approximately 13.84 ± 1 dB over the 1.75–2.25 GHz frequency range, with a minimum noise figure of 1.615 dB at 2 GHz. Stable operation is maintained across the entire band, and the measured 1 dB gain compression point is approximately 0.5 dBm. The results demonstrate that a fully distributed microstrip matching approach provides a practical and reproducible PCB-based LNA solution for sub-6-GHz receiver front-end applications. Full article

Motor position sensors are critical parts for traction motors control in electrified automotive powertrains. As motors are becoming more compact due to the advance of technology the packaging space for motor position sensors is becoming increasingly restricted. This study presents a two-layer (2L) printed circuit board (PCB) routing strategy for inductive motor position sensors with limited area. A prototype was fabricated and tested on a test bench using a comprehensive design of experiments that contains 625 combinations of X- and Y-offsets, tilt angle, and airgap at various levels (±0.5 mm in X/Y, ±0.5° tilt, 1.9–3.1 mm airgap). Across the tolerance box, the accuracy under all test cases remained within ±1 electrical degree. The accuracy analysis through Fourier series on a circle shows that the DC offset and magnitude mismatches of the 3 Rx signals are the dominant error contributors due to the routing modification. An Extreme Gradient Boosting (XGBoost) model was trained and validated with R² = 0.9951. A comparison with a Multiple Linear Regression baseline (R² = 0.0565) demonstrates that installation-induced accuracy degradation is inherently non-linear. The SHapley Additive exPlanations (SHAP) and interaction intensity analysis identified tilt and Y-offset as dominant error drivers, revealing a strong coupled influence (interaction intensity = 0.9581). The model revealed a mild Y-axis asymmetry introduced by routing modifications. This integrated workflow provides a general, quantitative framework for optimizing and analyzing inductive sensor layouts and establishing installation tolerances. Full article

A conceptual design study is presented for a hemispherical, two-chamber water Cherenkov detector instrumented with bladder-embedded light traps. The detector consists of a rigid aluminium vessel enclosing a water volume that is divided into an outer, optically black chamber and a inner, reflective chamber lined by a flexible bladder. Arrays of light-trap modules, based on plastic scintillators with wavelength-shifting elements and thin silicon photomultipliers, are integrated into the bladder and selected inner surfaces. This geometry is intended to enhance muon tagging, increase acceptance for inclined air showers, and enable improved discrimination between electromagnetic and hadronic components. The study describes the mechanical and optical layout of the detector, the baseline aluminium housing, and the use of 3D-printed hexagonal prototypes to validate integration of the bladder and readout electronics. A first-order structural assessment based on thin-shell and plate theory is presented, indicating large safety margins for the hemispherical shells and identifying the flat base as the mechanically most loaded component. While GEANT4 simulations for detector response to extensive air showers in the atmosphere and performance measurements are left to future work, the present study establishes a mechanically validated, costed baseline design and outlines the steps needed to assess its impact in air-shower arrays. Full article

Power converters based on gallium nitride (GaN) are progressing swiftly owing to their exceptional efficiency and tiny dimensions, boosted by high power density and fast switching capabilities. Nevertheless, these benefits are accompanied by considerable thermal management issues that impact reliability, performance, and operational lifespan. This review examines advanced thermal management approaches for high-power-density GaN power converters, including active and passive cooling technologies, sophisticated packaging designs, and the use of novel materials like graphene and diamond to improve heat dissipation. The impacts of thermal boundary resistance, self-heating phenomena, and substrate selection on thermal performance are thoroughly analyzed. Strategies for enhancing printed circuit board (PCB) layouts, thermal vias, and the use of thermal interface materials (TIMs) are also emphasized. The study highlights co-design approaches that optimize thermal resistance and layout efficiency, supporting GaN operation under high-frequency conditions. This thorough investigation offers insights into addressing the thermal challenges linked to GaN technology, promoting its adoption in forthcoming power devices. Full article

Printed circuit boards (PCBs) are critical in the electronics industry. As PCB layouts grow increasingly complex, defect detection processes often encounter challenges such as low image contrast, uneven brightness, minute defect sizes, and irregular shapes, making it difficult to achieve rapid and accurate automated inspection. To address these challenges, this paper proposes a novel object detector, YOLO-UMS, designed to enhance the accuracy and speed of PCB surface defect detection. First, a lightweight plug-and-play Unified Multi-Scale Feature Fusion Pyramid Network (UMSFPN) is proposed to process and fuse multi-scale information across different resolution layers. The UMSFPN uses a Cross-Stage Partial Multi-Scale Module (CSPMS) and an optimized fusion strategy. This approach balances the integration of fine-grained edge information from shallow layers and coarse-grained semantic details from deep layers. Second, the paper introduces a lightweight RG-ELAN module, based on the ELAN network, to enhance feature extraction for small targets in complex scenes. The RG-ELAN module uses low-cost operations to generate redundant feature maps and reduce computational complexity. Finally, the Adaptive Interaction Feature Integration (AIFI) module enriches high-level features by eliminating redundant interactions among shallow-layer features. The channel-priority convolutional attention module (CPCA), deployed in the detection head, strengthens the expressive power of small target features. The experimental results show that the new UMSFPN neck can help improve the AP50 by 3.1% and AP by 2% on the self-collected dataset PCB-M, which is better than the original PAFPN neck. Meanwhile, UMSFPN achieves excellent results across different detectors and datasets, verifying its broad applicability. Without pre-training weights, YOLO-UMS achieves an 84% AP50 on the PCB-M dataset, which is a 6.4% improvement over the baseline YOLO11. Comparing results with existing target detection algorithms shows that the algorithm exhibits good performance in terms of detection accuracy. It provides a feasible solution for efficient and accurate detection of PCB surface defects in the industry. Full article

We introduce KORIE, a curated benchmark of 748 Korean retail receipts designed to evaluate scene text detection, Optical Character Recognition (OCR), and Information Extraction (IE) under challenging digitization conditions. Unlike existing large-scale repositories, KORIE consists exclusively of receipts digitized via flatbed scanning (HP LaserJet MFP), specifically selected to preserve complex thermal printing artifacts such as ink fading, banding, and mechanical creases. We establish rigorous baselines across three tasks: (1) Detection, comparing Weakly Supervised Object Localization (WSOL) against state-of-the-art fully supervised models (YOLOv9, YOLOv10, YOLOv11, and DINO-DETR); (2) OCR, benchmarking Tesseract, EasyOCR, PaddleOCR, and a custom Attention-based BiGRU; and (3) Information Extraction, evaluating the zero-shot capabilities of Large Language Models (Llama-3, Qwen-2.5) on structured field parsing. Our results identify YOLOv11 as the optimal detector for dense receipt layouts and demonstrate that while PaddleOCR achieves the lowest Character Error Rate (15.84%), standard LLMs struggle in zero-shot settings due to domain mismatch with noisy Korean receipt text, particularly for price-related fields (F1 scores ≈ 25%). We release the dataset, splits, and evaluation code to facilitate reproducible research on degraded Hangul document understanding. Full article

Soil compaction in grassland and agricultural soils reduces water infiltration, root growth and ecosystem services. Conventional deep tillage and coring can alleviate compaction but are energy intensive and strongly disturb the turf. This study proposes an earthworm-inspired subsurface robot as a low-disturbance loosening tool for compacted grassland soils. Design principles are abstracted from earthworm body segmentation, anchoring–propulsion peristaltic locomotion and corrugated body surface, and mapped onto a robotic body with anterior and posterior telescopic units, a flexible mid-body segment, a corrugated outer shell and a brace-wire steering mechanism. Kinematic simulations evaluate the peristaltic actuation mechanism and predict a forward displacement of approximately 15 mm/cycle. Using the finite element method and a Modified Cam–Clay soil model, different linkage layouts and outer-shell geometries are compared in terms of radial soil displacement and drag force in cohesive loam. The optimised corrugated outer shell combining circumferential and longitudinal waves lowers drag by up to 20.1% compared with a smooth cylinder. A 3D-printed prototype demonstrates peristaltic locomotion and steering in bench-top tests. The results indicate the potential of earthworm-inspired subsurface robots to provide low-disturbance loosening in conservation agriculture and grassland management, and highlight the need for field experiments to validate performance in real soils. Full article

The surface defect detection of printed circuit boards (PCBs) plays a crucial role in the field of industrial manufacturing. However, the existing PCB defect detection methods have great challenges in detecting the accuracy of tiny defects under the complex background due to its compact layout. To address this problem, we propose a novel YOLO-AMBA-EASPP-BiFPN (YOLO-AEB) network based on the YOLOv10 framework that achieves high precision and real-time detection of tiny defects through multi-level architecture optimization. In the backbone network, an adaptive multi-branch attention mechanism (AMBA) is first proposed, which employs an adaptive reweighting algorithm (ARA) to dynamically optimize fusion weights within the multi-branch attention mechanism (MBA), thereby optimizing the ability to represent tiny defects under complex background noise. Then, an efficient atrous spatial pyramid pooling (EASPP) is constructed, which fuses AMBA and atrous spatial pyramid pooling-fast (ASPF). This integration effectively mitigates feature degradation while preserving expansive receptive fields, and the extraction of defect detail features is strengthened. In the neck network, the bidirectional feature pyramid network (BiFPN) is used to replace the conventional path aggregation network (PAN), and the bidirectional cross-scale feature fusion mechanism is used to improve the transfer ability of shallow detail features to deep networks. Comprehensive experimental evaluations demonstrate that our proposed network achieves state-of-the-art performance, whose F₁ score can reach 95.7% and mean average precision (mAP) can reach 97%, representing respective improvements of 7.1% and 5.8% over the baseline YOLOv10 model. Feature visualization analysis further verifies the effectiveness and feasibility of YOLO-AEB. Full article