Search Results (94)

Fault identification in Printed Circuit Board Assembly (PCBA) testing is essential for assuring product quality; nevertheless, conventional methods still have difficulties due to the lack of labeled faulty data and the “black box” nature of advanced models. This study introduces a label-free, interpretable self-supervised framework that uses two pretext tasks: (i) an autoencoder (reconstruction error and two latent features) and (ii) isolation forest (faulty score) to form a four-dimensional representation of each test sequence. A two-component Gaussian Mixture Model is used, and the samples are clustered into normal and fault groups. The decision is explained with cluster mean differences, SHAP (LinearSHAP or LinearExplainer on a logistic-regression surrogate), and a shallow decision tree that generated if–then rules. On real PCBA data, internal indices showed compact and well-separated clusters (Silhouette 0.85, Calinski–Harabasz 50,344.19, Davies–Bouldin 0.39), external metrics were high (ARI 0.72; NMI 0.59; Fowlkes–Mallows 0.98), and the clustered result used as a fault predictor reached 0.98 accuracy, 0.98 precision, and 0.99 recall. Explanations show that the IForest score and reconstruction error drive most decisions, causing simple thresholds that can guide inspection. An ablation without the self-supervised tasks results in degraded clustering quality. The proposed approach offers accurate, label-free fault prediction with transparent reasoning and is suitable for deployment in industrial test lines. Full article

Hexaglide parallel manipulators are characterized by high accuracy and dynamic performance, which makes them suitable for industrial high-precision assembly tasks such as placement of electronic THT components on printed circuit boards. In this paper we describe an assembly system that comprises a Hexaglide manipulator with vertical ball screws, moving printed circuit boards relative to stationary THT components. We evaluate the effects of the manufacturing tolerances of machine parts, such as bar length tolerance, ball screw axis position uncertainty, and ball screw axis orientation uncertainty, on Hexaglide end-effector pose accuracy using a geometric simulation study based on stochastic tolerance sampling. In the investigated configuration and under standard industrial tolerances, bar length inaccuracy and axis position uncertainty lead to significant position and rotation deviations for the Hexaglide end-effector in the horizontal plane that need to be compensated for by control algorithms to enable THT assembly using the Hexaglide prototype. The geometric simulation method applied in this paper can be used by designers of Hexaglide machines to study and evaluate different machine configurations. Full article

In printed circuit board (PCB) assembly, the volume of dispensed glue is closely related to the PCB’s durability, production costs, and the overall product reliability. Currently, quality inspection is performed manually by operators, inheriting the limitations of human-performed procedures. To address this, we propose an automatic optical inspection framework that utilizes convolutional neural networks (CNNs) and post-hoc explainable methods. Our methodology handles glue quality inspection as a three-fold procedure. Initially, a detection system based on CenterNet MobileNetV2 is developed to localize PCBs, thus, offering a flexible lightweight tool for targeting and cropping regions of interest. Consequently, a CNN is proposed to classify PCB images into three classes based on the placed glue volume achieving 92.2% accuracy. This classification step ensures that varying glue volumes are accurately assessed, addressing potential quality issues that appear early in the production process. Finally, the Deep SHAP and Grad-CAM methods are applied to the CNN classifier to produce explanations of the decision making and further increase the interpretability of the proposed approach, targeting human-centered artificial intelligence. These post-hoc explainable methods provide visual explanations of the model’s decision-making process, offering insights into which features and regions contribute to each classification decision. The proposed method is validated with real industrial data, demonstrating its practical applicability and robustness. The evaluation procedure indicates that the proposed framework offers increased accuracy, low latency, and high-quality visual explanations, thereby strengthening quality assurance in PCB manufacturing. Full article

This paper presents the first demonstration and comparison of two identical oscillator circuits employing piezoelectric zinc oxide (ZnO) microelectromechanical systems (MEMS) resonators, implemented on conventional printed-circuit-board (PCB) and three-dimensional (3D)-printed acrylonitrile butadiene styrene (ABS) substrates. Both oscillators operate simultaneously at dual frequencies (260 MHz and 437 MHz) without the need for additional circuitry. The MEMS resonators, fabricated on silicon-on-insulator (SOI) wafers, exhibit high-quality factors (Q), ensuring superior phase noise performance. Experimental results indicate that the oscillator packaged using 3D-printed chip-carrier assembly achieves a 2–3 dB improvement in phase noise compared to the PCB-based oscillator, attributed to the ABS substrate’s lower dielectric loss and reduced parasitic effects at radio frequency (RF). Specifically, phase noise values between −84 and −77 dBc/Hz at 1 kHz offset and a noise floor of −163 dBc/Hz at far-from-carrier offset were achieved. Additionally, the 3D-printed ABS-based oscillator delivers notably higher output power (4.575 dBm at 260 MHz and 0.147 dBm at 437 MHz). To facilitate modular characterization, advanced packaging techniques leveraging precise 3D-printed encapsulation with sub-100 μm lateral interconnects were employed. These ensured robust packaging integrity without compromising oscillator performance. Furthermore, a comparison between two transistor technologies—a silicon germanium (SiGe) heterojunction bipolar transistor (HBT) and an enhancement-mode pseudomorphic high-electron-mobility transistor (E-pHEMT)—demonstrated that SiGe HBT transistors provide superior phase noise characteristics at close-to-carrier offset frequencies, with a significant 11 dB improvement observed at 1 kHz offset. These results highlight the promising potential of 3D-printed chip-carrier packaging techniques in high-performance MEMS oscillator applications. Full article

Printed circuit board (PCB) assembly, on the basis of surface mount electronic component welding, is one of the most important electronic assembly processes, and its defect detection is also an important part of industrial generation. The traditional two-stage target detection algorithm model has a large number of parameters and the runtime is too long. The single-stage target detection algorithm has a faster running time, but the detection accuracy needs to be improved. To solve this problem, we innovated and modified the YOLO11n model. Firstly, we used the Retention Block (RetBlock) to improve the C3K2 module in the backbone, creating the RetC3K2 module, which makes up for the limitation of the original module’s limited, purely convolutional local receptive field. Secondly, the neck structure of the original model network is fused with a Multi-Branch Auxiliary Feature Pyramid Network (MAFPN) structure and turned into a multi-branch auxiliary neck network, which enhances the model’s ability to fuse multiple scaled characteristics and conveys diverse information about the gradient for the output layer. The improved YOLO11n model improves its mAP50 by 0.023 (2.5%) and mAP75 by 0.026 (2.8%) in comparison with the primitive model network, and detection precision is significantly improved, proving the superiority of our proposed approach. Full article

The growing complexities, power densities, and cooling demands of modern electronic systems and batteries—such as three-dimensional integrated circuit chip packaging, printed circuit board assemblies, and electronics enclosures—have pushed the urgency for efficient and dynamic thermal management strategies. Traditional numerical methods like computational fluid dynamics (CFD) and the finite element method (FEM) are computationally impractical for large-scale or real-time thermal analysis, especially when dealing with complex geometries, temperature-dependent material properties, and rapidly changing boundary conditions. These approaches typically require extensive meshing and repeated simulations for each new scenario, making them inefficient for design exploration or optimization tasks. Physics-informed neural networks (PINNs) emerge as a powerful alternative approach that incorporates physical principles such as mass and energy conservation equations into deep learning models. This approach delivers rapid and adaptable resolutions to the partial differential equations that govern heat transfer and fluid dynamics. This review examines the basic principle of PINN and its role in thermal management for electronics and batteries, from the small unit scale to the system scale. We highlight recent advancements in PINNs, particularly their superior performance compared to traditional CFD methods. For example, studies have shown that PINNs can be up to 300,000 times faster than conventional CFD solvers, with temperature prediction differences of less than 0.1 K in chip thermal models. Beyond speed, we explore the potential of PINNs in enabling efficient design space exploration and predicting outcomes for previously unseen scenarios. However, challenges such as training convergence in fine-grained or large-scale applications remain. Notably, research combining PINNs with LSTM networks for battery thermal management at a 2.0 C charging rate has achieved impressive results—an R² of 0.9863, a mean absolute error (MAE) of 0.2875 °C, and a root mean square error (RMSE) of 0.3306 °C—demonstrating high predictive accuracy. Finally, we propose future research directions that emphasize the integration of PINNs with advanced hardware and hybrid modeling techniques to advance thermal management solutions for next-generation electronics and battery systems. Full article

This study presents a novel approach to manufacture a rigid printed circuit board (PCB) using sustainable polymers. Current PCBs use a fossil-fuel-based substrate, like FR4. This presents recycling challenges due to its composite nature. Replacing the substrate with an environmentally friendly alternative leads to a reduction in negative impacts. Polylactic acid (PLA) and Polyhydroxybutyrate (PHB) biopolymers are used in this study. These two biopolymers have low melting points (130–180 °C, and 170–180 °C, respectively) and cannot withstand the high temperature soldering process (up to 260 °C for standard SAC (SnAgCu, tin/silver/copper) lead free solder processes). Our approach for replacing the PCB substrate is applying the PLA/PHB carrier substrate at the end of the PCB manufacturing process using injection molding technology. This approach involves all the standard PCB processes, including wet etching of the Cu conductors, and component assembly with SAC solder on a thin flexible polyimide (PI) foil with patterned Cu conductors and then overmolding the biopolymer onto the foil to create a rigid base. This study demonstrates the functionality of two test circuits fabricated using this method. In addition, we evaluated the adhesion between the biopolymer and PI to achieve a durable PCB. Moreover, we performed two different end-of-life approaches (debonding and composting) as a part of the end-of-life consideration. By incorporating biodegradable materials into PCB standard manufacturing, the CO₂ emissions and energy consumption are significantly reduced, and installation costs are lowered. Full article

The Printed Circuit Board (PCB) manufacturing industry is a rapidly expanding sector, fueled by advanced technologies and precision-oriented production processes. The placement of Surface-Mount Device (SMD) components in PCB assembly is efficiently automated using robots and design software-generated coordinate files; however, the PCB repair process remains significantly more complex and challenging. Repairing faulty PCBs, particularly replacing defective SMD components, requires high precision and significant manual expertise, making automated solutions both rare and difficult to implement. This study introduces a novel real-time machine vision-based coordinate estimation system designed for estimating the coordinates of SMD components during soldering or desoldering tasks. The system was specifically designed for Selective Compliance Articulated Robot Arm (SCARA) robots to overcome the challenges of repairing miniature PCB components. The proposed system integrates Image-Based Visual Servoing (IBVS) for precise X and Y coordinate estimation and a simplified laser triangulation method for Z-axis depth estimation. The system demonstrated accuracy rates of 98% for X and Y axes and 99% for the Z axis, coupled with high operational speed. The developed solution highlights the potential for automating PCB repair processes by enabling SCARA robots to execute precise picking and placement tasks. When equipped with a hot-air gun as the end-effector, the system could enable automated soldering and desoldering, effectively replacing faulty SMD components without human intervention. This advancement has the potential to bridge a critical gap in the PCB repair industry, improving efficiency and reducing dependence on manual expertise. Full article

Soldering of printed circuit board (PCB)-based surface-mounted assemblies is a critical process, and to enhance the accuracy of detecting their multi-targeted soldering defects, we propose an automated sample generation method that combines ControlNet and a Stable Diffusion Model. This method can expand the dataset by quickly obtaining sample images with high quality containing both defects and normal detection targets. Meanwhile, we propose the Cascade Mask R-CNN model with ConvNeXt as the backbone, which performs well in dealing with multi-target defect detection tasks. Unlike previous detection methods that can only detect a single component, it can detect all components in the region. The results of the experiment demonstrate that the detection accuracy of our proposed approach is significantly enhanced over the previous convolutional neural network model, with an increase of more than 10.5% in the mean accuracy precision (mAP) and 9.5% in the average recall (AR). Full article

The rapid and timely evaluation of the mental health of emergency rescuers can effectively improve the quality of emergency rescues. However, biosensors for mental health evaluation are now facing challenges, such as the rapid and portable detection of multiple mental biomarkers. In this study, a non-invasive, flexible, wearable electrochemical biosensor was constructed based on the self-assembly of nanomagnetic beads for the rapid detection of cortisol in interstitial fluid (ISF) to assess the mental stress of emergency rescuers. Based on a one-step reduction, gold nanoparticles (AuNPs) were functionally modified on a screen-printed electrode to improve the detection of electrochemical properties. Afterwards, nanocomposites of MXene and multi-wall carbon nanotubes were coated onto the AuNPs layer through a physical deposition to enhance the electron transfer rate. The carboxylated nanomagnetic beads immobilized with a cortisol antibody were treated as sensing elements for the specific recognition of the mental stress marker, cortisol. With the rapid attraction of magnets to nanomagnetic beads, the sensing element can be rapidly replaced on the electrode uniformly, which can lead to extreme improvements in detection efficiency. The detected linear response to cortisol was 0–32 ng/mL. With the integrated reverse iontophoresis technique on a flexible printed circuit board, the ISF can be extracted non-invasively for wearable cortisol detection. The stimulating current was set to be under 1 mA for the extraction, which was within the safe and acceptable range for human bodies. Therefore, based on the positive correlation between cortisol concentration and mental stress, the mental stress of emergency rescuers can be evaluated, which will provide feedback on the psychological statuses of rescuers and effectively improve rescuer safety and rescue efficiency. Full article

Technological advances in the production of printed circuit boards (PCBs) are increasing the number of components inserted on the surface. This has led the electronics industry to seek improvements in their inspection processes, often making it necessary to increase the level of automation on the production line. The use of machine vision for quality inspection within manufacturing processes has increasingly supported decision making in the approval or rejection of products outside of the established quality standards. This study proposes a hybrid smart-vision inspection system with a machine vision concept and vision sensor equipment to verify 24 components and eight screw threads. The goal of this study is to increase automated inspection reliability and reduce non-conformity rates in the manufacturing process on the assembly line of automotive products using machine vision. The system uses a camera to collect real-time images of the assembly fixtures, which are connected to a CMOS color vision sensor. The method is highly accurate in complex industry environments and exhibits specific feasibility and effectiveness. The results indicate high performance in the failure mode defined during this study, obtaining the best inspection performance through a strategy using Vision Builder for automated inspection. This approach reduced the action priority by improving the failure mode and effect analysis (FMEA) method. Full article

In this study, cooling methods for a typical spacecraft circuit board assembly are investigated. The power dissipation of the assembly is more than 100 W, and the max heat dissipation of a component is 16 W, making it very difficult to cool the assembly. According to the packaging characteristics and heat dissipation of the components on the circuit board, cooling methods such as potting brackets, cooling springs, and cooling blocks are used, and the effects of various cooling methods are analyzed. Through simulation and experimental research, it is proven that the power components in the printed circuit board assembly meet the requirements of temperature derating, which provides a reference for the thermal design of spacecraft electronic equipment. Full article

Cardiac mapping is a crucial procedure for diagnosing and treating cardiac arrhythmias. Still, current clinical techniques face limitations including insufficient electrode coverage, poor conformability to complex heart chamber geometries, and high costs. This study explores the design, testing, and validation of a 64-electrode soft robotic catheter that addresses these challenges in cardiac mapping. A dual-layer flexible printed circuit board (PCB) was designed and integrated with sensors into a soft robotic sensor array (SRSA) assembly. Design considerations included flex PCB layout, routing, integration, conformity to heart chambers, sensor placement, and catheter durability. Rigorous SRSA in vitro testing evaluated the burst/leakage pressure, block force for electrode contact, mechanical integrity, and environmental resilience. For in vivo validation, a porcine model was used to demonstrate the successful deployment, conformability, and acquisition of electrograms in both the ventricles and atria. This catheter-deployable SRSA represents a meaningful step towards translating the integration of soft robotic actuators and stretchable electronics for clinical use, showcasing the unique mechanical and electrical performance that these designs enable. The high-density electrode array enabled rapid 2 s data acquisition with detailed spatial and temporal resolution, as illustrated by the clear and consistent cardiac signals recorded across all electrodes. The future of this work will lie in enabling high-density, anatomically conformable devices for detailed cardiac mapping to guide ablation therapy and other interventions. Full article

Visualizing magnetic fields is essential for studying the operation of electromagnetic systems and devices that use permanent magnets or magnetic particles. However, commercial devices for this purpose are often expensive due to their complex designs, which may not always be necessary for specific research needs. This work presents a method for designing an automated laboratory setup for magnetic cartography, utilizing a 3D printer to produce structural plastic components for the scanner. The assembly process is thoroughly described, covering both the hardware and software aspects. Spatial resolution and mapping parameters, such as the number of data points and the collection time, were configured through software. Multiple tests were conducted on samples featuring flat inductive coils on a printed circuit board, providing a reliable model for comparing calculated and measured results. The scanner offers several advantages, including a straightforward design, readily available materials and components, a large scanning area (100 mm × 100 mm × 100 mm), a user-friendly interface, and adaptability for specific tasks. Additionally, the integration of a pre-built macro enables connection to any PC running Windows, while the open-source microcontroller code allows users to customize the scanner’s functionality to meet their specific requirements. Full article

The balance scheduling of Printed Circuit Board (PCB) assembly lines plays a crucial role in enhancing production efficiency. Traditional scheduling methods rely on fixed heuristic rules, which lack flexibility and adaptability to changing production demands. To address this issue, this paper proposes a PCB assembly line scheduling method based on Deep Q-Network (DQN). The PCB assembly line model is constructed using the FlexSim simulation tool, and the optimal scheduling strategy is learned through the DQN algorithm. Comparative analysis is conducted against traditional heuristic rules. Experimental results indicate that the DQN-based scheduling method achieves substantial improvements in balance and production efficiency. For instance 1, the DQN approach achieved a total completion time (S) of 2.521 × 10⁵, compared to the best heuristic rule result of 2.541 × 10⁵. Similarly, for instance 2 and instance 3, the DQN method achieved total completion times of 2.549 × 10⁵ and 2.522 × 10⁵, respectively, outperforming all heuristic rules evaluated. This study provides a novel approach and method for intelligent scheduling of PCB assembly lines. Full article