The practical application of Raman spectroscopy is often constrained by its low signal sensitivity, particularly for low-concentration liquid samples. This study introduces a straightforward platform that enhances Raman signals by physically concentrating analytes, providing an alternative to complex substrate fabrication and chemical treatments. We employed a femtosecond pulse laser to create functional micro-grid patterns on a silicon (Si) substrate. This laser process induces localized ablation and simultaneous oxidation, resulting in three-dimensional, hydrophilic microstructures of nonstoichiometric silicon oxide (SiO_2-x). These grid structures effectively confine aqueous sample droplets through a pinning effect, functioning as a microwell array that traps and concentrates suspended polystyrene (PS) particles. This physical concentration mechanism achieved a notable signal enhancement, with a maximum factor of 5.2 for PS particles, without the need for sample dehydration. This work presents a simple, cost-effective, and highly reproducible alternative to conventional SERS for analyzing low-concentration liquid samples, demonstrating strong potential for integration into microfluidic systems.

The design of the extrusion die significantly affects both the extrusion process and the quality of multi-lumen tubes. Traditional design methods that rely on trial and error tend to increase manufacturing time and costs while diminishing product quality. This study utilizes inverse extrusion simulation and optimization to design the extrusion die without the need for trial and error. The inverse extrusion simulation generates the die profile necessary to achieve the desired extrudate shape. Subsequently, direct extrusion simulations are conducted to predict the extrudate profile based on the derived die. The optimal volumetric flow rates of air within the lumens are also identified to ensure the extrudate meets the target profile. The results from the direct extrusion simulation, combined with optimization, confirm that the designed extrusion die can successfully produce the target profile. Using the derived die, the multi-lumen tube with the desired specifications is successfully extruded. This design and manufacturing approach enhances both the quality and productivity of multi-lumen tubes.

In the field of gimbal targeting systems, image error tracking plays a crucial role in various applications, including object detection, enemy surveillance, and aircraft inspection. Enhancing image tracking performance presents a significant challenge due to singularity issues at the azimuth and elevation joints. To tackle this problem, this paper proposes a rotation-matrix-based tracking error compensation method centered on real-time object tracking. Specifically, our approach involves creating a virtual rotation frame that aligns the visual tracking frame with the gimbal base frame. Using our method, a gimbal with two degrees of freedom (DOF) can achieve superior tracking performance near the ±90° joint positions compared to conventional gimbal tracking methods. We compare the proposed method with existing approaches in the literature by assessing initial pose RMS error and singular pose RMS error through MATLAB Simscape simulations. The experimental results demonstrate that our method can reduce the line of sight RMS error by 89% in the azimuth position and by 99% in the elevation position, respectively.

Military shelters contain various electronic devices that generate significant heat during operation due to their high power output. This heat buildup can degrade the performance of the equipment and shorten its operational lifespan. In high-temperature environments, overheating can lead to serious malfunctions in communication systems or information management platforms, jeopardizing the efficiency and reliability of military operations. Conversely, in low-temperature or high-humidity conditions, condensation may form inside the shelter, increasing the risk of physical damage to electronic components. Such damage can significantly compromise the reliability and durability of the equipment, raising the likelihood of system failure. This study proposes using various environmental control systems, including heating, ventilation, and air conditioning (HVAC) units and air ducts, to mitigate the adverse effects of temperature and humidity fluctuations within military shelters. To achieve this, thermal analysis models were utilized to evaluate and verify the performance of these systems. The analysis specifically examined the heat output of individual devices to determine if the proposed control systems could effectively maintain optimal operating temperatures within the shelter. The results of this study aim to provide a valuable foundation for designing environmental control systems that ensure thermal stability in military shelters.

This paper presents a tiltable cable-suspended aerial manipulation (SAM) system designed to improve the utility of aerial manipulators in industrial settings. Although drone-robot arm systems have shown promise, suspended configurations encounter notable stability challenges, particularly during inclined operations. To tackle these challenges, we performed simulation-based analyses focusing on the system's kinematics, dynamic response, and thrust requirements under tilted conditions. We utilized Monte Carlo sampling and forward kinematics to assess the workspace and manipulability. The findings indicated that each propeller needs to generate over 32 N of thrust to maintain stable control. Additionally, simulation experiments showed that the system can uphold its attitude and execute end-effector motions effectively, even in the presence of disturbances. This study establishes a foundational verification step toward developing a physical SAM system capable of safe and robust operation in inclined scenarios.

Flexible electronics are becoming the next generation of devices due to their advantages, such as mechanical flexibility, eco-friendliness, large-area applicability, and scalability for mass production. However, solution-based manufacturing processes are prone to defects like discontinuities and local smudging, which can significantly degrade both device quality and yield. To tackle these challenges, rapid and accurate defect classification is crucial for real-time diagnosis during manufacturing. This study investigates the impact of data scale and key training hyperparameters on the performance of deep learning–based defect diagnosis models, using a dataset of conductive pattern defects in flexible electronics. We specifically examine how the number of training images affects model accuracy and generalization, and we analyze how adjustments to hyperparameters—such as L2 regularization and dropout—influence model performance in data-limited scenarios. Our findings offer insights into optimal training strategies tailored to different data scales and learning constraints, providing practical guidelines for designing and developing AI-based defect diagnosis models for flexible electronic devices.

This study examines a 2kW photovoltaic (PV) support structure, highlighting the vulnerability of conventional metal frames to corrosion and strength degradation in harsh environmental conditions. To overcome these challenges, we propose using pultruded fiber-reinforced polymer (PFRP) members as an alternative structural material. An optimal design framework is established to identify efficient PFRP cross-sections. The study aims to determine lightweight cross-sectional dimensions for box sections (columns and girders) and C-sections (purlins) while maintaining structural safety. We evaluate structural performance using the allowable stress design (ASD) method, incorporating safety factors recommended by the American Association of State Highway and Transportation Officials (AASHTO). Finite element analysis (FEA) assesses critical design constraints, including buckling, material failure, and serviceability deflection limits. From the feasible designs, we select the lightest cross-sectional configuration that meets all safety requirements. The results demonstrate that PFRP members can significantly reduce weight while ensuring structural safety, thus validating their potential as an alternative to conventional metal photovoltaic support structures.

Digital twin technologies in manufacturing have evolved into dynamic, data-synchronized systems that facilitate real-time monitoring and control. Given that machining involves closely interconnected multi-physics behaviors, the effectiveness of a digital twin largely relies on the accuracy and reliability of its underlying process models. This review systematically evaluates three primary paradigms for machining process modeling in digital twins: physics-based, data-driven, and hybrid approaches. Physics-based models provide interpretability and physical consistency but are hindered by high computational costs and limited adaptability to changing conditions. In contrast, data-driven models offer real-time capabilities and adaptive learning but face challenges related to data scarcity and black-box behavior. Hybrid modeling has emerged as the most promising approach, combining physical laws with machine learning through techniques such as parameter correction, physics-guided learning, and state-estimation-based intelligent control. Recent research demonstrates significant advancements in predictive performance, adaptability, and computational efficiency across various machining applications, underscoring the effectiveness of new process modeling strategies for digital twins. However, challenges remain, including multi-physics integration, model reduction for real-time deployment, and autonomous self-updating in data-limited scenarios. The review concludes that hybrid models present the most viable pathway to achieving high-fidelity, self-adaptive, and trustworthy digital twins for autonomous manufacturing.

This paper examines the role of generative AI and large language models (LLMs) in advancing intelligent manufacturing as we transition from Industry 4.0 to Industry 5.0. We begin by analyzing the current limitations of rule-based and manufacturing data systems in facilitating flexible, human-centric production. Next, we categorize LLM utilization strategies into three methodological axes: fine-tuning domain-specific models, employing general-purpose models through prompt engineering, and utilizing retrieval-augmented generation (RAG), which includes multimodal RAG that integrates sensor and text data. For each strategy, we present representative case studies across key application areas such as asset management, maintenance intelligence, quality control, process optimization, and knowledge- and document-centric support systems. Concurrently, we explore how information modeling and ontology-based knowledge graphs can be integrated with LLMs to enhance structured manufacturing semantics, improve source traceability, and minimize hallucinations. Finally, we summarize the advantages and limitations of each approach and propose future research directions for human-centric manufacturing, including the development of trustworthy LLM pipelines, standardized data schemas, and closer integration between digital twins and LLM-based decision support systems.

This study examines the porosity behavior during the directed energy deposition (DED) of dissimilar metals S45C and H13. We analyzed the effects of deposition parameters, including laser power, feed rate, and powder characteristics, on pore formation, taking into account the unique properties of these metals. Our findings indicate that laser power is the primary factor influencing porosity. At a low power of 200 W, insufficient energy input, along with differences in thermal conductivity and chemical composition between S45C and H13, led to incomplete melting and lack-of-fusion, resulting in high porosity. As the laser power increased to 400-600 W, the melt pool stabilized, enhancing interfacial bonding and significantly reducing porosity. However, at an excessive power of 800 W, rapid melting and solidification of the powder caused gas entrapment and pore formation, which increased porosity, particularly due to the differing thermal conductivities of S45C and H13. Therefore, our results suggest that maintaining an adequate laser power of 400-600 W is essential for achieving a stable melt pool and minimizing porosity in the DED process for dissimilar S45C and H13 metals.

Robots are increasingly utilized in manufacturing and logistics, where bin-picking has become crucial for managing randomly placed objects. However, traditional methods often rely on expensive 3D vision systems, have limited adaptability to unstructured environments, and primarily focus on the picking process, neglecting the placing tasks. To address these challenges, this study presents a cost-effective system that combines a depth camera, YOLO-based instance segmentation, and optimization-based inverse kinematics for real-time object detection and stable manipulation. In the placing stage, an adaptive algorithm detects empty tray holes and generates grid patterns, ensuring reliable placement even in the presence of tray misalignments, occupied slots, or partial occlusions. Experimental validation revealed a 91% success rate in mixed-object environments during picking tasks and a 94% success rate for placing tasks, even with tray displacement and occlusion conditions. The results demonstrate that the system maintains stable performance across both picking and placing processes while minimizing reliance on expensive hardware and complex initial setups. By enhancing flexibility and scalability, the proposed approach offers a practical solution for intelligent automation and can serve as a foundation for broader applications in assembly, logistics, and service robotics.

Cable chains are essential in the semiconductor industry for preventing the twisting or sagging of moving cables. They can be broadly categorized into two types based on their fastening methods, with rivet-based assembly being the most common. An alternative method utilizes integral locking features without rivets, which simplifies manufacturing and reduces production costs. However, integral cable chains are more susceptible to breakage during assembly, limiting their use in various industrial environments.This study introduces a structural design approach aimed at minimizing localized stress during assembly while ensuring the cable chain meets the required retention force. Design variables were selected from the modifiable features of the integral cable chain. Through sensitivity analysis, we identified key variables that significantly influence the retention force, which allowed us to reduce the number of design iterations. By employing finite element analysis and response surface methodology, we derived an optimal shape that achieved the target pull-out force and resulted in a 9.7% reduction in assembly stress compared to the original design.

Carbon nanotubes (CNTs) are popular in strain sensors due to their exceptional electrical conductivity, flexibility, and sensitivity to deformation. In this study, a high-sensitivity strain sensor was fabricated by spray-coating CNT ink onto various paper substrates, with “lint-free paper” identified as the optimal choice. A total of 10 spray cycles ensured a reliable conductive coating. To enhance durability and broaden application potential, a PET protective layer was incorporated. The sensor's performance was assessed through bending tests using a push-pull gauge across a strain range of 0-2%. The lintfree paper-based sensor exhibited a consistent response up to 1.4% strain. The measured gauge factors (GF) were 121.370 in the 0-0.3% range, 70.999 in the 0.3-0.8% range, and 20.935 in the 0.8-1.4% range. A precise response was also noted when adjusting the bending angle in 1° increments, particularly within the 0-20° range. Additionally, the sensor was tested on the human wrist, confirming its viability for wearable applications. These findings indicate that the lint-free paper-based CNT strain sensor offers high sensitivity and measurement precision within narrow strain ranges. Its lightweight structure and flexible design suggest strong potential for practical use in areas such as sports monitoring and human motion detection.

The future mobility industry is increasingly utilizing advanced tools for cutting and machining lightweight parts to enhance the fuel efficiency of automotive engines. Machining companies are turning to polycrystalline diamond (PCD) tools to boost productivity in the production of these lightweight components. PCD tools provide exceptional machining performance and a long service life, making them ideal for high-mix, low-volume production, which often involves customized requirements for various materials. To further improve efficiency, this study explores the application of metal 3D printing technology in the manufacturing of PCD tools. This technology allows for the creation of PCD tools with superior cutting performance and wear resistance, tailored for high-speed machining of lightweight materials, including complex shapes. Thus, research into this area is essential. In this study, we manufactured boring tools by brazing PCD tips onto three different laminated structures created using Fused Deposition Modeling (FDM), a method within metal 3D printing technologies. We then evaluated the fabricated boring tools through comparative machining experiments against existing sintered PCD boring tools. The results indicated that the 3D-printed solid tools demonstrated no significant differences in machining accuracy or surface quality compared to the conventional tools.

This paper details the design and development of a robotic joint actuator that combines a frameless BLDC motor with a two-stage stepped planetary gear reducer, as well as a custom-built controller for precise position control. The rotor is physically coupled to a hollow sun gear shaft to facilitate internal cable routing, and the actuator features a high-resolution absolute encoder utilizing the BiSS-C protocol. The controller includes a 3-phase H-bridge driver, differential signal conversion for encoder communication, and a CAN interface for host communication. Position control is achieved through a PID loop operating at 1 kHz. A prototype actuator and controller have been fabricated, and step response tests were conducted. Experimental results indicate stable and accurate tracking of position commands, with a short settling time of 0.04773 seconds. These findings confirm the effectiveness of the integrated actuator system for robotic joint applications. Future work will focus on optimizing internal cable space and implementing sensorless control algorithms.