Department of Mechanical Engineering

The Fraunhofer Innovation Platform for Composites Research@UNIST (FIP-Composites@UNIST) has secured a third five-year term from Germany's Fraunhofer-Gesellschaft following a successful review by Fraunhofer headquarters. The renewal recognizes the center's contributions to composite materials research, industry engagement, and international cooperation, and will support expanded research and training activities over the next five years. Established in 2016 through a partnership between UNIST and the Fraunhofer Institute for Chemical Technology (Fraunhofer ICT), FIP-Composites@UNIST conducts research in lightweight structures, advanced manufacturing, composite recycling, structural health monitoring, and AI-enabled predictive maintenance. Professor Young-Bin Park from the Department of Mechanical Engineering at UNIST serves as director of the center. During its first two phases (2016–2025), FIP-Composites@UNIST established dedicated research facilities and advanced manufacturing and testing capabilities while supporting international research projects, researcher exchanges, and technology development for the Korean industry. Through its partnership with Fraunhofer ICT, the center has built an international research network in composite materials and helped connect Korean companies with advanced manufacturing expertise from Germany. These achievements contributed to the center's successful evaluation by Fraunhofer headquarters and the approval of a third five-year term. Led by Professor Young-Bin Park of the Department of Mechanical Engineering, the center will continue advancing research in composite materials and manufacturing technologies while strengthening industry partnerships and expanding training opportunities for students and researchers. The renewal coincided with the recent FIP in Korea Networking program, which brought together representatives from Fraunhofer headquarters, German research institutes, and Korea-based Fraunhofer Innovation Platforms. UNIST hosted the program's second day, where participants discussed ways to expand cooperation across Fraunhofer's Korean network. “UNIST has demonstrated strong achievements in international research cooperation,” said Frauke Mickler, Program Manager for International Research Collaboration at Fraunhofer-Gesellschaft. “We hope FIP-Composites@UNIST will continue to serve as a leading model for Fraunhofer's international partnerships worldwide.” UNIST President Chong Rae Park welcomed the renewal and reaffirmed the university's commitment to the partnership. "This renewal reflects the strength of the partnership that UNIST and Fraunhofer have built together,” said President Park. “We look forward to expanding joint research and creating new opportunities for researchers, students, and industry partners in the years ahead.

Abstract Despite the advantages of additive manufacturing (AM) in creating customized 3D shapes, conventional layer-by-layer approaches are limited by low production rates, restricting their broader applications. Volumetric additive manufacturing (VAM) has emerged as a promising technique, enabling the simultaneous photopolymerization of entire volumes, which significantly reduces fabrication time. However, current computed axial lithography requires manual operations per print cycle, such as loading resin into a vial, physically placing and aligning the vial (with or without an index-matching medium), and removing the printed object, limiting continuous, high-throughput production of multiple parts. Here, we propose a dispensing volumetric additive manufacturing (DVAM) method that prints and dispenses each part within a droplet in less than a minute. The printing process occurs within a single droplet dispensed from a glass pipette, enabling simultaneous printed object removal and resin replenishment in a second. Light pattern distortion caused by the absence of the index-matching fluid was corrected through real-time droplet profile estimation and inverse ray-tracing within the optical system. We demonstrate rapid serial VAM of 10 different objects within 10 min. This approach establishes a practical pathway toward scalable, high-throughput volumetric manufacturing, enabling rapid production of complex 3D structures without the operational bottlenecks of conventional VAM workflows. A research team affiliated with UNIST has achieved a major breakthrough in 3D microfabrication, enabling the rapid production of intricate 3D structures within a single minute. The new technology promises to significantly accelerate manufacturing processes in fields, ranging from biomedical devices to microelectronics. Led by Professor Im Doo Jung from the Department of Mechanical Engineering at UNIST, the research team developed a cutting-edge dispensing volumetric additive manufacturing (DVAM) system.The new approach moves beyond traditional layer-by-layer methods, allowing for the swift, continuous fabrication of diverse three-dimensional shapes within a matter of minutes by employing a volumetric printing process. DVAM utilizes a fine resin droplet dispensed from a glass pipette as the entire build volume. Light is projected onto the droplet to cure the desired shape in real-time. Once a structure is solidified, compressed air expels the finished object, and a new droplet is immediately dispensed for the next cycle. This seamless process enables high-speed, continuous production without the need for post-processing or layer-by-layer assembly. Unlike conventional volumetric printing methods—such as Computed Axial Lithography (CAL)—which require filling a resin container, matching refractive indices with immersion fluids, and manually removing finished parts, the UNIST team’s approach eliminates these constraints. By performing printing within a single resin droplet that acts as the entire volume, they achieve rapid, uninterrupted manufacturing. One of the main challenges was optical distortion caused by the droplet’s curved surface. To overcome this, the researchers integrated artificial intelligence with inverse ray-tracing optical calculations. A deep learning-based AI accurately detects the droplet’s profile in real-time, enabling the system to mathematically correct for distortions and project precise light patterns for uniform curing. This advanced system successfully produced intricate structures, such as the Eiffel Tower and The Thinker, within just 10 minutes—demonstrating the ability to fabricate ten different objects with an average time of around 60 seconds each. Hongryung Jeon, the first author of the study, explained, “Unlike traditional 3D printing that builds objects layer by layer, our method cures the entire volume at once and continuously dispenses resin without additional post-processing, increasing speed by over 100 times. This opens up exciting possibilities for large-scale, rapid production of micro-scale components.” Professor Jung, the corresponding author, emphasized, “Speed has long been a limiting factor in 3D printing, especially for customized manufacturing. By moving away from conventional photopolymerization and employing artificial intelligence to compensate for optical distortions, we have significantly advanced the potential of ultrafast volumetric fabrication. Now, complex shapes can be produced in seconds—eliminating long wait times.” The findings of this research have been published online in Advanced Functional Materials (Impact Factor: 19.0, top 5% in JCR) on March 21, 2026. The project was supported by the National Research Foundation of Korea (NRF), the Ministry of Science and ICT (MSIT), the Institute for Information & communications Technology Planning & Evaluation (IITP), and the Ministry of Trade, Industry & Energy (MOTIE). Journal Reference Hongryung Jeon, Yunsoo Lee, Seobin Park, et al., "Dispensing Volumetric Additive Manufacturing," Adv. Funct. Mater., (2026).

UNIST has secured a government-funded project to establish a testing and validation center for AI-based ship equipment and advanced components, marking a major step toward advancing Korea’s shipbuilding industry. The project, supported by the Ministry of Trade, Industry and Energy (MOTIE), was selected under a 2026 government initiative to strengthen infrastructure in the shipbuilding and offshore sector. With a total budget of KRW 38.2 billion, it will support the development and validation of AI-driven autonomous ship systems and advanced composite materials in response to tightening environmental regulations led by the International Maritime Organization (IMO), as well as evolving industry demands. Scheduled for completion by 2030, the center will be located in the Ulsan Mipo National Industrial Complex. UNIST will oversee its construction and operation in collaboration with the Ulsan Metropolitan Government, with participation from Ulsan Technopark, the Korea Testing & Research Institute (KTR), and the Korea Research Institute of Ships and Ocean Engineering (KRISO). Designed as an end-to-end validation platform, the facility will support the full development cycle—from design and prototyping to performance testing and certification readiness. In the advanced components domain, it will enable the validation of fiber-reinforced plastic (FRP)-based ship structures, exterior materials, and propulsion components under real-world operating conditions, including mechanical stress, corrosion, and cryogenic environments. The center will also incorporate AI- and digital twin-based manufacturing systems to enhance process optimization, quality control, and productivity. For AI-based ship systems, the center will establish a hybrid validation environment combining simulation and field testing, supporting the evaluation of autonomous navigation algorithms, sensor modules, and control systems in terms of safety, reliability, and repeatability, and accelerating commercialization. The center is expected to play a key role in supporting small and mid-sized shipbuilding suppliers by improving access to testing infrastructure and validation data, thereby strengthening industrial competitiveness and facilitating entry into global markets. Professor Young-Bin Park of the Department of Mechanical Engineering at UNIST, who leads the project, said, “This initiative will help bridge the gap between technology development and commercialization, supporting the advancement of environmentally sustainable and intelligent shipbuilding."

Abstract Contact lenses are emerging as strong candidates for next-generation extended reality (XR) interfaces due to their lightweight and ergonomic form factor. However, integrating photodetector arrays onto the limited area of a lens remains challenging with conventional micropatterning approaches, which rely on masks, multistep processes, and specialized equipment that inherently limit throughput and scalability. To address these constraints, we introduce a Meniscus Pixel Printing (MPP) strategy that enables rapid, mask-free patterning of MAPbI3 perovskite photodetectors without costly or complex fabrication tools. MPP uses a self-confined meniscus at a pipette tip to deterministically transfer perovskite ink, enabling 200 µm pixels to be printed within 1 s per pixel. In addition to planar substrates, MPP demonstrates stable pixel patterning on curved surfaces, highlighting its geometric adaptability and process versatility. Using this approach, we fabricate a 10 × 10 perovskite photodetector array and demonstrate stable photoresponse, retaining 92% of its initial performance after two months of storage. To overcome limited pixel density, a deep-learning-based super-resolution (SR) model reconstructs 10 × 10 inputs into 80 × 80 optical information with 97.2% accuracy and 0.03 s latency. Additionally, an AI-based eye-tracking system recognizes nine eye gestures with 99.3% accuracy, enabling smooth hands-free robotic arm control. A research team, led by Professor Im Doo Jung from the Department of Mechanical Engineering at UNIST, has developed a groundbreaking smart contact lens that enables users to control robots through eye movements. This innovative device combines embedded optical sensors with AI-based signal processing, offering a lightweight, intuitive human-machine interface with vast potential across industries. The lens incorporates a 10×10 array of light sensors capable of detecting subtle changes in light distribution caused by eye movements, including gaze direction and blinks. These signals are transmitted to control external robotic systems, as demonstrated with a robotic arm. Notably, the team employed a novel Meniscus Pixel Printing (MPP) technique to directly print sensors onto the curved lens surface without masks or complex fabrication steps, ensuring high precision and customizability. In addition to robotic control, the system demonstrates vision sensing capabilities by reconstructing optical information. To address the limited signal resolution inherent to micro-scale devices, the researchers applied deep-learning-based super-resolution algorithms, reconstructing high-fidelity signals equivalent to an 80x80 sensor array in just 0.03 seconds. This enables real-time, accurate control based solely on eye movements, achieving recognition accuracies of up to 99.3% under experimental conditions. This technology marks a significant advancement in ultra-compact human-machine interfaces, enabling precise, hands-free control of electronic devices. Potential applications include remote robotic operation, medical assistive devices, exploration in hazardous environments, defense systems, and smart mobility. Published in the March 2026 issue of Advanced Functional Materials (Impact Factor: 19.0, JCR Top 5%)—a top-tier journal in materials science—the research was selected as the Front Cover of the latest issue. The study received support from the National Research Foundation of Korea (NRF), the Ministry of Science and ICT (MSIT), the Institute of Information & Communications Technology Planning & Evaluation (IITP), and the Ministry of Trade, Industry, and Energy (MOTIE). Journal Reference Byung-Hoon Gong, Dohyean Kim, Jiyun Jeong, et al ., “Meniscus Pixel Printing for Contact-Lens Vision Sensing and Robotic Control,” Adv. Funct. Mater. , (2026).

UNIST has launched a major interdisciplinary research initiative to accelerate AI-driven transformation (AX) across the shipbuilding sector. Selected under the Ministry of Science and ICT’s national initiative for large-scale industrial AI, the project is supported by a total budget of KRW 40.3 billion (approximately USD 30 million). It aims to develop domain-specific AI foundation models trained on real-world shipyard data and deploy them in operational environments. The initiative brings together faculty from the Graduate School of Artificial Intelligence (AIGS), along with multiple engineering and business disciplines, forming a collaborative structure that spans the full lifecycle of industrial AI—from data generation and model training to system deployment and real-world validation. At the core of the project is the development of a multimodal, domain-specific foundation model for shipbuilding. This effort is led by Professor Sung Youb Kim from the Graduate School of Semiconductor Materials and Devices Engineering, with participation from Professors Seung-Hoon Na, Youngsoo Jang, Seungryul Baek, and Taehwan Kim from AIGS, along with Professor Hayoung Chung from the Department of Mechanical Engineering. The team will develop a model capable of integrating diverse data generated across shipyard operations, serving as a central engine for a wide range of downstream applications. Complementing this effort, a parallel workstream led by Professors Sung Whan Yoon, Jae-Young Sim, and Yeon-Chang Lee from AIGS, along with Professor Saerom Park (Department of Industrial Engineering) and Professor Seongil Wi (Department of Computer Science and Engineering) will establish a scalable industrial platforms for optimized for heterogeneous datasets. In parallel, Professors Seungjoon Yang, Gi-soo Kim, and Yeon-Chang Lee from AIGS will lead research on synthetic data generation, addressing limitations in real-world data and improving model robustness across diverse scenarios. Application-focused research will be led by Professors Youngdae Kim, Sungil Kim, Hyungho Na, Dong Young Lim, and Chiehyeon Lim from the Department of Industrial Engineering, along with Professor Junghoon Kim (Department of Computer Science and Engineering), Professor Jinyoung Choi (AIGS), and Professor Junhyoung Ha (Department of Mechanical Engineering). Their work will focus on manufacturing optimization, production scheduling, predictive maintenance, and the intelligent operation of collaborative robotic systems. The development of lightweight, on-device AI technologies will be led by Professors Taesik Gong (Department of Computer Science and Engineering), Jongeun Lee (Department of Electrical Engineering), and Ranggi Hwang (Department of Computer Science and Engineering). Meanwhile, Professors Byeong Ki Seo (School of Business Administration) and Namhun Kim (Department of Mechanical Engineering) will oversee field validation and service deployment to ensure practical application and scalability. To ensure real-world impact, the initiative will be carried out in close collaboration with industry partners, including HD Hyundai Heavy Industries, HD Korea Shipbuilding & Offshore Engineering, and CrowdWorks. By leveraging operational data from active shipyards, the consortium will validate and refine technologies through on-site implementation. Professor Seung-Hoon Na, who coordinates the overall project, stated, “This initiative goes beyond developing AI models—it establishes a foundation for industrial AX. By integrating AI into production planning, process optimization, and quality control, we expect to drive meaningful innovation across the shipbuilding industry.” The UNIST Office of Research Affairs supported the initiative through its Pre-Award pilot program, providing strategic and administrative assistance in proposal development, budgeting, and coordination. The university plans to further advance its research management framework to support large-scale, high-impact initiatives.

A startup incubated at UNIST has successfully listed on the KOSDAQ, marking a significant milestone in the university's efforts to translate research into commercially viable technologies and foster globally competitive ventures. RecensMedical Inc., a faculty-founded startup established in 2016, began trading on the KOSDAQ on March 31, 2026. The company is based on proprietary research developed in the laboratory of Professor Gun-Ho Kim of the Department of Mechanical Engineering at UNIST. The company specializes in precision cooling-based medical devices for dermatology, ophthalmology, and veterinary applications. Its core technology enables real-time monitoring of tissue temperature and precise control of cooling intensity and duration, offering an alternative to conventional chemical anesthesia by reducing patient discomfort and improving procedural efficiency. "RecensMedical exemplifies how UNIST's foundational research can be transformed into impactful, market-ready solutions," said Professor Gun-Ho Kim, CEO of RecensMedical Inc. "We will continue to accelerate global expansion by securing regulatory approvals and strengthening international distribution channels." The company's public listing underscores the effectiveness of UNIST's R&D Scale-Up Strategy, which integrates the full innovation pipeline—from fundamental research and intellectual property development to startup incubation, manufacturing, and global market entry. UNIST has systematically supported technology commercialization by building strong patent portfolios from early-stage research, providing on-campus incubation infrastructure, and offering programs for prototyping, investment attraction, and overseas expansion. RecensMedical Inc. benefited from this ecosystem throughout its growth. In particular, UNIST’s GMP-certified manufacturing facilities played a critical role in securing regulatory approval from the Ministry of Food and Drug Safety (MFDS), enabling both product validation and commercialization. The company has also achieved steady growth through continued institutional and external support. It was selected as the first TIPS (Tech Incubator Program for Startup) in Ulsan in 2017 and later secured investment from a fund managed by UNIST Holdings in 2022. Additional support was provided through the Ulsan-Ulju INNOTOWN, where UNIST serves as a core technology institution. The initiative offers access to advanced research infrastructure, demonstration facilities, and R&BD funding, while facilitating connections to private investment and global mentoring networks. President Chong Rae Park stated, "UNIST's R&D Scale-Up Strategy emphasizes commercialization from the earliest stages of research. RecensMedical's growth demonstrates how integrated support—from technology validation to global collaboration—can translate research into real industrial impact. We will continue to strengthen an innovation ecosystem that connects research, industry, and global markets." RecensMedical Inc. has developed a portfolio of precision cooling devices designed to reduce pain and shorten treatment time across multiple clinical fields. Its flagship products include TargetCool for dermatology, OcuCool for ophthalmology, and VetEase for veterinary care. Following its KOSDAQ debut, the company is positioned to accelerate global commercialization and further establish itself as a leader in precision cooling medical technology.

Abstract Skin-interfacing electrodes are essential for wearable bioelectronics, yet conventional gel- and dry-type electrodes often suffer from dehydration, poor skin conformity, irritation, and delamination during motion, limiting their long-term performance. Here, a self-attachable liquid metal channel (S-LMC) patch is presented that integrates open-bottom Galinstan microchannels and micropillar arrays, both featuring re-entrant geometries for enhances skin adhesion and leakage suppression. A via-hole interconnect enables direct vertical signal transmission, eliminating the need for bulky wiring and facilitating compact integration. The patch achieves strong, reusable skin adhesion (>60 kPa), low contact impedance (7.35 kΩ·cm2 at 10 Hz), and minimal skin irritation. Compared to commercial Ag/AgCl gel electrodes, the S-LMC patch exhibits >5× lower impedance, >2× higher ECG signal fidelity (20.23 dB vs. 9.03 dB under motion), and >2.4× higher long-term adhesion after 7 days. Its re-entrant microarchitecture also improves Galinstan confinement, achieving >2× higher critical pressure for leakage. These features enable motion-resilient biosignal monitoring and scalable system integration, establishing the S-LMC patch as a promising platform for next-generation skin-conformal bioelectronic interfaces. Conventional ECG patches often require cold gels and adhesives, which can cause skin irritation and leave marks. These materials can also lose effectiveness during vigorous movement, compromising signal quality. Addressing these issues, UNIST researchers have developed a novel, self-adhesive ECG patch that eliminates the need for gels and chemical adhesives. Led by Professor Hoon Eui Jeong of the Department of Mechanical Engineering, the team introduced an innovative design integrating liquid metal microchannels with microstructured silicone elastomers. The patch features a spiral-shaped liquid metal channel—about 20 micrometers wide—that directly contacts the skin through an open-bottom structure. This configuration enables direct transmission of cardiac signals to the electrodes, ensuring high-fidelity recording even during intense physical activity. To prevent liquid metal leakage, the researchers incorporated inwardly curled horizontal microstructures at the base of the channels. This structural feature effectively contains the liquid metal, maintaining device integrity under pressure. Moreover, the ultra-thin microchannels—less than 20 micrometers thick—prevent the patch from feeling cold or uncomfortable against the skin. The entire surface of the patch is embedded with microprotrusions measuring 28 micrometers in diameter and 20 micrometers in height, which serve as physical anchors. These crown-like structures conform tightly to the skin’s microcurvatures, significantly enhancing adhesion. As a result, the patch maintains a strong, reusable bond that withstands vigorous movements such as walking or running. Figure 1. Design and key advantages of the S-LMC patch Performance evaluations demonstrated that the new patch’s electrode impedance is over five times lower than that of commercial products, enabling the detection of weak signals with greater clarity. Its robust adhesion—capable of supporting a weight of 100 grams—ensures stable contact during physical activity. Importantly, even after seven days of repeated use, the patch maintained more than twice the adhesion strength of conventional disposable patches, reducing motion artifacts and improving signal stability. Unlike traditional patches that rely on adhesives and gels, this device is durable and can be reused over 500 times. Its structural adhesion mechanism remains effective over prolonged use, and it maintains high signal accuracy without the need for gels that dry out and impair performance over time. Professor Jeong remarked, "We have successfully solved the challenges of liquid metal leakage and skin adhesion through precise structural design. This technology lays the foundation for next-generation wearable health monitoring systems, especially for patients with sensitive skin, and for high-precision human-machine interfaces." The research team is currently collaborating with Anvix Lab Co., Ltd. to commercialize the technology. Recognized for its innovation, the project was selected for the TIPS program supported by the Ministry of SMEs and Startups, and initial investments are already underway. Founded by Professor Jeong and Professor Jae Joon Kim of the Department of Electrical Engineering, Anvix Lab Co., Ltd. aims to lead the future of wearable healthcare devices by integrating this advanced patch technology with on-chip AI solutions. The findings of this research have been featured as Inside Front Cover of the January 2026 issue of Advanced Science. The research was supported by the Ministry of Science and ICT (MSIT), the Ministry of Trade, Industry & Energy (MOTIE), and the National Research Foundation of Korea (NRF). Journal Reference Sang-Woo Lee, Hyeonseok Song, Jinseo Kim, et al., "Self-Adhesive Liquid Metal Channel Patch with Tip-Guided Conformal Coupling and Leakage Suppression for Skin Bioelectronics," Adv., Sci., (2025).

Abstract Proportional-integral-derivative (PID) equipp-ed controllers are widely used in industrial robots potentially facing substantial payload and environmental variations. Their widespread use may impede the implementation of advanced robust control methods. This study presents a robust adaptive PID control algorithm that is model-independent, simple, and suitable for deployment on commercial PID-equipped devices. The proposed adaptive PID control is based on a modified version of adaptive time-delay control employing modified Nussbaum functions to ensure stability under substantial variations of robot and environment dynamics, and includes straightforward gain drift prevention schemes. The performance of the proposed adaptive PID control was experimentally evaluated with other PID controls and advanced model-independent control, showing enhanced robustness and no gain drift. These results demonstrate the potential of the proposed PID control for immediate deployment in industrial robots with a PID-equipped controller. A groundbreaking control technology has been developed to enable robotic arms to manage sudden heavy loads without excessive vibrations. This innovative solution can be implemented instantly through a simple software update to existing industrial robot controllers—used in over 90% of manufacturing facilities worldwide. Researchers, led by Professor Sanghoon Kang from the Department of Mechanical Engineering at UNIST announced the successful development of an adaptive proportional-integral-derivative (PID) control algorithm, capable of maintaining stable operation even under abrupt load changes or external impacts. PID controllers serve as the brain of robotic arms, calculating the force needed to follow a desired trajectory in real time. Due to their simplicity and proven reliability, they are the dominant control method in industrial robotics. However, conventional PID systems lack flexibility. They rely on fixed parameters, making them vulnerable to malfunctions or vibrations if the load suddenly varies or the robot encounters external contact. To overcome this limitation, the research team developed an algorithm that enables robots to automatically fine-tune their control parameters in real time based on error feedback. Unlike traditional adaptive PID systems, their approach is specifically engineered to counteract the minute signal noise—called quantization errors—generated by digital sensors in robot joints. Previous adaptive algorithms often overreacted to this noise, causing unnecessary force adjustments and system instability. Figure 1. Experimental setup of a planar two-DOF direct-drive five-bar robot with hardware and software architecture and kinematic structure. A hook in end-effectors allows the robot to be connected with external dynamics. A key advantage of this new technology is that it requires only a software update—no hardware modifications, complex parameter inputs, or costly additional sensors are necessary. Any robot equipped with a PID controller can adopt this algorithm immediately. The team demonstrated the effectiveness of the algorithm on a two-joint robotic arm under challenging conditions, such as lifting weights equal to its own mass and operating in environments with complex spring mechanisms. Results showed that the robot could adaptively adjust its control parameters to follow precise trajectories, despite environmental disturbances. In contrast, traditional control methods exhibited larger positional errors and instability. Professor Kang remarked, "This development offers a significant enhancement in the performance of PID controllers, which are prevalent in industrial automation. It has broad potential applications—from smart factories with dynamic work environments to rehabilitation robots that respond to subtle human forces, and even humanoid robots." The findings of this research have been published in the January 2026 issue of IEEE/ASME Transactions on Mechatronics, one of the most respected journals in the fields of mechanical and robotic engineering. This work was supported through the Future Promising Fusion Technology Pioneer Project by the National Research Foundation of Korea (NRF) and by the National Rehabilitation Center (NRC). Journal Reference Jeongwoo Son, Hyunah Kang, Murim Kim, et al., "Robust Adaptive PID Control Based on a Modified Nussbaum Function for Robots Exposed to Significant Changes in Dynamics," TMECH, (2025).

Professor Sang Hoon Kang of the Department of Mechanical Engineering at UNIST has been honored with the Most Cited Article Award from the International Journal of Precision Engineering and Manufacturing (IJPEM), a leading international journal in robotics and manufacturing engineering. The award recognizes researchers whose work has demonstrated outstanding academic impact. Professor Kang's paper was selected as the most cited paper published in IJPEM between 2023 and 2024, highlighting its influence within the field of robot control and manufacturing engineering. Their paper, titled 'A Review on Robust Control of Robot Manipulators for Future Manufacturing,' was published in the April 2023 issue of IJPEM. The review examines robust control techniques for robot manipulators, with a particular focus on the theoretical development and practical applicability of Time-Delay Control (TDC). In the paper, Professor Kang brings together more than three decades of accumulated research on TDC, offering a systematic overview of its evolution and use in real-world applications. He shows that TDC enables stable and efficient control of complex robotic systems—including humanoid robots, rehabilitation robots, and industrial manipulators—despite the challenges posed by nonlinearity and time-varying dynamics. The study has been widely cited for providing a clear and comprehensive reference long sought by researchers in the field. Reflecting on the award, Professor Kang said, "This work is meaningful in that it presents, for the first time, a comprehensive review of control methods that can be applied in practice even to highly nonlinear and complex robotic systems. The citation record shows that many researchers have been seeking a systematic understanding of TDC." He added, "I will continue to pursue research on more robust and efficient control techniques that can be applied across a wide range of robotic systems, including humanoid and rehabilitation robots."

Abstract Automated and effective urban stormwater management (USM) is essential to address the increase in stormwater hazards due to climate change and urbanization. Although droplet-based electricity generators (DEGs) can help monitor dynamic environmental changes in real-time, they have limited long-term reliability. Herein, a superhydrophobic fiber-reinforced polymer-based DEG (S-FRP-DEG) is proposed for efficient USM. The fiber-reinforced polymer (FRP), composed of carbon fiber fabric, carbon fiber tow (CFT), glass fiber fabric, and epoxy, offers a high strength-to-weight ratio and corrosion resistance. A spray coating of polydimethylsiloxane and polytetrafluoroethylene particles on the FRP creates a superhydrophobic surface with a water contact angle of 167°, enabling rapid droplet contact and separation over large areas. The coating accords self-cleaning ability and forms a negatively charged surface, enhancing energy harvesting performance. A closed circuit forms upon droplet impact on the superhydrophobic surface and contact with the adjacent CFT, generating up to 117.93 mW m−2 per droplet. Textured finish formed by peel ply during FRP manufacturing further improves energy generation and coating durability. The S-FRP-DEG can be configured with multiple CFTs to increase the energy storage rate, enabling LED operation and autonomous USM. This technology provides a practical solution for energy-efficient urban management and supports smart city development. A research team, affiliated with UNIST has introduced a technology that generates electricity from raindrops striking rooftops, offering a self-powered approach to automated drainage control and flood warning during heavy rainfall. Led by Professor Young-Bin Park of the Department of Mechanical Engineering at UNIST, the team developed a droplet-based electricity generator (DEG) using carbon fiber-reinforced polymer (CFRP). This device, called the Superhydrophobic fiber-reinforced polymer (S-FRP-DEG), converts the impact of falling rain into electrical signals, capable of operating stormwater management systems without an external power source CFRP composites are lightweight, yet durable used in a variety of applications, such as aerospace and construction for its strength and resistance to corrosion. Such characteristics make it well suited for long-term outdoor installation on rooftops and other exposed urban structures. The generator produces electricity through a process similar to static charge generation. When a positively charged raindrop contacts the device's negatively charged superhydrophobic surface, electric charge is transferred as the droplet rapidly detaches and rolls away. This motion drives an electric current through embedded carbon fibers, generating power almost instantly. Figure 1. Structure and Working Mechanism of S-FRP-DEG. Unlike conventional metal-based droplet generators, which are prone to corrosion from moisture and urban pollutants, the CFRP-based design maintains stable performance under harsh environmental conditions. The research team further improved efficiency by introducing a textured surface and lotus-leaf-inspired coating that enhances water repellency while preventing the buildup of dirt and soot. Traditional droplet generators made from metals often suffer from corrosion caused by pollution and moisture. This new CFRP-based device overcomes that problem with excellent durability and stability over time. In laboratory tests, a single raindrop with a volume of approximately 92 microliters generated up to 60 volts and a few microamps of current. When four units were connected in series, the system briefly powered 144 LED lights, demonstrating its scalability. The team also validated the technology in real-world settings by installing the device on building rooftops and drainage pipes. As rainfall intensity increased, the electrical signals became stronger and more frequent, allowing the system to distinguish between light, moderate, and heavy rain and automatically activate drainage pumps when necessary. "This technology enables urban infrastructure to monitor rainfall and respond to flood risks using only the energy of rain itself," said Professor Park. "Looking ahead, it could be further integrated into mobility systems, including vehicles or aircrafts, where carbon fiber composites are already widely used." The study was led by Dr. Seong-Hwan Lee and Dr. Jae-Jin Kim, as first authors. Funded by the Ministry of Science and ICT (MSIT) and the National Research Foundation (NRF) of Korea, the research was published in the online version of Advanced Functional Materials on November 20, 2025. Journal Reference Seonghwan Lee, Jaejin Kim, and Young-Bin Park, "Structural Droplet-Based Electricity Generator Using Superhydrophobic Fiber-Reinforced Polymer for Smart Stormwater Management," Adv. Funct. Mater., (2025).