In this study, we developed a deep learning-based real-time fault diagnosis system to automate the weaving preparation process in textile manufacturing. By analyzing typical faults such as shaft eccentricity and rotational imbalance, we designed a data-driven fault diagnosis algorithm. We utilized tension data from both normal and faulty states to implement AI-based diagnostic models, including 1D CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and LSTM-AE (Long Short-Term Memory Autoencoder). These models enable real-time fault classification, followed by a comparative performance analysis. The LSTM-AE model achieved the best performance, with a classification accuracy of 99-100% for severe faults, such as 1.5 mm eccentricity and 100 or 150 g rotation imbalance, and 92.2% for minor faults like 1 mm eccentricity. This accuracy was optimized through threshold adjustments based on ROC curve analysis to select an optimal threshold. Building on these findings, we developed a GUI (Graphical User Interface) system capable of real- time fault diagnosis using TCP/IP (Transmission Control Protocol/Internet Protocol) communication between Python and LabVIEW. The results of this study are expected to accelerate the smartization of the weaving preparation process, contributing to improved textile quality and reduced defect rates, while also serving as a model for automation in other sectors.
This study reports an autonomous fine dust source tracking system of a water spray robot for high-rise building demolition. The core function of this system is performing a self-controlled fine dust tracking of the endpoint of the excavator, which is the fine dust generation point. The water spray robot has a lift with a parallelogram-shaped linkage to lift the water spray drum to 10 m from the ground. The sensor network system is connected to the robot and the excavator to calculate the relative position of the water spray drum and excavator endpoint using forward kinematics. RTK-GPS is attached to the robot and the excavator to calculate the relative distance. By sensor network, forward kinematics, and RTK-GPS, the water spray robot can autonomously track fine dust generation point and spray water to the endpoint of the excavator. The experiment was conducted to confirm the accuracy of kinematics calculation and tracking performance of the robot. The first experiment showed that the calculation result of forward kinematics was accurate enough to fulfill tracking operations. The second experiment showed that the tracking accuracy was precise enough, meaning that the robot could autonomously track fine dust generation point.
There have been frequent fatal accidents of firefighters at fire scenes. A firefighting robot can be an alternative to humans at a fire scene to reduce accidents. As a critical function of the firefighting robot, it is mandatory to autonomously detect a fire spot and shoot water. In this research, a deep learning model called YOLOv7 was employed based on thermal images to recognize the shape and temperature information of the fire. Based on the results of the test images, which were not used for learning purposes, a recognition rate of 99% was obtained. To track the recognized fire spot, a 2-DOF pan-tilt actuation system with cameras was developed. By using the developed system, a moving target can be tracked with an error of 5%, and a variable target tracking test by alternately covering two target braziers showed that it takes about 1.5 seconds to track changing targets. Through extinguishment experiments with a water spray mounted on the pan-tilt system, it was observed that the temperature of the brazier dropped from 600 degrees to 13 degrees. Based on the obtained data, the feasibility of a robotic firefighting system using image recognition was confirmed.
In this study, a novel input signal transformer is introduced to remove an overshoot and steady-state error that hinder stable tracking and landing of an unmanned aerial vehicle (UAV) based on image recognition, and the performance is verified. The Input Signal Transformer (IST) is designed in the shape of a sigmoid function to attenuate or amplify the input signal to resolve the aforementioned problems. For the verification of the UAV control system based on the IST, the UAV, target, and sensors were implemented in a virtual environment using the robot operating system (ROS). And data exchange structure and control system were built by the ROS-based message communication. A simulation was performed to confirm the elimination of the overshoot and steady-state error when the UAV to which the above control system was applied tracks and lands a fixed target and a moving target. As a result of the simulation, when the IST was not used, the UAV performed an unstable because of the overshoot and could not land on the target by the steady-state error. Conversely, in the case of using IST, it was confirmed that the flight was stable and landed successfully.
In this study, a novel size adjustable robot that could overcome an unstructured environment was introduced. To provide the robot with a volume-modifiable function, negative Poisson’s ratio structure with a unique characteristic about deformation of material was applied to the design of the body frame. The robot could simultaneously adjust its width and length with only one directional control with the help of the negative Poisson’s ratio structure. An omni-directional mobile mechanism was adopted to drive its wheels and allow flexible movement in a narrow space. However, during the procedure to adjust the size of the robot, a slip phenomenon occurred, resulting in an unnecessary movement. To solve this problem, the unnecessary offset was measured through repetitive tests and applied to the robot to compensate the position shift. To verify the performance of the robot, a test bed with a narrow space was fabricated. Extensive experiments were conducted to evaluate environmental recognition and size adjustment function by calculating the width of the narrow space and scaling the robot"s body. Results confirmed that the robot sufficiently achieved the motion objective to move in a narrow space with its size adjustment function.
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Auxetic and Holonomic Mobile Robot for Enhanced Navigation in Constrained Terrains Cheonghwa Lee, Jinwon Kim, Hyeongyeong Jeong, Hyunbin Park, Baeksuk Chu Journal of Field Robotics.2025; 42(8): 4414. CrossRef
In Korea, water spraying to suppress the dust during building dismantling operations has been done manually by human laborers, considered extremely dangerous since it often causes fatal accidents. Abroad, however, water spraying machines have been developed and used in construction sites instead of workers to prevent such serious industrial accidents. In this study, the first domestic water spraying machine is suggested. Since the spraying machine should have a novel dust tracking function, an optimal structure and mechanism should be designed to guarantee its motion performance. The motion for target tracking is achieved by the 2 DOF (Degrees of Freedom) structure comprising a linear and a rotary actuator. Then, the geometric analysis was performed to provide a sufficient kinematic workspace. Through the dynamic performance simulation, the optimal actuator capacities could be selected to generate an appropriate acceleration. The geometric and dynamic performance was evaluated by the extensive motion experiments. With this study, it is expected that an advanced water spraying machine can be developed only with domestic technologies to protect construction laborers from potentially dangerous accidents.
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Excavator Posture Estimation and Position Tracking System Based on Kinematics and Sensor Network to Control Mist-Spraying Robot Sangwoong Lee, Hyunbin Park, Baeksuk Chu IEEE Access.2022; 10: 107949. CrossRef
In this study, slip phenomenon that occurs during trajectory tracking motion of an omni-directional mobile robot based on Mecanum wheels was analyzed. Mecanum wheels which generate the omni-directionality to the mobile robot comprise a centered rim wheel and passive sub-rollers. In forward and backward motion, they function like usual wheels to enable rolling along the ground. However, in sideways motion, they create lateral motion of the mobile robot from the rotational actuation using their peculiar structural configuration, during which slip of the sub-rollers occurs. Unnecessary over-slip of the sub-rollers causes tracking errors of the mobile robot motion. To analyze the properties and reasons for the slip phenomenon, squared and circular trajectory tacking experiments were performed. From the experiments, it was observed that sideways motion generated respectively larger tracking errors than forward and backward motion. The geometric analysis regarding the tracking error generation was discussed using the Mecanum wheel structure. Finally, it was confirmed that suspension mechanism to provide four Mecanum wheels of the mobile robot with even reaction forces on the ground is necessary.
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Auxetic and Holonomic Mobile Robot for Enhanced Navigation in Constrained Terrains Cheonghwa Lee, Jinwon Kim, Hyeongyeong Jeong, Hyunbin Park, Baeksuk Chu Journal of Field Robotics.2025; 42(8): 4414. CrossRef
Development of Pipe Robot by Using Mecanum Wheels Daeyoung Kim, Soonwook Park, Hojoong Lee, Jongpil Kim, Wonji Chung, Dohoon Kwak Journal of the Korean Society of Manufacturing Process Engineers.2021; 20(2): 58. CrossRef
Mobile Robot Overcoming Narrow Space Using Negative Poisson’s Ratio Jinwon Kim, Hyeongyeong Jeong, Baeksuk Chu Journal of the Korean Society for Precision Engineering.2021; 38(7): 479. CrossRef
Recently, improvement of productivity of the paper cup forming machine has being conducted by increasing manufacturing speed. However, rapid manufacturing speed imposes high load on cams and cam followers. It accelerates wear and cracking, and increases paper cup failure. In this study, a failure diagnosis algorithm was suggested using vibration data measured from cam driving parts. Among various paper cup forming processes, a test bed imitating the bottom paper attaching process was manufactured. Accelerometers were installed on the test bed to collect data. To diagnose failure from measured data, the K-NN (K-Nearest Neighbor) classifier was used. To find a decision boundary between normal and abnormal state, learning data were collected from normal and abnormal state, and normal and abnormal cams. A few representative features such as mean and variance were selected and transformed to the relevant form for the classifier. Classification experiments were performed with the developed classifier and data gathered from the test bed. According to assigned K values, a successful classification result was obtained which means appropriate failure recognition.
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A Study on 3D Printing Conditions Prediction Model of Bone Plates Using Machine Learning Song Yeon Lee, Yong Jeong Huh Journal of the Korean Society for Precision Engineering.2022; 39(4): 291. CrossRef
Water spraying work to prevent the dust from scattering during building dismantling operation has usually been done manually. Since it is very risky and often causes fatal accidents due to unexpected collapse, a few countries have adopted mechanical water spaying machines. However, these machines are still operated by human laborer, specifically in orienting the spraying direction, which induces low dust suppression efficiency. In this research, an automated fine dust tracking system was suggested to identify and track the dust generating position accurately. A GPS is installed on the secured body of the excavator which contains a crusher as an end-effector for building dismantlement. Assuming the position of the crusher is the dust generating spot, a forward kinematics analysis is performed to identify the crusher position from the body origin on which the GPS sensor is placed. With another GPS on the water-spraying robot, its relative position to the dust generating spot and its heading angle for tracking can be calculated consequently. Tracking experiments were conducted with a miniature excavator and a reduced size water spraying robot. The results showed a sufficient tracking performance enough to be applied to the water spaying machines.
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Autonomous Fine Dust Source Tracking System of the Water Spray Robot for High-rise Building Demolition Hyeongyeong Jeong, Hyunbin Park, Jaemin Shin, Hyeonjae Jeong, Baeksuk Chu Journal of the Korean Society for Precision Engineering.2023; 40(9): 695. CrossRef
Motion Trajectory Planning and Design of Material Spraying Service Robot Gang Wang, Hongyuan Wen, Jun Feng, Jun Zhou, Haichang Zhang Advances in Materials Science and Engineering.2022; 2022: 1. CrossRef
Excavator Posture Estimation and Position Tracking System Based on Kinematics and Sensor Network to Control Mist-Spraying Robot Sangwoong Lee, Hyunbin Park, Baeksuk Chu IEEE Access.2022; 10: 107949. CrossRef
Optimal Design and Verification of a Water Spraying Robot for Dust Suppression Seolha Kim, Baeksuk Chu Journal of the Korean Society for Precision Engineering.2020; 37(10): 729. CrossRef
A paper cup forming machine performs the entire process to produce paper cups. Recently, as the demand for paper cups in various fields increases, the need for rapid and timely paper cup forming also increases. However, the more rapid the manufacturing speed is, the higher the possibility of forming failure. Frequent fault occurrences cause a time-consuming and costly repair process and reduces manufacturing efficiency. Among various fault factors in this research, position deviation of the paper from the original position, which induces a jamming and process stop, was selected and a novel deviation detecting system using multiple photo sensors was suggested. Before operating the position detecting system, the performance of the photo sensors was evaluated with respect to response speed and photo beam precision. A deviation detecting mechanism was designed. The developed deviation detecting system was integrated with the paper cup forming machine and experimented with using base papers. It was conformed that the suggested system could be used to diagnose paper deviation failure.
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The Development of a Failure Diagnosis System for High-Speed Manufacturing of a Paper Cup-Forming Machine Seolha Kim, Jaeho Jang, Baeksuk Chu Journal of the Korean Society of Manufacturing Process Engineers.2019; 18(5): 37. CrossRef
In this paper an omni-directional mobile robot is suggested for educational robot platform. Comparing to other robots, a mobile robot can be easily designed and manufactured due to its simple geometric structure. Moreover, since it is required to have low DOF motion on planar space, fabrication of control system is also simple. In this research, omni-directional wheels were adopted to remove the non-holonomic characteristic of conventional wheels and facilitate control system design. Firstly, geometric structure of a Mecanum wheel which is a most frequently used omni-directional wheel was demonstrated. Then, the organization of the mobile platform was suggested in aspects of mechanism manufacturing and electronic hardware design. Finally, a methodology of control system development was introduced for educational purpose. Due to an intuitive motion generating ability, simple hardware composition, and convenient control algorithm applicability, the omni-directional mobile robot suggested in this research is expected to be a promising educational platform.
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