Recently, flexible pressure sensors featuring enhanced sensitivity and durability through nano/micro additive manufacturing have been employed in various fields, including medical monitoring, E-skin technology, and soft robotics. This study focuses on the fabrication and verification of an interdigitated electrode (IDE) based flexible pressure sensor that incorporates microstructures, utilizing a direct patterning-based additive process. The IDE-patterned sample was designed with a total size of 7.95 × 10 mm2, a line width of 150 µm, a spacing of 200 µm, and a probe pad measuring 1.25 × 2 mm2. It was fabricated using AgNP ink on a primed 100 µm thick polyethylene naphthalate (PEN) substrate. The electrode layer was subsequently covered with a sensing layer made of a MWCNT/Ecoflex composite material, resulting in the final pressure sensor sample. Measurements indicated that the sensor exhibited good sensitivity and response speed, and it was confirmed that further improvements in sensitivity could be achieved by optimizing the size, spacing, and height of the microstructures. Building on the flexible pressure sensor structure developed in this study, we plan to pursue future research aimed at fabricating array sensors with integrated circuits and exploring their applicability in wearable devices for pressure sensing and control functions.
Overhang structures are essential geometries in metal additive manufacturing for realizing complex shapes. However, achieving stable, support-free overhang structures requires precise control of process parameters, and securing shape fidelity becomes particularly challenging as overhang length increases due to thermal deformation. To address this challenge, this study proposed a Bayesian optimization framework for efficiently identifying optimal process parameters to fabricate high-difficulty overhang structures. An image-based scoring method was developed to quantitatively evaluate shape defects. Experimental data were collected by fabricating 3, 6, and 9 mm overhang structures with various process parameters. Based on collected data, Gaussian Process Regression (GPR) models were trained. A physics-informed soft penalty term based on energy density was incorporated to construct a surrogate model capable of making physically plausible predictions even in extrapolated regions. Using this model, Bayesian optimization was applied to overhang lengths of 12, 15, and 18 mm, for which no prior experimental data existed. Recommended parameters enabled stable, support-free fabrication of overhang structures. This study demonstrates that reliable optimization of process parameters for complex geometries can be achieved by combining minimal experimental data with physics-informed modeling, highlighting the framework’s potential extension to a wider range of geometries and processes
Facility Layout Problem (FLP) aims to optimize arrangement of facilities to enhance productivity and minimize costs. Traditional methods face challenges in dealing with the complexity and non-linearity of modern manufacturing environments. This study introduced an approach combining Reinforcement Learning (RL) and simulation to optimize manufacturing line layouts. Deep Q-Network (DQN) learns to reduce unused space, improve path efficiency, and maximize space utilization by optimizing facility placement and material flow. Simulations were used to validate layouts and evaluate performance based on production output, path length, and bending frequency. This RL-based method offers a more adaptable and efficient solution for FLP than traditional techniques, addressing both physical and operational optimization.
This paper presents the development of a design optimization module for achieving the best performance of hydrostatic bearings. The design optimization module consists of two components: a bearing performance analysis module and an optimization module that utilizes optimization algorithms. Widely recognized global search methods, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm, were employed as the optimization algorithms. The design optimization problem was defined for hydrostatic bearings. Optimization design processes were carried out to improve load capacity, stiffness, and flow rate. Subsequent experimental validation was conducted through the fabrication of a practical experimental setup. The design optimization model demonstrated superior performance compared to the initial model while satisfying design conditions and constraints. This confirms the practical applicability of the design optimization module developed in this study.
This study deals with the structural integrity of a co-axial octocopter cargo drone. Most unstable states in progress of various flight missions of the cargo drone are considered to be derived from take-off and landing operations. In order to evaluate the structural integrity of these states, three-dimensional FE (finite element) simulation using whole frame assembled with structural members and components is performed, and then the effective stress level and deflection degree are investigated. Also, topology optimization is adopted to improve the locally concentrated stress and large deflection around front and rear sections of the motor-support side member. From topology optimization, it is ensured that the shape and location of plate support have to be modified for improving the stress level and the deflection degree. Based on the optimized and modified feature, FE simulation is re-performed. Consequently, it is confirmed that the effective stress and the deflection are reduced to about 26.67% and 19.15% around the side member, respectively.
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Utilization of topology optimization and generative design for drone frame optimization Michał Kowalik, Michał Śliwiński, Mateusz Papis Aircraft Engineering and Aerospace Technology.2025; 97(7): 813. CrossRef
With advancements in the 3D printing technology, many industrial sectors are transitioning from traditional production methods, such as cutting processing, and casting, to utilizing 3D printers for manufacturing. For instance, in the automotive industry, the production of vehicle upright knuckle parts typically involves casting followed by machining processes, such as turning and milling, to achieve dimensional accuracy. However, this approach is associated with high processing costs and longer lead times. This study focuses on the production of vehicle upright knuckle parts using a selective laser melting (SLM)-type 3D printer, with SUS 630 as the material. To evaluate the feasibility of utilizing this method in industrial vehicles, this study conducts static and modal analyses, along with topology optimization. Additionally, experimental test drives are performed with the parts installed in KSAE BAJA vehicles, and modal frequency experiments are conducted. The objective of these analyses and experiments is to assess the performance, reliability, and applicability of utilizing SLM-based 3D printing for manufacturing vehicle upright knuckle parts by optimizing the design through topology optimization and evaluating the results through experiments and analysis.
M.2 NVMe SSD (Non-Volatile Memory express Solid-State Drive), which have higher computational speed and reliability than conventional devices, have come to be widely used. Recent studies have reported that M.2 NVMe SSD are beginning to have thermal issues due to the increasing heat generation occurring with the high chip density and high-performance operation in a limited space. Thermal issues in the controller and memory units of M.2 NVMe SSD lead to increased failure rates and decreased data retention times. In this study, we propose a compact and optimized thermal solution for commercial M.2 NVMe SSD installed between the mainboard and GPU (Graphic Processing Unit). A thermal and fluid dynamics simulation of an M.2 NVMe SSD, including the heatsink, was performed, and the Genetic Algorithm method was used to optimize the heatsink size.
This paper presents a distortion compensation algorithm for cable-driven master devices. Such device has four string pots at four corners of a frame. Four cables are tied from the four corners to the center holder. When the central holder, which is a haptic grip, moves, lengths of the four cables will change. From the four cable lengths, the spatial position of the haptic grip can be estimated using triangulation. In this case, distortion such as barrel image of the image field occurs when estimating a position with an offset parallel to the plane in which the four string pots are located. The closer to the corner, the smaller the position estimate value is than the true value. After distortion phenomenon is modeled by projecting onto the ellipsoid, the position in the vertical direction of the cable plane is compensated by the corresponding value and flattened. The mean error in the x-direction position was improved by 91% from 0.7833±0.8381 mm to -0.0709±0.4341 mm. This cable-driven master device can be used as a haptic device for operating a surgical robot.
A virtual machine tool, a computer simulation model of the machine motion and cutting process with a level of accuracy and consistency that can replace an accurate machine tool, is one of the critical digital transformation technologies in the manufacturing industry. During the machine development phase, cost and time can be reduced by evaluating machining efficiency and quality through virtual prototyping. In the machine application phase, virtual machine tools can be used to accurately assess the condition of equipment and processes by analyzing actual data combined with simulated data. This paper introduces a virtual machine tool system that can analyze the behavior of an accurate machine tool by integrating physical models of structure, numerical controller, and cutting process. The key features of the virtual machine tool, synchronous machining simulation, machining stability detection, machining error estimation, and part program optimization, were evaluated through various machining tests with a vertical 3-axis milling machine.
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A Review of Intelligent Machining Process in CNC Machine Tool Systems Joo Sung Yoon, Il-ha Park, Dong Yoon Lee International Journal of Precision Engineering and Manufacturing.2025; 26(9): 2243. CrossRef
Electric kickboards provide personal mobility with a simple structure and easy operation. With these advantages, the number of users is increasing annually. However, as the number of users of electric kickboards increases, related accidents are also increasing. To prevent accidents, this study proposes the topological optimization of an electric kickboard connecting part to improve structural strength during a front collision. The results confirmed that as the volume fraction increased, the structure connecting the board and the bottom of the handle support changed to a toroidal shape, thereby lowering the maximum stress and improving the uniformity of the stress distribution. In addition, the topological optimization was safer than the connecting parts of two typical electric kickboards sold in the Korean market. These findings can contribute to improving the safety and optimizing the design direction of electric kickboards.
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Two-Dimensional Topology Optimization of Headtube in Electric Scooter Considering Multiple Loads Min Gyu Kim, Jun Won Choi, Jung Jin Kim Applied Sciences.2025; 15(5): 2829. CrossRef
Personalized Stem Length Optimization in Hip Replacement: A Microscopic Perspective on Bone—Implant Interaction Su Min Kim, Jun Won Choi, Jung Jin Kim Bioengineering.2024; 11(11): 1074. CrossRef
Magnetic bearings are being actively adopted by the turbo-chiller industry because of their higher efficiency during partial load, quieter operation, and smaller footprint than that which machines with ball bearings provide. Since magnetic bearings are open-loop unstable, feedback control is necessary. In the industry, traditional PID-based control is preferred to model-based control, because of its simplicity. When traditional control algorithms are used, significant resources are required to obtain and tune control parameters, which is an impediment to the widespread use of magnetic bearing technology in the industry. In this paper, we propose a mixed optimization method by combining genetic algorithm and sequential quadratic programming. To obtain the initial guess to be used for the mixed optimization, a phase-margin maximization algorithm is also proposed, based on the rigid-body model of the system. Mixed optimization results in suitable control parameters in less than 2.8% of the time it takes a genetic algorithm only to find similar solutions. The proposed optimization also ensures the robustness of the control parameters. The output sensitivity measured from a prototype compressor with magnetic bearings confirms the validity of the control parameters.
Owing to recent advances in additive manufacturing technology, design for additive manufacturing (DfAM) has been used to overcome design limitations due to constraints in traditional manufacturing processes. In this study, we applied DfAM technology to design lightweight and consolidated vacuum grippers for inspection equipment. We proposed a consolidated design to reduce manufacturing time and costs, which previously encompassed assembling eleven components. Topology optimization was used to reduce part weight while maintaining structural rigidity and safety, and two optimization models were designed: two-piece and one-piece models. Based on these optimized geometries, the internal vacuum paths were designed in a curved shape to enhance adsorption characteristics. Numerical simulations were conducted to evaluate the structural performance and flow characteristics of the initial design and the two optimization models. The pressure drop of the one-piece model, which was the best design, was reduced to 1/8 of the initial design and the structural safety factor was predicted to be 6.37. This final design was then additively manufactured by a digital light processing type 3D printer and the weight of the resulting parts was reduced from 12.94 to 2.08 g. Experimental observation found that the additively manufactured vacuum gripper showed enhanced absorption performance compared to the initial design.
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A Study on Improvement of Flow Characteristics of TPMS Heat Exchanger based on Mathematical Filtering Seo-Hyeon Oh, Jeong Eun Kim, Ji Seong Yun, Do Ryun Kim, Jungwoo Kim, Chang Yong Park, Keun Park Journal of the Korean Society for Precision Engineering.2024; 41(7): 541. CrossRef
A Study on Injection Mold Design Using Topology Optimization Mi-Jin Kim, Jae-Hyuk Choi, Gyeng-Yun Baek Journal of the Korean Society of Manufacturing Process Engineers.2022; 21(4): 100. CrossRef
Improving product quality is a crucial factor in determining the competitiveness and business efficiency of enterprises. This study investigates the influence of the cutting parameters, including the cutting speed, the depth of cut, and the feed rate on the surface roughness and the residual stress during the turning of AISI 304 austenitic stainless steel. Moreover, the work aims to determine optimal cutting parameters to satisfy both surface roughness and residual stress requirements. The mathematical model of the relationship between the machining parameters and the performance characteristics was formulated based on the response surface methodology (RSM) and the Box-Behnken design of the experiments. Pareto optimal solution applying natural-inspired algorithm (Bat Algorithm) is proposed to solve the bi-objective optimization problem to obtain the lowest surface roughness and minimal residual stress. The optimum cutting parameters selected by the manufacturing planners from the Pareto optimal fronts are calculated to comply with the production requirements.
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Multi-Objective Optimization for Turning Process of 304 Stainless Steel Based on Dung Beetle Optimizer-Back Propagation Neural Network and Improved Particle Swarm Optimization Huan Xue, Tao Li, Jie Li, Yansong Zhang, Shiyao Huang, Yongchun Li, Chongwen Yang, Wenqian Zhang Journal of Materials Engineering and Performance.2024; 33(8): 3787. CrossRef
As global warming is rapidly emerging as one of the inherently global issues, one of the renewable energies, i.e. thermo-electric generation, has received attention. In order to increase the efficiency of thermo-electric generation, a maximum temperature difference in plain fins in a heat exchanger is needed, and an appropriate pressure drop is required to ensure stable flow of high temperature fluid. In the present study, the characteristics of the temperature difference and pressure difference for the 2 kW-class thermo-electric generator installed in exhaust pipes of a cogeneration plant were investigated numerically via thermal fluid analysis using ANSYS CFX. Then, size optimization for plain fins of a heat exchanger was carried out using SAS JMP, in which the temperature difference was maximized while meeting the requirement of a given pressure drop condition. A meta-model was generated by using the response surface model, and individual desirability functions were defined to derive the optimal solution that provided the maximal overall desirability function. The result obtained by size optimization showed that the temperature difference of the optimized plain fins of a heat exchanger increased by approximately 27% in comparison with the original model under the given pressure drop condition.
The purpose of this study was to design a sledge frame for para ice hockey in which an athlete sits and plays on a sledge. A sledge comprises a bucket, a blade carrier, and a frame. A sledge frame is usually fabricated by welding a number of pipes, and thus its structural safety is degenerated at the welded joints. In this study, the sledge frame was redesigned using the principle of DfAM (Design for Additive Manufacturing), to reduce the frame weight as well as to have sufficient structural safety. As an application of DfAM, the part consolidation was performed for six joints from which the number of welding spots was reduced to 56% (From 16 to 9). Among the resulting four consolidated joints, topology optimization was performed for three joints to reduce their weight while maintaining higher structural stiffness and safety. As a result, the structural stiffness and safety of the joints improved remarkably, and the resulting frame weight was reduced by 20% (From 1.66 to 1.34 kg). This weight reduction with structural enhancement is expected to improve athletes’ performance and safety in para ice hockey games.
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A Study on Improvement of Flow Characteristics of TPMS Heat Exchanger based on Mathematical Filtering Seo-Hyeon Oh, Jeong Eun Kim, Ji Seong Yun, Do Ryun Kim, Jungwoo Kim, Chang Yong Park, Keun Park Journal of the Korean Society for Precision Engineering.2024; 41(7): 541. CrossRef
Additive Manufacturing of a Release Agent Injection Manifold for Hot Forging Hak-Sung Lee, Min-Kyo Jung, Eun-ah Kim, Soonho Won, Do Wock Chun, Taeho Ha Journal of the Korean Society for Precision Engineering.2021; 38(9): 675. CrossRef
Analysis on the Warm Bending Process of Magnesium Alloy Sheet Using Additively Manufactured Polymer Die-Set Hyung-Won Youn, Jun-Hyun Kyeong, Keun Park, Chang-Whan Lee Journal of the Korean Society for Precision Engineering.2021; 38(10): 775. CrossRef
Lightweight Design of a Vacuum Gripper for Inspection Equipment Using Topology Optimization Euddeum Cha, Tae-Young Kim, Taeho Ha, Keun Park Journal of the Korean Society for Precision Engineering.2021; 38(9): 683. CrossRef
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