• Earthquakes and Structures
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• v.27 no.4
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• pp.285-302
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• 2024

Conducting nonlinear pushover analysis typically demands intricate and resource-intensive computational efforts, involving a highly iterative process necessary for meeting both design-defined and requirements of codes in performance-based design. This study presents a computer-based technique for reinforced concrete (RC) buildings, incorporating optimization numerical approaches, optimality criteria and pushover analysis to automatically enhance seismic design performance. The optimal design of concrete beams, columns and shear walls in concrete frames is presented using the artificial bee colony optimization algorithm. The methodology is applied to three frames: a 4-story, an 8-story and a 12-story. These structures are designed to minimize overall weight while satisfying the levels of performance including Life Safety (LS), Collapse Prevention (CP), and Immediate Occupancy (IO). The process involves three main steps: first, optimization codes are implemented in MATLAB software, and the OpenSees software is used for nonlinear static analysis. By solving the optimization problem, several top designs are obtained for each frame and shear wall. Pushover analysis is conducted considering the constraints on relative displacement and plastic hinge rotation based on the nonlinear provisions of the FEMA356 nonlinear provisions to achieve each level of performance. Subsequently, convergence, pushover, and drift history curves are plotted for each frame, and leading to the selection of the best design. The results demonstrate that the algorithm effectively achieves optimal designs with reduced weight, meeting the desired performance criteria.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.4
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• pp.64-69
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• 2011

Magnesium alloy has been known as lightweight material in automobile and electronic industry with aluminum alloy, titanium alloy and plastic material. Friction welding is useful to join different kinds of metals and nonferrous metals they are difficult to be joined by such as gas welding, resistance welding and electronic beam welding. In this study, friction welding was performed to investigate optimization process of Mg alloy with a 20mm diameter solid bar. For that, the orthogonal array $(L_{9}(3^{4}))$ was used that contained four factors and each factor had three levels. Control factors were heating pressure, heating time, upsetting pressure and upsetting time. Also tensile tests were carried out to measure mechanical properties for welded conditions. The levels of heating pressure and upsetting pressure used were 15, 25, 35MPa, and 30, 50, 70MPa, respectively. In addition those of heating time and upsetting time were 0.5, 1, 1.5 sec and 3, 4, 5 sec., respectively, rotating speed of 2000rpm. From the experimental results, optimization condition was estimated as follows; heating pressure=35MPa, upsetting pressure=70MPa, heating time=1.5sec, upsetting time=3sec.

• Journal of Distribution Science
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• v.17 no.4
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• pp.51-57
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• 2019

Purpose - This study aims to improve the distribution structure of the OLED market and develop cost-effective optimization techniques. Specifically, it is a study on the optimization of ferric chloride to improve the etch of SUS MASK for OLED. Research design, data, and methodology - Applying the optimal conditions of the experiment, the final confirmation was evaluated for improvement by the Process Capability Index (Cpk). It is possible to derive social performance such as improvement of precision of SUS MASK manufacturing, economic performance such as defect rate, reduction of waste generation and treatment cost, technological achievement such as SUS MASK production technology, improvement of profit structure of technology development and process improvement do. Results - The improvement of the Cpk before the improvement was made was confirmed to be 0.57% with a defect estimate of 25.07% with a failure estimate of 0.57% after the improvement, and 8.84% with a failure estimate of 0.57% level after the improvement. Conclusions - If the conclusions obtained from the specimen experiment are applied to the manufacturing process of SUS MASK, it will be possible to expect excellent cost-effective competitiveness due to the improvement of precision and reduction of defect rate to enhance the OLED market penetration.

• Journal of Power System Engineering
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• v.17 no.5
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• pp.64-68
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• 2013

One of the water management goals in PEM fuel cell is to avoid flooding and drying in the membrane, therefore the air humidification process is required. In order to increase water removal out of the membrane, the water management system may require the dehumidification process and it also requires a large space for application, moreover the process time is slow. In conformity with this fact, this present study proposes an advanced dynamic fuel cell water management which can be an intermittent optimization control using air flow rate instead of the air humidity as an variable in the optimization process. The results of this study have shown that the membrane flooding and drying can be avoided after being assisted by air velocity controlling method.

• Current Optics and Photonics
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• v.5 no.3
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• pp.322-328
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• 2021

In this paper, a pretreatment method for a matrix in convex optimization is proposed to optimize the spectral reconstruction process of a disordered dispersion spectrometer. Unlike the reconstruction process of traditional spectrometers using Fourier transforms, the reconstruction process of disordered dispersion spectrometers involves solving a large-scale matrix equation. However, since the matrices in the matrix equation are obtained through measurement, they contain uncertainties due to out of band signals, background noise, rounding errors, temperature variations and so on. It is difficult to solve such a matrix equation by using ordinary nonstationary iterative methods, owing to instability problems. Although the smoothing Tikhonov regularization approach has the ability to approximatively solve the matrix equation and reconstruct most simple spectral shapes, it still suffers the limitations of reconstructing complex and irregular spectral shapes that are commonly used to distinguish different elements of detected targets with mixed substances by characteristic spectral peaks. Therefore, we propose a special pretreatment method for a matrix in convex optimization, which has been proved to be useful for reducing the condition number of matrices in the equation. In comparison with the reconstructed spectra gotten by the previous ordinary iterative method, the spectra obtained by the pretreatment method show obvious accuracy.

• 한국전산유체공학회:학술대회논문집
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• 2009.04a
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• pp.57-65
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• 2009

An efficient and high-fidelity design approach for wing-body shape optimization is presented. Depending on the size of design space and the number of design of variable, aerodynamic shape optimization process is carried out via different optimization strategies at each design stage. In the first stage, global optimization techniques are applied to planform design with a few geometric design variables. In the second stage, local optimization techniques are used for wing surface design with a lot of design variables to maintain a sufficient design space with a high DOF (Degree of Freedom) geometric change. For global optimization, Kriging method in conjunction with Genetic Algorithm (GA) is used. Asearching algorithm of EI (Expected Improvement) points is introduced to enhance the quality of global optimization for the wing-planform design. For local optimization, a discrete adjoint method is adopted. By the successive combination of global and local optimization techniques, drag minimization is performed for a multi-body aircraft configuration while maintaining the baseline lift and the wing weight at the same time. Through the design process, performances of the test models are remarkably improved in comparison with the single stage design approach. The performance of the proposed design framework including wing planform design variables can be efficiently evaluated by the drag decomposition method, which can examine the improvement of various drag components, such as induced drag, wave drag, viscous drag and profile drag.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.1
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• pp.102-110
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• 2022

This paper proposes a solution to the problems of manufacturing cost and processability by applying discrete material and thickness optimization (DMTO) and minimizing the use of high-strength, lightweight materials in the optimization process. A simple infant pop-up seat model was selected as the application target, and the weight reduction effect and variation in strength according to the optimization results were observed. In this study, a simplified finite element model of an infant pop-up seat frame was first constructed. The model was used to perform a static structural analysis to verify the weight and strength of each part. The D-optimal design of the experimental method was then used to observe the influence of each part on the weight and strength. This process was applied using discrete thickness optimization (DTO) (which applies high-strength, lightweight materials and optimizes only the thickness) and DMTO (which considers both the material and thickness). The DTO and DMTO results were compared to verify the design method that determines the major parts and simultaneously considers the material and thickness. Accordingly, in this study, an optimal lightweight design that satisfied the strength standards of the seat frame was derived. Furthermore, discretization parameters were used to minimize the application of high-strength, lightweight materials.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.4
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• pp.107-113
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• 2022

At present, it is possible to manufacture electrodes down to several micrometers (~ ㎛) using inkjet printing technology owing to the development of precision ejection heads. Inkjet printing technology is also used in the manufacturing of bio-sensors, electronic sensors, and flexible displays. To reduce the difference between the electrode design/simulation performance and actual printing pattern performance, it is necessary to analyze and optimize the processable area of the ink material, which is a fluid. In this study, process optimization was conducted to manufacture an IDE pattern and fabricate an impedance sensor. A total of 25 IDE patterns were produced, with five for each lamination process. Electrode line width and height changes were measured by stacking the designed IDE pattern with a nanoparticle-based conductive ink multilayer. Furthermore, the optimal process area for securing a performance close to the design result was analyzed through impedance and capacitance. It was observed that the increase in the height of stack layer 4 was the lowest at 4.106%, and the increase in capacitance was measured to be the highest at 44.08%. The proposed stacking process pattern, which is optimized in terms of uniformity, reproducibility, and performance, can be efficiently applied to bio-applications such as biomaterial sensing with an impedance sensor.

• Journal of Korean Society for Quality Management
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• v.52 no.1
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• pp.43-56
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• 2024

Purpose: The injection molding process, crucial for plastic shaping, encounters difficulties in sustaining product quality when replacing injection machines. Variations in machine types and outputs between different production lines or factories increase the risk of quality deterioration. In response, the study aims to develop a system that optimally adjusts conditions during the replacement of injection machines linked to molds. Methods: Utilizing a dataset of 12 injection process variables and 52 corresponding sensor variables, a predictive model is crafted using Decision Tree, Random Forest, and XGBoost. Model evaluation is conducted using an 80% training data and a 20% test data split. The dependent variable, classified into five characteristics based on temperature and pressure, guides the prediction model. Bayesian optimization, integrated into the selected model, determines optimal values for process variables during the replacement of injection machines. The iterative convergence of sensor prediction values to the optimum range is visually confirmed, aligning them with the target range. Experimental results validate the proposed approach. Results: Post-experiment analysis indicates the superiority of the XGBoost model across all five characteristics, achieving a combined high performance of 0.81 and a Mean Absolute Error (MAE) of 0.77. The study introduces a method for optimizing initial conditions in the injection process during machine replacement, utilizing Bayesian optimization. This streamlined approach reduces both time and costs, thereby enhancing process efficiency. Conclusion: This research contributes practical insights to the optimization literature, offering valuable guidance for industries seeking streamlined and cost-effective methods for machine replacement in injection molding.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.1027-1031
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• 2000

In this paper, multiphase dynamic optimization of machine structure is presented. The final goal is to obtain ( i ) light weight, and ( ii ) rigidity statically and dynamically. The entire optimization process is carried out in two steps. In the first step, multiple optimization problem with two objective functions is treated using Pareto genetic algorithm. Two objective functions are weight of the structure, and static compliance. In the second step, maximum receptance is minimized using genetic algorithm. The method is applied to a simplified milling machine.