• International Journal of Quality Innovation
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• v.10 no.1
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• pp.1-23
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• 2009

Face the process yield rate improvements of motherboard, although general enterprises finish deployment goal of each functions by overall quality managements, through quality improvement methods, industry engineering methods, plan-do-check-act (PDCA) methods and other improvement solutions, but it is only can be improved partially and unable to enhance the yield rate of product to the target. It only can takes one step ahead to enhance the process yield rate of motherboard with six sigma ($6{\sigma}$) overall DMAIC process and tactics. This research aimed to use six sigma quality improvement tactics by DMAIC systematic procedure and tactics, and find the key factors that effect to the process yield rate of surface mount technology. It also identified the keys input and process and output index to satisfy customer requirements and internal process index. The results showed that the major effective factors by fishbone and process failure modes and effects analysis (PFMEA). If the index of input and output that can be quantified, the optimum parameter can be found through design of experiment to ensure that the process is stable. If the factor of input and output that cannot be quantified, we found out the effective countermeasure by Mind_Mapping, make sure whole processes can be controlled stably, to reach the high product quality and enhance the customer satisfaction.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.5
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• pp.64-71
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• 2009

Cylindrical lens core for optical transceiver was designed and machined. With the lens design data, WC asymmetric core surface data were generated for non-revolutional ultra-precision grinding. Grinding process for optimum machining conditions of target surface was studied in terms of surface roughness and form profile. We used experimental results to optimize turbine speed, feed-rate and depth of cut with durable grinding wheel wear. Ground WC cores were measured contact type profilers and verified.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.221-224
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• 2009

In this paper, we deal with the development of an optimal design program for a dual-band of 0.92 GHz and 2.45 GHz with shorting pin and slot by using evolution strategy. the optimal shorting pin, coaxial feed and H-shaped patch are determined by using an optimal design program based on the evolution strategy. To achieve this, an interface program between a commercial EM analysis tool and the optimal design program is constructed for implementing the evolution strategy technique that seeks a global optimum of the objective function through the iterative design process consisting of variation and reproduction. The resonance frequencies of the dual-band antenna yielded by the optimal design program are 0.92 GHz and 2.45 GHz that show a good agreement to the design target values.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.484-490
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• 2000

The abjective of the study is the development of the system for effective prediction of residual stresses using the backpropagation algorithm from the neural network. To achieve this goal, the series experiment were carried out and measured the residual stresses using the sectional method. Using the experimental results, the optional control algorithms using a neural network should be developed in order to reduce the effect of the external disturbances on during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, the neural network based on backpropagation algorithm might be controlled weld quality. This system can not only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.12a
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• pp.141-145
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• 2003

We propose the model of Software Rejuvenation methodology, which is applicable for survivability. Software rejuvenation is a proactive fault management technique and being used in fault tolerant systems as a cost effective technique for dealing with software faults. Survivability focuses on delivery of essential services and preservation of essential assets, even systems are penetrated and compromised. Thus, our objective is to detect the intrusions in a real time and survive in face of such attacks. As we deterrent against an attack in a system level, the Intrusion tolerance could be maximized at the target environment. We address the optimal time to execute ad hoc software rejuvenation and we compute it by using the semi Markov process. This is one way that could be really frustrated and deterred the attacks, as the attacker can't make their progress. This Software Rejuvenation method can be very effective under the assumption of unknown attacks. In this paper, we compute the optimum time to perform an ad hoc Software Rejuvenation through intrusions.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.2
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• pp.51-61
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• 2019

In this study, interest in rural life of good natural environment rather than busy life is increasing as human life is extended by scientific and medical technology and society has improved. The reckless development of rural villages has caused social problems as the natural environment has been damaged, failing to function as a pleasant home. To address these problems, the government has implemented a rural village development project, but if the site is selected incorrectly, the residents may not be recruited, or applicants may lose their status, or the portion of the infrastructure building fee may increase. In order to prepare objective and clear assessment methods for the target site not to cause such problems, a layering analysis method (AHP: Analytic Method) was used to identify the current status of the rural village formation project, draw assessment items, and determine the importance of each item.

• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.255-266
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• 2009

In this study, a robust aerospace system design process for the aerospace system is developed by considering the uncertainties of user requirements, manufacturing errors, and operational environment variation. User requirements are analyzed and quantified by decision making models and system engineering methods to select alternative concepts which satisfies the various requirements. Robust design and optimization method is applied to derive the robust solution of the selected system. First, a variance of objective function is calculated, and a mean value and a variance of target value are determined by the deterministic design optimization results of the system. A robust optimum design formulation is then needed to derive the robust solution that minimizes the variance of the response and moves the mean values to the target value. It is applied to Very Light Jet (VLJ) aircraft to which much attention is paid recently in civil aerospace market.

• Environmental Engineering Research
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• v.23 no.2
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• pp.175-180
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• 2018

Box-Behnken Design (BBD) under response surface methodology (RSM) was implemented to optimization the operating parameters and assess the removal and recovery efficiencies of crude oil from contaminated soil using subcritical water extraction. The effects of temperature, extraction time and water flow rate were explored, and the results indicate that temperature has a great impact on crude oil removal and recovery. The correlation coefficients for oil removal ($R^2=0.74$) and recovery ($R^2=0.98$) suggest that the proposed quadratic model is useful. When setting the target removal and recovery (>99%), BBD-RSM determined the optimum condition to be a temperature of $250^{\circ}C$, extraction time of 120 min, and water flow rate of 1 mL/min. An experiment was carried out to confirm the results, with removal and recovery efficiencies of 99.69% and 87.33%, respectively. This result indicates that BBD is a suitable method to optimize the process variables for crude oil removal and recovery from contaminated soil.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1243-1252
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• 2005

A current trend of design methodologies is to make engineers objectify or automate the decision-making process. Numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, the Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, a design procedure for global robust optimization is developed based on the kriging and global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Robustness is determined by the DACE model to reduce real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. As the postprocess, the first order second-moment approximation method is applied to refine the robust optimum. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.