• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.6
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• pp.1-9
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• 2011

In this study, the specifications of the components of the vent vale were optimally determined in order to enhance the performance of the vent valve. Design objective was to minimize fuel leakage while satisfying the design constraints on the performance indices. To obtain the optimum solution based on real experiments, several design techniques available in PIAnO, a commercial PIDO tool, were used. First, an orthogonal array was used to generate training design points and then real experiments were performed to measure the experimental data at the training design points. Next, Kriging metamodels for the objective function and design constraints were generated using the experimental data. Finally, a genetic algorithm was employed to obtain the optimization results using the Kriging models. Fuel leakage of the optimized vent valve was found to be reduced by 95.8% compared to that of the initial one while satisfying all the design constraints.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.5
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• pp.62-67
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• 2010

Exhausting fossil fuel and increasing concern of air pollution have brought on the change of the focus of developing new vehicles from performance to fuel economy and emission. The gasoline engines adopting the naturally aspirated way use the throttle-body for engine load control. Therefore, its pumping loss increases more than that of the diesel engine, and also mostly operating in a partial load condition has bad influence on fuel economy and emission. In these days, the continuous variable valve timing system and variable induction system are adopted in order to improve fuel consumption and emission. In this study, we optimize the runner length and operate region of variable induction system to simulataneously improve the performance, fuel economy, and emission of gasoline engine with employing GT-Power as a CAE tool for engine analysis and PIAnO as PIDO tool for process integration and design optimization.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2006.06a
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• pp.81-85
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• 2006

The characteristic of sheet metal process is the few loss of material during process, the short processing time and the excellent price and strength. The sheet metal process with above characteristic is common used in industrial field, but in order to analysis irregular field problems the reliable and economical analysis method is demanded. Finite element method is very effective method to simulate the forming processes with good prediction of the deformation behaviour. Among Finite element method, The static-implicit finite element method is applied effectively to analyze real-size auto-body panel stamping processes, which include the forming stage. In this paper, it was focussed on the drawability factors on auto-body panel stamping by AUTOFORM with using tool planing alloy to reduce law price as well as high precision from Design Optimization of die. According to this study, the results of simulation will give engineers good information to access the Design Optimization of die.

• Microbiology and Biotechnology Letters
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• v.51 no.1
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• pp.69-82
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• 2023

Nitrilases are a hydrolase group of enzymes that catalyzes nitrile compounds and produce industrially important organic acids. The current objective is to optimize nitrilase production using statistical methods assisted with artificial intelligence (AI) tool from novel nitrile degrading isolate. A nitrile hydrolyzing bacteria Bacillus subtilis AGAB-2 (GenBank Ascension number- MW857547) was isolated from industrial effluent waste through an enrichment culture technique. The culture conditions were optimized by creating an orthogonal design with 7 variables to investigate the effect of the significant factors on nitrilase activity. On the basis of obtained data, an AI-driven support vector machine was used for the fitted regression, which yielded new sets of predicted responses with zero mean error and reduced root mean square error. The results of the above global optimization were regarded as the theoretical optimal function conditions. Nitrilase activity of 9832 ± 15.3 U/ml was obtained under optimized conditions, which is a 5.3-fold increase in compared to unoptimized (1822 ± 18.42 U/ml). The statistical optimization method involving Plackett Burman Design and Response surface methodology in combination with an AI tool created a better response prediction model with a significant improvement in enzyme production.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.2
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• pp.105-114
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• 2008

Natural element method (NEM) is quick in research activities by natural sciences and mechanical engineering fields, and from which good results are watched by various engineering fields and applied too. However no paper or research about the applied case has announced yet. Therefore on this paper, I will rediscover an optimum design and apply NEM into other fields with NEM for existing optimum design of mainly using FEM. NEM and genetic algorithm (GA) are applied to optimize a membrane with a center hole. The optimal design obtained by NEM is compared to the counterpart obtained by the finite element method (FEM). Result by NEM is found to be better than the result by FEM. NEM can be a feasible analysis tool in design optimization.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.143-148
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• 1999

Design optimization is performed to calculated the draw-bead force for satisfying the design re-quirements. For an analysis tool a rigid-plastic finite element method with modified membrane element is adopted. response surface methodology is utilized for constructing the approximation surface for the optimum searching of draw bead force in sheet metal forming process. the algorithm developed is ap-plied to a design of the draw bead forces in a deep drawing process. The results show that the design of process parameters is applicable in complex metal forming analysis. It is also noted that the present algo-rithm enhances the stable optimum solution with small times of optimization iteration.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.6
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• pp.125-131
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• 2005

The purpose of this study is an optimization of gate valve made by forging method instead of welding method. In this study, we propose an optimal shape design to improve the mechanical efficiency of gate valve. In order to optimize more efficiently and reliably, the meta-modeling technique has been developed to solve such a complex problems combined with the DACE (Design and Analysis of Computer Experiments). The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Also, we prove reliability of the DACE model's application to gate valve by computer simulations using FEM(Finite Element Method).

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.493-499
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• 2022

This paper presents the design for the kinematic structure of a system for an automatically moored 6000 ton autonomous ship in a port, and the process and results of optimal design for the link cross-sectional shape. We propose an automatic mooring system with a PPP type serial manipulator structure capable of linear motion in the XYZ axis. The mooring force applied by the mooring system was derived with dynamics simulation tool "ADAMS". The design goal is the minimization of the cross-sectional area of the link. Constrains include compressive stress and shear stress. The optimization problems were solved by using the sequential quadratic programing method implemented in the fmincon package. The shape of the cross section was assumed to be rectangle. Through future research, we plan to manufacture automatic mooring system for 6000ton class autonomous ship.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.3
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• pp.82-88
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• 2003

SGA(Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, ${\mu}GA$(Micro-Genetic Algorithm) has recently been developed. In this study, ${\mu}GA$ which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of ${\mu}GA$ were compared with those of SGA. Solutions of ${\mu}GA$ for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that ${\mu}GA$ is a suitable and very efficient optimization algorithm for structural design.

• Journal of the Optical Society of Korea
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• v.11 no.2
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• pp.55-58
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• 2007

We propose a systematic method for design of fiber-optic surface plasmon resonance (SPR) sensors. We used rigorous coupled wave analysis (RCWA) for analysis of the transmission spectrum, and the (1+1) evolution strategy (ES) was employed as an optimization tool. The simulation results show that the optimization method presented here is very useful in designing fiber-optic SPR sensor for strain and temperature measurement. This algorithm can be extended to another objective function with other weighting factors and optical parameters.