• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.927-928
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• 2013

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.30-35
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• 2008

As the quality of a weld feint is strongly influenced by process parameters during the welding process, an intelligent algorithms that can predict the bead geometry and shape to accomplish the desired mechanical properties of the weldment should be developed. This paper focuses on the development of mathematical models fur the selection of process parameters and the prediction of bead geometry(bead width, bead height and penetration) in robotic GMA(Gas Metal Arc) welding. Factorial design can be employed as a guide for optimization of process parameters. Three factors were incorporated into the factorial model: arc current, welding voltage and welding speed. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters. The results obtained show that developed mathematical models can be applied to estimate the effectiveness of process parameters for a given bead geometry, and a change of process parameters affects the bead width and bead height more strongly than penetration relatively.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.3
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• pp.241-245
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• 2001

In this paper, we present parameter optimization technique for GaAs/AlGaAs multiple quantum well avalanche photodiodes used for image capture mechanism in high-definition system. Even under flawless environment in semiconductor manufacturing process, random variation in process parameters can bring the fluctuation to device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. This paper will first use experimental design and neural networks to model the nonlinear relationship between device process parameters and device performance parameters. The derived model was then put into genetic algorithms to acquire optimized device process parameters. From the optimized technique, we can predict device performance before high-volume manufacturign, and also increase production efficiency.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.4
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• pp.13-19
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• 2004

This paper deals with the analysis of geometric error and the optimization of process parameters in surface grinding. Taguchi method which is one of the design of experiments has been introduced in achieving the aims. The process parameters were the grain size, the wheel speed, the depth of cut and the table speed. The effect of the process parameters on the geometric error was examined and an optimal set of the parameters was selected to minimize the geometric error within the controllable range of the used grinding machine. The reliability of the results was evaluated by the ANOVA.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.12
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• pp.14-20
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• 1998

Cutting fluid is a factor which has big effects on both machinability and environment in machining process. The loss of cutting fluids may be reduced by the optimization of machining parameters in process planning. In this study, the environmental impact of fluid loss is analyzed. The fluid loss models in milling process are constructed with the machining parameters. The models are utilized to obtain the optimal machining parameters to minimize the fluid loss. The factors with significant effects on the fluid loss are analyzed by ANOVA test. Finally, optimal parameters are suggested considering both machining economics and environmental impact. This study is expected to be used as a part of a framework for the environmental impact assessment of machining process.

• Transactions of Materials Processing
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• v.30 no.3
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• Journal of computational fluids engineering
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• v.21 no.3
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• pp.39-47
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• 2016

We conducted a multi-stage optimization to secure the desired performance of a centrifugal fan for home appliance in an early stage of product development. In optimization, the static pressure at the outlet of the fan is chosen as an objective function that is to be maximized, providing the required flow rate at the operating point of the fan. The optimization procedure begins with parameters for an initial baseline fan design. The baseline design is optimized by using a commercial optimization package. Accordingly, the corresponding blade models with a set of geometrical parameters are generated. Flow through a fan is simulated by solving the Reynolds-averaged Navier-Stokes equations. A multi-stage optimization scheme is employed to determine the family of optimum values for the parameters, leading to the pressure increase at the outlet of the fan. To validate the numerically obtained optimal design parameters, we fabricated the three types of fans using rapid prototyping and assessed the performance using a fan tester. Experimental results show that the design parameters at each stage satisfy the goal of optimization. The multi-stage optimization process turned out to be a useful tool in the development of a centrifugal fan.

• Structural Engineering and Mechanics
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• v.27 no.1
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• pp.49-61
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• 2007

This paper presents a modified and improved bi-directional evolutionary structural optimization (BESO) method for topology optimization. A sensitivity filter which has been used in other optimization methods is introduced into BESO so that the design solutions become mesh-independent. To improve the convergence of the optimization process, the sensitivity number considers its historical information. Numerical examples show the effectiveness of the modified BESO method in obtaining convergent and mesh-independent solutions. A study of the effects of various BESO parameters on the solution is then conducted to determine the appropriate values for these parameters.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.10a
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• pp.476-479
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• 2005

In this study, the process parameters in powder compaction are optimized for getting high relative densities. To find optimized parameters, the analytic models of powder compaction are firstly prepared by 2-dimensional rod arrays with random green densities using a quasi-random multi-particle array. Then, using finite element method, the changes in relative densities are analyzed by varying the size of the particle, the amplitude of cyclic compaction, and the coefficient of friction, which influence the relative density in cyclic compactions. After the analytic function of relative density associated process parameters are formulated by aid of the response surface method, the optimal conditions in powder compaction process are found by the grid search method.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1350-1354
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• 2007

As the importance of low power design is emphasized, power consumption became one of the standards that represent the performance of the system. The purpose of this study is to decide design variable that minimize power consumption for the oxygen concentrator in two bed-one compressor 8 step PSA process that has above 90% purity at 3lpm by using given constants and selected parameters. Setting selected parameters as cycle time and equalization time, optimization for PSA process in the oxygen concentrator is progressed. For this, we need to know the features and basic principals of PSA process and to deduce objective function of performance analysis. Validations for objective function and lots of experiments are needed too. By using the characteristic curve of the compressor and the pressure curve of the process for 1 cycle, objective function was set. After theoretical 2 dimensional optimized paths was obtained. And then, by experiment, theoretical optimized path was verified.