• Journal of the Korean Geosynthetics Society
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• v.6 no.1
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• pp.39-43
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• 2007

Geocomposites of needle punched and spunbonded nonwovens having the reinforcement and drainage functions were manufactured by use of thermal bonding method. The physical properties (e.g. tensile, tear and bursting strength, permittivity) of these multi-layered nonwovens were dependent on the processing parameters of temperatures, pressures, bonding periods etc. - in manufacturing by use of thermal bonding method. Therefore, it is very meaningful to optimize the processing parameters and physical properties of the geocomposites by thermal bonding method. In this study, an algorithm has been developed to optimize the process of the geocomposites using an artificial neural network (ANN). Geocomposites were employed to examine the effects of manufacturing methods on the analysis results and the neural network simulations have been applied to predict the changes of the nonwovens performances by varying the processing parameters.

• Korean Journal of Optics and Photonics
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• v.27 no.6
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• pp.203-211
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• 2016

We have studied the hybrid method for reconstructing apodized chirped fiber Bragg gratings, using both an analytic estimation of grating parameters and an optimization algorithm. The Hilbert transform of the reflection spectrum was utilized to estimate grating parameters, and then the layer-peeling algorithm was used to obtain refined parameter values by the differential-evolution optimization process. Calculations for a fiber Bragg grating with wavelength chirp rate 2 nm/cm were obtained with an accuracy of $6{\times}10^{-5}nm/cm$ for the chirp rate and $3{\times}10^{-9}nm/cm$ for the index modulation, with much improved calculation speed and high reliability.

• Transactions of Materials Processing
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• v.25 no.1
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• pp.29-35
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• 2016

In the current study, finite element analysis was conducted for the press hemming of automotive panels in order to predict various hemming defects such as roll-in and turn down. The analysis used the exact punch movement based on the cam location and considered the sealer between the inner and outer panels with an artificial contact thickness. The analysis results quantify the hemming defects especially at the flange edge in the matching region of the head lamp. A design of experiments along with the parameter study was used to obtain the optimum process parameters for minimizing hemming defects. The optimization process selects the intake angle, bending angle of the hemming punch, and the flange height of the outer panel. The optimum design process determines an appropriate tool angle and flange height to reduce the roll-in and turn-down as compared to the initial design.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.5
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• pp.451-458
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• 2014

This study is a practical research for setting a process of making references of design decision and guidlines of limitation in the movement from the design development to the construction design by StrAuto. StrAuto, as a parametric modeling and optimization tool for building structure, enables a quantity of design cases to be analyzed automatically by changing parameters of sturctural properties. So the designer using StrAuto can check a lot of analysis data crossing thousands of cases, see which case is out of acceptable range, and make a decision for design and optimization. In this thesis, the application of StrAuto optimization process to the residence tower UIC project shows the practical applicability in the construction design and value engineering. StrAuto optimized ideally volume of core walls by 31.3% and lead the final revised model applied to the construction design to reduce volume by 18.1%. The significance of this research is the implementation of process that the designer can quickly review a number of cases and get a direction for construction design and optimization after design development.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.596-599
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• 2004

The robotic $CO_2$ welding is a manufacturing process to produce high quality joints for metal and it could provide a capability of full automation to enhance productivity. Despite the widespread use in the various manufacturing industries, the full automation of the robotic $CO_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this research, an attempt has been made to develop an intelligent algorithm to predict the weld geometry (top-bead width, top-bead height, back-bead width and back-bead height) as a function of key process parameters in the robotic $CO_2$welding. To achieve this above objective, Taguchi method was employed using five different process parameters (tip gap, gas flow rate, welding speed, arc current, welding voltage) as a guide for optimization of process parameters.

• Journal of Astronomy and Space Sciences
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• v.38 no.3
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• pp.157-164
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• 2021

We describe a method for the in-orbit calibration of body-mounted magnetometers based on the CHAOS-7 geomagnetic field model. The code is designed to find the true calibration parameters autonomously by using only the onboard magnetometer data and the corresponding CHAOS outputs. As the model output and satellite data have different coordinate systems, they are first transformed to a Star Tracker Coordinate (STC). Then, non-linear optimization processes are run to minimize the differences between the CHAOS-7 model and satellite data in the STC. The process finally searches out a suite of calibration parameters that can maximize the model-data agreement. These parameters include the instrument gain, offset, axis orthogonality, and Euler rotation matrices between the magnetometer frame and the STC. To validate the performance of the Python code, we first produce pseudo satellite data by convoluting CHAOS-7 model outputs with a prescribed set of the 'true' calibration parameters. Then, we let the code autonomously undistort the pseudo satellite data through optimization processes, which ultimately track down the initially prescribed calibration parameters. The reconstructed parameters are in good agreement with the prescribed (true) ones, which demonstrates that the code can be used for actual instrument data calibration. This study is performed using Python 3.8.5, NumPy 1.19.2, SciPy 1.6, AstroPy 4.2, SpacePy 0.2.1, and ChaosmagPy 0.5 including the CHAOS-7.6 geomagnetic field model. This code will be utilized for processing NextSat-1 and Small scale magNetospheric and Ionospheric Plasma Experiment (SNIPE) data in the future.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.699-702
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• 2000

The automation and control of robotic welding process is a very complex assignment because the system is affected by a number of variables which are very difficult to determine or predict in practice. Not only the optimization of the robotic welding process is considered from the point of view of the time and the cost of manufacturing. as well as quality of the weldment. the human factors of the production and many other factors must taken into consideration. hi order to determine the optimal parameters of robotic welding process, it is necessary to build a computer model representing all parameters influencing the welding process as well as the mutual dependence between them. This paper presents an approach to modeling the robotic welding process in which all parameters affecting the welding process are included using a neural network. A detailed analysis of the simulation results has been carried out to evaluate the proposed neural network model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.1
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• pp.117-125
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• 2013

Variations of springback in stamped parts are induced by the uncontrollable noises including the variation of incoming material properties, lubrication and other forming process parameters. Reduction of springback variation is very important during springback compensation processes on stamping dies and assembly processes. To reduce the variation of springback, a robust optimization methodology which uses complex method combined with orthogonal array is proposed. The proposed method is applied to the robust design of U-channel die for the reduction of side wall curl. It is shown that the drawbead and die radius of U-channel draw die can be effectively optimized by the proposed method.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.17-22
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• 2019

The method of electrical discharge machining (EDM), one of the processing methods based on non-traditional manufacturing procedures, is gaining increased popularity, since it does not require cutting tools and allows machining involving hard, brittle, thin and complex geometry. This present investigation details the determination of optimum process parameter to attain the better machining performance in EDM of AISI 4340 steel with graphite as a tool electrode. The experimental combinations are planned and analyzed by Taguchi's design of experiments approach. To predict the optimal condition, the experiments are conducted by using Taguchi's L27 orthogonal array. The influence of process variables such as discharge current, pulse on and pulse off time, voltage and spark speed were investigated to control the various desired performance measures such as surface roughness. Analysis of Variance (ANOVA) has to be performed to know the magnitude of each factor. Investigations indicate that the surface roughness is strongly depend on pulsed current.

• Proceedings of the IEEK Conference
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• 2000.06b
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• pp.234-237
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• 2000

In this paper, a method to implement new Quasi-SOI LDMOSFET is introduced and the electrical characteristics of the device are studied. Key process steps of the device are explained briefly. By performing process and device simulations, electrical characteristics of the device are investigated, with emphasis on the optimization of the tilt angle of p$\^$0/ channel region. The electrical properties of the Quasi-SOI device are compared with those of bulk and SOI devices with the same process parameters. Simulated device characteristics are threshold voltage, off-state leakage current, subthreshold swing, DIBL, output resistance, lattice temperature, I$\_$D/-V$\_$Ds/, and cut-off frequency.