• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.482-489
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• 2011

In practical design process, designer needs to find an optimal solution by using full factorial discrete combination, rather than by using optimization algorithm considering continuous design variables. So, ANOVA(Analysis of Variance) based on an orthogonal array, i.e. Taguchi method, has been widely used in most parts of industry area. However, the Taguchi method is limited for the shape optimization by using CAE, because the multi-level and multi-objective optimization can't be carried out simultaneously. In this study, a combined method was proposed taking into account of multi-level computational orthogonal array and TOPSIS(Technique for Order preference by Similarity to Ideal Solution), which is known as a classical method of multiple attribute decision making and enables to solve various decision making or selection problems in an aspect of multi-objective optimization. The proposed method was applied to a case study of the multi-level shape optimization of lower arm used to automobile parts, and the design space was explored via an efficient application of the related CAE tools. The multi-level shape optimization was performed sequentially by applying both of the neural network model generated from seven-level four-factor computational orthogonal array and the TOPSIS. The weight and maximum stress of the lower arm, as the objective functions for the multi-level shape optimization, showed an improvement of 0.07% and 17.89%, respectively. In addition, the number of CAE carried out for the shape optimization was only 55 times in comparison to full factorial method necessary to 2,401 times.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2004.11a
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• pp.237-240
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• 2004

Sulfur Hexafluoride, SF6 is widely used for leak detection and as a gaseous dielectric in transformers, condensers and circuit breakers. SF6 gas is also effective as a cleanser in the semiconductor industry. This paper presents a numerical study of the sealing force of disk type seal in SF6 gas safety valve. The sealing force on the disk seal is analyzed by the FEM method based on the Taguch's experimental design technique. Disk seals in SF6 gas safety valve are designed with 9 design models based on 3 different contact length, compressive ratio and gas pressure. The calculated results of Cauchy stress and strain showed that the sealing characteristics of Teflon PTFE is more effective compared to that of FKM(Viton), which is related to the stiffness of the materials. And also, the contact length of the disk seal is important design parameter for sealing the SF6 gas leakage in the safety valve.

• Laser Solutions
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• v.2 no.3
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• pp.42-51
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• 1999

This research was conducted as a fundamental study to apply tailored blank welding technique into automotive production process. The materials used in this study were 2.0mm thickness low carbon steel sheets and 1.2mm Zn-coated low carbon steel sheets. To ensure the reproducibility and to consider various factors, experiments were. conducted by applying Taguchi experimental method with 6 factors. Every welding process was repeated 3 times to offset the effect of uncontrolled factors. Elongation and LDH(Limited Dome Height)were measured to evaluate formability of specimens and Optical microscopy, XRD, SEM, and EDS analysis were performed to observe the microstructures and to determine the solidification mode in the weld. The elongation of specimen welded with optimum condition was 83% of base metal, and LDH was 84% of base metal. In case of laser treated specimen where Zn coating was removed, elongation was 85% of base metal, and LDH was 85% of base metal. In fusion zone, phases were consisted of quasi-polygonal ferrite, banitic ferrite, and martensite.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.13-19
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• 2008

A Wire-woven Bulk Kagome (WBK) is the new truss type cellular metal fabricated by assembling the helical wires in six directions. The WBK seems to be promising with respect to morphology, fabrication cost, and raw materials. In this paper, first, the geometric and material properties are defined as the main design parameters of the WBK considering the fact that the failure of WBK is caused by buckling of truss elements. Taguchi approach was used as statistical design of experiment(DOE) technique for optimizing the design parameters in terms of maximizing the compressive strength. Normalized specific strength is constant regardless of slenderness ratio even if material properties changed, while it increases gradually as the strainhardening coefficient decreases. Compressive strength of WBK dominantly depends on the slenderness ratio rather than one of the wire diameter, the strut length. Specifically the failure of WBK under compression by elastic buckling of struts mainly depended on the slenderness ratio and elastic modulus. However the failure of WBK by plastic failed marginally depended on the slenderness ratio, yield stress, hardening and filler metal area.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.1-6
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• 2008

Biological and chemical sensors are the two most frequently used sensors to monitor the water resource. Chemical sensor is very accurate to pick up the types and to measure the concentration of the chemical substance. Drawback is that it works for just one type of chemical substance. Therefore a lot of expensive monitoring system needs to be installed to determine the safeness of the water, which costs too much expense. Biological sensor, on the contrary, can judge the degree of pollution of the water with just one monitoring system. However, it is not easy to figure out the type of contaminant with a biological sensor. In this study, an endeavor is made to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve (FIC) from a biological monitoring system. Wem-tox values are calculated from the amount of flourescence of contaminated and reference water. Curve fitting is executed to find the representative curve of the raw data of Wem-tox values. Then the curves are digitalized at the same interval to train the neural network model. Taguchi method is used to optimize the neural network model parameters. The optimized model shows a good capacity to figure out the toxicant from FIC.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.1
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• pp.40-45
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• 2012

The glass edge sealing is one of the vacuum glazing core manufacturing process and it needs the high reliability for the vacuum keeping. Conventionally, the glass edge sealing had been being researched by the method that pasted the flit on the glass edge part and bonded two sheets of glass. But this way has the defect that can't make tempered glass. In order to remedy it's faults, in this paper, the glass edge was sealed by using the hydrogen mixture gas torch within the furnace. The parameter having an effect on the glass edge sealing through the basic test was set. And the correlation of the thickness of the glass edge and parameter were analyzed through the design of experiment. By using the Taguchi method, the optimal process condition for the glass edge sealing was drawn and the validity was verified.

• Transactions of Materials Processing
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• v.19 no.2
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• pp.79-87
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• 2010

The Pipe bending process using high frequency local induction heating is an advanced technique to bend pipes with a small bending radius and a large diameter. Even though the pipe bending process is a quite widespread engineering practice, it depends heavily upon trial and error method by field engineers with several years of experience. So it is necessary to develop an integrated methodology for optimum design of the pipe bending process. During hot pipe bending using induction heating, outward wall thickness of a pipe is thinned due to tensile stress and the reduction of wall thickness is not allowed to exceed 12.5%. Taguchi method and dynamic reverse moment is proposed to maintain a reduction ratio of thickness within 12.5%, when D/t ratio is high. An application of the proposed approach was compared with those of the finite element analysis and has good in agreements.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.12
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• pp.116-123
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• 2001

An automated injection molding design methodology has been developed to optimize multiple quality attributes, which are usually in conflict with each other, in injection molded parts. For the optimization, commercial CAE simulation tools and optimization techniques are integrated into the methodology. To decal with the multiple objective problem the relative closeness computed in TOPSIS(Technique for Order Preference by Similarity to Ideal Solution) is used as a performance measurement index for optimization multiple part defects. To attain robustness against process variation, Taguchi's quadratic loss function is introduced in the TOPSIS. Also, the modified complex method is used as an optimization tool to optimize objective function. The verification of the developed design methodology was carried out on simulation software with an actual model. Applied to production this methodology will be useful to companies in reducing their product development time and enhancing their product quality.

• Journal of Electrochemical Science and Technology
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• v.10 no.3
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• pp.276-283
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• 2019

Nitinol is an alloy of nickel and titanium. Nitinol is one of the shape memory alloys(SMA) that are restored to a remembered form, changing the crystal structure at a given temperature. Because of these unique features, it is used in medical devices, high precision sensors, and aerospace industries. However, the conventional method of mechanical machining for nitinol has problems of thermal and residual stress after processing. Therefore, the electrochemical machining(ECM), which does not produce residual stress and thermal deformation, has emerged as an alternative processing technique. In addition, to replace the existing experimental planning methods, this study used deep neural network(DNN), which is the basis for AI. This method was shown to be more useful than conventional method of design of experiments(RSM, Taguchi, Regression) by applying deep neural network(DNN) to electrochemical machining(ECM) and comparing root mean square errors(RMSE). Comparison with actual experimental values has shown that DNN is a more useful method than conventional method. (DOE - RSM, Taguchi, Regression). The result of the machining was accurately and efficiently predicted by applying electrochemical machining(ECM) and deep neural network(DNN) to the shape memory alloy(SMA), which is a hard-mechinability material.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.59-64
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• 2002

Recently the applications of high speed machining are increasing due to the need for high performance and high accuracy machining and machining for difficult-to-cut material. However, the high speed machining also accompanies some problems: the product quality can be degraded due to the tool wear and the product cast can go up due to frequent tool replacements. Therefore, it is necessary to develop a technique of quantitative tool wear measurement to determine the precise timing for tool replacement. In this respect, this study suggests a reliable technique far the reduction of error components by developing a system using a CCD camera and an exclusive jig to be able to precisely measure the size of tool wear in flat end mill for high speed machining.