• Journal of Power System Engineering
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• v.17 no.6
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• pp.108-114
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• 2013

This paper describes process optimization for thermal-sprayed Co-based self-flux alloy coating by Taguchi method. Co-based self-flux alloy coatings were fabricated according to $L_9(3^4)$ orthogonal array using flame spray process. Hardness test and wear test were performed, the results were analyzed by analysis of variance(ANOVA) considering a multi response signal to noise ratio(MRSN). From the results of ANOVA, the optimal combination of the flame spray parameters on Co-based self-flux alloy coating could be predicted. The calculated hardness and wear rate of the coatings by ANOVA were found to be close to that of confirmation experimental result.

• Journal of the military operations research society of Korea
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• v.25 no.2
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• pp.62-72
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• 1999

Taguchi´s parameter design is to determine the optimal settings of design parameters of a product or a process such that the characteristics of a product exhibit small variabilities around their targer values. His analysis of the problem has focused only on a single characteristic or response. However the quality of most products is seldom defined by a characteristic, and is rather the composite of a great number of characteristics which are often interrelated and nearly always measured in a variety of units. The critical problem in dealing with multiple characteristics is how to compromise the conflict among the selected levels of the design parameters for each individual characteristic. In this paper, Methodology using SN ratio optimized by unvariate technique is proposed and a parameter design procedure to achieve the optimal compromise among several different response variables is developed. One existing case study is solved by the proposed method and the results are compared with ones by the sum of SN ratios, the expected weighted loss, the desirability functions, and EXTOPSIS model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.625-633
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• 2013

Multiple response surface optimization (MRSO) aims at finding a setting of input variables which simultaneously optimizes multiple responses. The minimization of mean squared error (MSE), which consists of the squared bias and variance terms, is an effective way to consider the location and dispersion effects of the responses in MRSO. This approach basically assumes that both the terms have an equal weight. However, they need to be weighted differently depending on a problem situation, for example, in case that they are not of the same importance. This paper proposes to use the weighted MSE (WMSE) criterion instead of the MSE criterion in MRSO to consider an unequal weight situation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.97-105
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• 2015

Multiresponse optimization (MRO) seeks to find the setting of input variables, which optimizes the multiple responses simultaneously. The approach of weighted mean squared error (WMSE) minimization for MRO imposes a different weight on the squared bias and variance, which are the two components of the mean squared error (MSE). To date, a weighted sum-based method has been proposed for WMSE minimization. On the other hand, this method has a limitation in that it cannot find the most preferred solution located in a nonconvex region in objective function space. This paper proposes a Tchebycheff metric-based method to overcome the limitations of the weighted sum-based method.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2013.04a
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• pp.41-41
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• 2013

화학산업이 발달함에 따라 화학 산업 현장에서 사용되고 있는 가연성물질들의 여러 가지 화재 및 폭발 위험이 증가되고 있으며, 화재 및 폭발의 예방 안전을 위한 화학공정설계 및 대처에 있어, 물질의 연소특성치 데이터를 필요로 한다. 인화점은 가연성 액체를 다루는 공정에서 안전한 취급과 사고방지를 위해 중요한 자료가 되며, 화재의 위험을 나타내는 지표로서 가연성액체의 액면 가까이서 인화할 때 필요한 증기를 발산하는 액체의 최저온도, 그리고 가연성증기의 포화증기압이 공기와 혼합기체의 폭발한계 하한농도와 같게 되는 온도로 정의한다. 본 연구에서는 2성분계 혼합물에 대해 인화점을 측정하였고, 측정값을 Raoult의 법칙과 다중회귀분석(Multiple Regression)을 도입하여 이론값과 비교 하였다. 따라서 본 연구에서 제시된 방법론에 의해 아직까지 밝혀지지 않은 순수가연성액체와 가연성혼합물의 인화점을 예측하는 방법을 전개하고자 하며, 실험에서 찾고자하는 자료에 도움을 주고자 한다. 본 연구를 바탕으로 혼합물의 인화점 예측 방법과 실험에서 측정한 자료를 화재 및 폭발을 방지하는 기초 자료로 제공하고자하며, 산업현장에서 취급되고 있고 위험성 평가가 되지 않은 보다 많은 물질에 대한 이론 및 실험 연구에 활용 되도록 하는데 그 목적이 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.7061-7070
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• 2015

Multi-Response Surface Optimization aims at finding the optimal setting of input variables considering multiple responses simultaneously. The Weighted Mean Squared Error (WMSE) minimization approach, which imposes a different weight on the two components of mean squared error, squared bias and variance, first obtains WMSE for each response and then minimizes all the WMSEs at once. Most of the methods proposed for the WMSE minimization approach to date are classified into the prior preference articulation approach, which requires that a decision maker (DM) provides his/her preference information a priori. However, it is quite difficult for the DM to provide such information in advance, because he/she cannot experience the relationships or conflicts among the responses. To overcome this limitation, this paper proposes a posterior preference articulation method to the WMSE minimization approach. The proposed method first generates all (or most) of the nondominated solutions without the DM's preference information. Then, the DM selects the best one from the set of nondominated solutions a posteriori. Its advantage is that it provides an opportunity for the DM to understand the tradeoffs in the entire set of nondominated solutions and effectively obtains the most preferred solution suitable for his/her preference structure.