http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
다중반응표면최적화를 위한 공정능력함수법에서 최소치최대화 기준의 활용에 관한 연구
정인준 한국지식경영학회 2019 지식경영연구 Vol.20 No.3
Response surface methodology (RSM) is a group of statistical modeling and optimization methods to improve the quality of design systematically in the quality engineering field. Its final goal is to identify the optimal setting of input variables optimizing a response. RSM is a kind of knowledge management tool since it studies a manufacturing or service process and extracts an important knowledge about it. In a real problem of RSM, it is a quite frequent situation that considers multiple responses simultaneously. To date, many approaches are proposed for solving (i.e., optimizing) a multi-response problem: process capability function approach, desirability function approach, loss function approach, and so on. The process capability function approach first estimates the mean and standard deviation models of each response. Then, it derives an individual process capability function for each response. The overall process capability function is obtained by aggregating the individual process capability function. The optimal setting is given by maximizing the overall process capability function. The existing process capability function methods usually use the arithmetic mean or geometric mean as an aggregation operator. However, these operators do not guarantee the Pareto optimality of their solution. Moreover, they may bring out an unacceptable result in terms of individual process capability function values. In this paper, we propose a maximin-based process capability function method which uses a maximin criterion as an aggregation operator. The proposed method is illustrated through a well‐known multiresponse problem.
쌍대반응표면최적화를 위한 사후선호도반영법: TOPSIS를 활용한 최고선호해 선택
정인준 한국지식경영학회 2018 지식경영연구 Vol.19 No.2
Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)’s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the tradeoff between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.
정인준 한국품질경영학회 2012 품질경영학회지 Vol.40 No.4
Purpose: This paper aims at improving inefficiency of an existing posterior preference articulation method proposed for dual response surface optimization. The method generates a set of non-dominated solutions and then allows a decision maker (DM) to select the best solution among them through an interval selection strategy. Methods: This paper proposes an iterative posterior preference articulation method, which repeatedly generates the predetermined number of non-dominated solutions in an interval which becomes gradually narrower over rounds. Results: The existing method generates a good number of non-dominated solutions not used in the DM's selection process, while the proposed method generates the minimal number of non-dominated solutions necessitated in the selection process. Conclusion: The proposed method enables a satisfactory compromise solution to be achieved with minimal cognitive burden of the DM as well as with light computation load in generating non-dominated solutions.
정인준,김은정,유웅렬,나원진 한국복합재료학회 2022 Composites research Vol.35 No.4
본 연구에서는 저가 재료인 전분과 물 기반의 현탁액을 이용하여 과속방지턱에 응용 가능한 스마트 소재를 개발하고 물성을 평가하였다. 유변물성측정기를 이용하여 전단율에 따른 점도 및 전단력을 측정하여 전분 농도별 전단농화 발생 현상을 확인하였다. 물체의 낙하 시험과 5-25 km/h의 주행 속도로 충격 후 진동을 측정한 자전거 주행 시험을 통해 거시적인 전단농화현상을 확인하였고, 과속방지턱의 적용 가능성을 확인하였다. 점도 측정 결과, 초기에 전단담화 구간에 이어 전단농화가 발생하였고, 전단농화 현상을 유발하는 임계 변형률은 농도가 증가함에 따라 감소하였다. 또한 전분 농도 증가에 따라 점도와 전단력이 크게 증가하였다. 낙하시험과 자전거 주행시험 결과 현탁액이 단시간에 고체 상태로 바뀌었고 충격 에너지가 유체에 흡수되었다. 유체의 농도와 가하는 충격(속도)이 증가할수록 전단농화현상이 쉽게 발생하였다. 최종적으로 물과 전분 기반의 비뉴턴 유체로 5-25 km/h 범위에서 구동하는 스마트 과속방지턱 재료의 개발을 제안하였다.