• Title, Summary, Keyword: Response Surface Model

Search Result 1,205, Processing Time 0.041 seconds

Optimization of Chassis Frame by Using D-Optimal Response Surface Model (D-Optimal 반응표면모델에 의한 섀시 프레임 최적설치)

  • Lee, Gwang-Gi;Gu, Ja-Gyeom;Lee, Tae-Hui
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.24 no.4
    • /
    • pp.894-900
    • /
    • 2000
  • Optimization of chassis frame is performed according to the minimization of eleven responses representing one total frame weight, three natural frequencies and seven strength limits of chassis frame that are analyzed by using each response surface model from D-optimal design of experiments. After each response surface model is constructed form D-optimal design and random orthogonal array, the main effect and sensitivity analyses are successfully carried out by using this approximated regression model and the optimal solutions are obtained by using a nonlinear programming method. The response surface models and the optimization algorithms are used together to obtain the optimal design of chassis frame. The eleven-polynomial response surface models of the thirteen frame members (design factors) are constructed by using D-optimal Design and the multi-disciplinary design optimization is also performed by applying dual response analysis.

Response Surface Methodology Using a Fullest Balanced Model: A Re-Analysis of a Dataset in the Korean Journal for Food Science of Animal Resources

  • Rheem, Sungsue;Rheem, Insoo;Oh, Sejong
    • Food Science of Animal Resources
    • /
    • v.37 no.1
    • /
    • pp.139-146
    • /
    • 2017
  • Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. In the analysis of response surface data, a second-order polynomial regression model is usually used. However, sometimes we encounter situations where the fit of the second-order model is poor. If the model fitted to the data has a poor fit including a lack of fit, the modeling and optimization results might not be accurate. In such a case, using a fullest balanced model, which has no lack of fit, can fix such problem, enhancing the accuracy of the response surface modeling and optimization. This article presents how to develop and use such a model for the better modeling and optimizing of the response through an illustrative re-analysis of a dataset in Park et al. (2014) published in the Korean Journal for Food Science of Animal Resources.

A Measure for Evaluating the Effect of Blocking in Response Surface Designs Using Cuboidal Regions (입방형 영역을 사용한 반응표면계획에서 블록효과를 평가하기 위한 측도)

  • 박상현;장대흥
    • Journal of the Korean Society for Quality Management
    • /
    • v.27 no.1
    • /
    • pp.59-79
    • /
    • 1999
  • The fitting of a response surface model and the subsequent exploration of the response surface are usually based on the assumption that the experimental runs are carried out under homogeneous conditions. This, however, may be quite often difficult to achieve in many experiments. To control such an extraneous source of variation, the response surface design should be arranged in several blocks within which homogeneity of conditions can be maintained. In this case, when fitting a response surface model, the least squares estimates of the model's parameters and the prediction variance will generally depend on how the response surface design is blocked. That is, the choice of a blocking arrangement for a response surface design can have a considerable effect on estimating the mean response and on the size of the prediction variance. In this paper, we propose a measure for evaluating the effect of blocking of response surface designs using cuboidal regions.

  • PDF

The Confidence Regions for the Logistic Response Surface Model

  • Cho, Tae-Kyoung
    • Journal of the Korean Society for Quality Management
    • /
    • v.25 no.2
    • /
    • pp.102-111
    • /
    • 1997
  • In this paper I discuss a method of constructing the confidence region for the logistic response surface model. The construction involves a, pp.ication of a general fitting procedure because the log odds is linear in its parameters. Estimation of parameters of the logistic response surface model can be accomplished by maximum likelihood, although this requires iterative computational method. Using the asymptotic results, asymptotic covariance of the estimators can be obtained. This can be used in the construction of confidence regions for the parameters and for the logistic response surface model.

  • PDF

Weight Minimization of a Double-Deck Train Carbody using Response Surface Method (반응표면 모델을 이용한 2층열차 차체의 경량화 설계)

  • Hwang Won-Ju;Kim Hyeong-Jin
    • Proceedings of the KSR Conference
    • /
    • /
    • pp.453-458
    • /
    • 2005
  • Weight minimization of double-deck train carbody is imperative to reduce cost and extend life-time of train. It is required to decide 36 thickness of aluminum extruded panels. However, the design variables are two many to tract. moreover, one execution of structural analysis of double-deck carbody is time-consuming. Therefore, we adopt approximation technique to save computational cost of optimization process. Response surface model is used to apporximate static response of double-deck carbody. To obtain plausible response surface model, orthogonal array is empolyed as design of experiment(DOE). Design improvement by approximate model-based optimization is described. Accuracy and efficiency of optimization by using response surface model are discussed.

  • PDF

Statistical Analysis and Prediction for Behaviors of Tracked Vehicle Traveling on Soft Soil Using Response Surface Methodology (반응표면법에 의한 연약지반 차량 거동의 통계적 분석 및 예측)

  • Lee Tae-Hee;Jung Jae-Jun;Hong Sup;Km Hyung-Woo;Choi Jong-Su
    • Journal of Ocean Engineering and Technology
    • /
    • v.20 no.3
    • /
    • pp.54-60
    • /
    • 2006
  • For optimal design of a deep-sea ocean mining collector system, based on self-propelled mining vehicle, it is imperative to develop and validate the dynamic model of a tracked vehicle traveling on soft deep seabed. The purpose of this paper is to evaluate the fidelity of the dynamic simulation model by means of response surface methodology. Various statistical techniques related to response surface methodology, such as outlier analysis, detection of interaction effect, analysis of variance, inference of the significance of design variables, and global sensitivity analysis, are examined. To obtain a plausible response surface model, maximum entropy sampling is adopted. From statistical analysis and prediction for dynamic responses of the tracked vehicle, conclusions will be drawn about the accuracy of the dynamic model and the performance of the response surface model.

A Study on Process Optimization Using Partial Least Squares Response Surface Function (편최소제곱 반응표면함수를 이용한 공정 최적화에 관한 연구)

  • Park, Sung-Hyun;Choi, Um-Moon;Park, Chang-Soon
    • Journal of the Korean Society for Quality Management
    • /
    • v.27 no.2
    • /
    • pp.237-250
    • /
    • 1999
  • Response surface analysis has been a popular tool conducted by engineers in many processes. In this paper, response surface function, named partial least squares response surface function is proposed. Partial least squares response surface function is a function of partial least squares components and the response surface modeling is used in either a first-order or a second-order model. Also, this approach will have the engineers be able to do the response surface modeling and the process optimization even when the number of experimental runs is less than the number of model parameters. This idea is applied to the nondesign data and an application of partial least squares response surface function to the process optimization is considered.

  • PDF

The Optimization of Bank Branches Efficiency by Means of Response Surface Method and Data Envelopment Analysis: A Case of Iran

  • Shadkam, Elham;Bijari, Mehdi
    • The Journal of Asian Finance, Economics, and Business
    • /
    • v.2 no.2
    • /
    • pp.13-18
    • /
    • 2015
  • In this paper the DRC model is presented for solving multi objective problem. The proposed model is a combination of data envelopment analysis, Cuckoo algorithm and the response surface method. Due to reasons like costs, time and irreversible damages, it is not possible to analyze each and every one of the proposed models in practice, so the simulation is used. Since the number of experiments for simulation process is high then the optimization has gone to practice and directs the simulation process. The response surface method is used as one of the approaches of simulation optimization. Furthermore, data envelopment analysis is used to consider several response surfaces as efficiency response surface. Then this efficiency response surface is solved by Cuckoo algorithms. The main advantage of DRC model is to make one efficiency response surface function instate of multi surface function for every output and also using the advantages of Cuckoo algorithms. In order to demonstrate the effectiveness of the proposed approach, the branches of Refah bank in Mashhad is analyzed and the results are presented.

Optimization of the Elastic Joint of Train Bogie Using by Response Surface Model (반응표면모델에 의한 철도 차량 대차의 탄성조인트 최적설계)

  • Park, Chan-Gyeong;Lee, Gwang-Gi
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.24 no.3
    • /
    • pp.661-666
    • /
    • 2000
  • Optimization of the elastic joint of train is performed according to the minimization of ten responses which represent driving safety and ride comfort of train and analyzed by using the each response se surface model from stochastic design of experiments. After the each response surface model is constructed, the main effect and sensitivity analyses are successfully performed by 2nd order approximated regression model as described in this paper. We can get the optimal solutions using by nonlinear programming method such as simplex or interval optimization algorithms. The response surface models and the optimization algorithms are used together to obtain the optimal design of the elastic joint of train. the ten 2nd order polynomial response surface models of the three translational stiffness of the elastic joint (design factors) are constructed by using CCD(Central Composite Design) and the multi-objective optimization is also performed by applying min-max and distance minimization techniques of relative target deviation.

Response Surface Methodology based on the D-optimal Design for Cell Gap Characteristic for Flexible Liquid Crystal Display (D-optimal Design을 이용한 Flexible 액정 디스플레이용 셀 갭 특성에 대한 반응 표면 분석)

  • Ko, Young-Don;Hwang, Jeoung-Yeon;Seo, Dae-Shik;Yun, Il-Gu
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
    • /
    • /
    • pp.510-513
    • /
    • 2004
  • This paper represents the response surface model for the cell gap on the flexible liquid crystal display (LCD) process. Using response surface methodology (RSM). D-optimal design is carried out to build the design space and the cell gap is characterized by the quadratic model. The statistical analysis is used to verify the response surface model. This modeling technique can predict the characteristics of the desired response, cell gap, varying with process conditions.

  • PDF