• Title/Summary/Keyword: response surface

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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 Korean Society for Quality Management
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    • v.27 no.1
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    • pp.59-79
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    • 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.

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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 Korean Society for Quality Management
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    • v.27 no.2
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    • pp.237-250
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    • 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.

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Derivation of a Simplified Measure of Slope Rotatability for a Particular Class of Response Surface Designs

  • Kim, Hyuk Joo;Park, Sung Hyun;Kim, Tae-Sung
    • Communications for Statistical Applications and Methods
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    • v.11 no.3
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    • pp.563-574
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    • 2004
  • Slope rotatability of response surface designs is a desirable property when we are interested in estimating slopes of response surfaces. In this paper, we derive a simplified measure of slope rotatability from new viewpoints for response surface designs that are frequently used in response surface methodology.

Multi-Level Response Surface Approximation for Large-Scale Robust Design Optimization Problems (다층분석법을 이용한 대규모 파라미터 설계 최적화)

  • Kim, Young-Jin
    • Korean Management Science Review
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    • v.24 no.2
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    • pp.73-80
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    • 2007
  • Robust Design(RD) is a cost-effective methodology to determine the optimal settings of control factors that make a product performance insensitive to the influence of noise factors. To better facilitate the robust design optimization, a dual response surface approach, which models both the process mean and standard deviation as separate response surfaces, has been successfully accepted by researchers and practitioners. However, the construction of response surface approximations has been limited to problems with only a few variables, mainly due to an excessive number of experimental runs necessary to fit sufficiently accurate models. In this regard, an innovative response surface approach has been proposed to investigate robust design optimization problems with larger number of variables. Response surfaces for process mean and standard deviation are partitioned and estimated based on the multi-level approximation method, which may reduce the number of experimental runs necessary for fitting response surface models to a great extent. The applicability and usefulness of proposed approach have been demonstrated through an illustrative example.

Improved Response Surface Method Using Modified Selection Technique of Sampling Points (개선된 평가점 선정기법을 이용한 응답면기법)

  • 김상효;나성원;황학주
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 1993.10a
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    • pp.248-255
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    • 1993
  • Recently, due to the increasing attention to the structural safety under uncertain environments, many researches on the structural reliability analysis have been peformed. Some useful methods are available to evaluate performance reliability of structures with explicit limit states. However, for large structures, in which structural behaviors can be analyzed with finite element models and the limit states are only expressed implicitly, Monte-Carlo simulation method has been mainly used. However, Monte-Carlo simulation method spends too much computational time on repetitive structural analysis. Many alternative methods are suggested to reduce the computational work required in Monte-Carlo simulation. Response surface method is widely used to improve the efficiency of structural reliability analysis. Response surface method is based on the concept of approximating simple polynomial function of basic random variables for the limit state which is not easily expressed in explicit forms of design random variables. The response surface method has simple algorithm. However, the accuracy of results highly depends on how properly the stochastic characteristics of the original limit state has been represented by approximated function, In this study, an improved response surface method is proposed in which the sampling points for creating response surface are modified to represent the failure surface more adequately and the combined use of a linear response surface function and Rackwitz-Fiessler method has been employed. The method is found to be more effective and efficient than previous response surface methods. In addition more consistent convergence is achieved, Accuracy of the proposed method has been investigated through example.

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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
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    • v.2 no.2
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    • pp.13-18
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    • 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.

A Study on Response Surface Method Using the Vector Projection Technique (벡터투영법을 이용한 응답면기법에 관한 연구)

  • 김상효;나성원;김우곤
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 1994.10a
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    • pp.167-174
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    • 1994
  • In this study, an improved response surface method is proposed. By using gradient projection method, the sampling points for creating response surface are evaluated at the region close to the failure surface. This points are combined with linear response surface function and Rackwitz-Fiessler algorithm. Also, a method controlling the range of selecting sampling points considering the non-linearity of the limit states is proposed to reduce the error produced by approximating the non-linear limit state to linear response surface. With the examples the result of the proposed method is found to be more accurate and efficient than the previous response surface method.

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Probabilistic determination of initial cable forces of cable-stayed bridges under dead loads

  • Cheng, Jin;Xiao, Ru-Cheng;Jiang, Jian-Jing
    • Structural Engineering and Mechanics
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    • v.17 no.2
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    • pp.267-279
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    • 2004
  • This paper presents an improved Monte Carlo simulation for the probabilistic determination of initial cable forces of cable-stayed bridges under dead loads using the response surfaces method. A response surface (i.e. a quadratic response surface without cross-terms) is used to approximate structural response. The use of the response surface eliminates the need to perform a deterministic analysis in each simulation loop. In addition, use of the response surface requires fewer simulation loops than conventional Monte Carlo simulation. Thereby, the computation time is saved significantly. The statistics (e.g. mean value, standard deviation) of the structural response are calculated through conventional Monte Carlo simulation method. By using Monte Carlo simulation, it is possible to use the existing deterministic finite element code without modifying it. Probabilistic analysis of a truss demonstrates the proposed method' efficiency and accuracy; probabilistic determination of initial cable forces of a cable-stayed bridge under dead loads verifies the method's applicability.

Capabilities of stochastic response surface method and response surface method in reliability analysis

  • Jiang, Shui-Hua;Li, Dian-Qing;Zhou, Chuang-Bing;Zhang, Li-Min
    • Structural Engineering and Mechanics
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    • v.49 no.1
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    • pp.111-128
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    • 2014
  • The stochastic response surface method (SRSM) and the response surface method (RSM) are often used for structural reliability analysis, especially for reliability problems with implicit performance functions. This paper aims to compare these two methods in terms of fitting the performance function, accuracy and efficiency in estimating probability of failure as well as statistical moments of system output response. The computational procedures of two response surface methods are briefly introduced first. Then their capabilities are demonstrated and compared in detail through two examples. The results indicate that the probability of failure mainly reflects the accuracy of the response surface function (RSF) fitting the performance function in the vicinity of the design point, while the statistical moments of system output response reflect the accuracy of the RSF fitting the performance function in the entire space. In addition, the performance function can be well fitted by the SRSM with an optimal order polynomial chaos expansion both in the entire physical and in the independent standard normal spaces. However, it can be only well fitted by the RSM in the vicinity of the design point. For reliability problems involving random variables with approximate normal distributions, such as normal, lognormal, and Gumbel Max distributions, both the probability of failure and statistical moments of system output response can be accurately estimated by the SRSM, whereas the RSM can only produce the probability of failure with a reasonable accuracy.

Grinding Characteristic Evaluation and Development of Response Surface Models in Surface Grinding Process (평면연삭에서 가공특성 평가 및 반응표면모델 개발)

  • Kwak Jae-Seob
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.29 no.4 s.235
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    • pp.614-623
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    • 2005
  • This study aims to perform the characteristic evaluation of surface grinding for the STD11 material with experimental and analytic techniques based on the response surface model. The grinding force acting on the workpiece and the ground surface roughness were measured according to the change of grain size, table speed and depth of cut. The effect of spark-out on the grinding force and the surface roughness was also characterized. The frictional coefficient between workpiece and grinding wheel could be determined by the analysis of spark-out effect. From the experimental data, the second-order response surface models were developed to predict the grinding force and the surface roughness. Validation of the developed model was examined.