• Korean Management Science Review
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• v.29 no.3
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• pp.81-89
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• 2012

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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• pp.481-496
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• 2012

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.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.3
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• pp.164-175
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• 2017

Dual response surface optimization (DRSO) attempts to optimize mean and variability of a process response variable using a response surface methodology. In general, mean and variability of the response variable are often in conflict. In such a case, the process engineer need to understand the tradeoffs between the mean and variability in order to obtain a satisfactory solution. Recently, a Posterior preference articulation approach to DRSO (P-DRSO) has been proposed. P-DRSO generates a number of non-dominated solutions and allows the process engineer to select the most preferred solution. By observing the non-dominated solutions, the DM can explore and better understand the trade-offs between the mean and variability. However, the non-dominated solutions generated by the existing P-DRSO is often incomprehensive and unevenly distributed which limits the practicability of the method. In this regard, we propose a modified P-DRSO using multiple objective genetic algorithms. The proposed method has an advantage in that it generates comprehensive and evenly distributed non-dominated solutions.

• Structural Engineering and Mechanics
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• v.69 no.6
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• pp.591-605
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• 2019

Investigations on seismic performance of eccentrically braced frames equipped with dual vertical links have received little attention. Therefore, the main goal of this paper is to describe design steps for such frames and evaluate nonlinear performance of this system according to the reliability analysis. In this study, four and eight story frame structures are analyzed and the response modification factors for different intensity and damage levels are derived in a matrix form based on a new approach. According to the obtained results, the system has high ductility and acceptable seismic performance. Moreover, it is concluded that using response modification factor equal to 8 in the design of system provides desirable seismic reliability under the design and maximum probable hazard levels. Due to desirable performance and significant advantages of the dual vertical links, this system can be used as a main lateral load bearing system, in addition to its application for rehabilitation of damaged structures.

• Wind and Structures
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• v.31 no.1
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• pp.59-73
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• 2020

Wind fragility analysis (WFA) of concrete chimney is often executed disregarding temperature effects. But combined wind and temperature effect is the most critical limit state to define the safety of a chimney. Hence, in this study, WFA of a 70 m tall RC chimney for combined wind and temperature effects is explored. The wind force time-history is generated by spectral representation method. The safety of chimney is assessed considering limit states of stress failure in concrete and steel. A moving-least-squares method based dual response surface method (DRSM) procedure is proposed in WFA to alleviate huge computational time requirement by the conventional direct Monte Carlo simulation (MCS) approach. The DRSM captures the record-to-record variation of wind force time-histories and uncertainty in system parameters. The proposed DRSM approach yields fragility curves which are in close conformity with the most accurate direct MCS approach within substantially less computational time. In this regard, the error by the single-level RSM and least-squares method based DRSM can be easily noted. The WFA results indicate that over temperature difference of 150℃, the temperature stress is so pronounced that the probability of failure is very high even at 30 m/s wind speed. However, below 100℃, wind governs the design.

• Industrial Engineering and Management Systems
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• v.7 no.3
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• pp.197-203
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• 2008

Many quality engineering practitioners continue to have a considerable interest in implementing the concept of response surface methodology to real situations. Recently, dual response surface approach is extensively studied and recognized as a powerful tool for robust design. However, existing methods do not consider the information provided by customers and design engineers. In this regard, this article proposes a flexible optimization model that incorporates that information via desirability function modeling. The optimization scheme and its modeling flexibility are demonstrated through an illustrative example by comparing the proposed model with existing ones.

• Knowledge Management Research
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• v.19 no.2
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• pp.151-162
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• 2018

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 trade-off 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.

• Journal of Korean Society for Quality Management
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• v.42 no.4
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• pp.685-700
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• 2014

Purpose: The purpose of this paper is to develop a systematic weighting procedure based on process capability indices method applying weighted mean squared error minimization (WMSE) approach to dual response surface optimization. Methods: The proposed procedure consists of 5 steps. Step 1 is to prepare the alternative vectors. Step 2 is to rank the vectors based on process capability indices in a pairwise manner. Step 3 is to transform the pairwise rankings into the inequalities between the corresponding WMSE values. Step 4 is to obtain the weight value by calculating the inequalities. Or, step 5 is to obtain the weight value by minimizing the total violation amount, in case there is no weight value in step 4. Results: The typical 4 process capability indices, namely, $C_p$, $C_{pk}$, $C_{pm}$, $C_{pmk}$ are utilized for the proposed procedure. Conclusion: The proposed procedure can provide a weight value in WMSE based on the objective quality performance criteria, not on the decision maker's subjective judgments or experiences.

• Steel and Composite Structures
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• v.45 no.4
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• pp.535-546
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• 2022

The memory response of nonlocal systematical formulation size-dependent coupling of viscoelastic deformation and thermal fields for piezoelectric materials with dual-phase lag heat conduction law is constructed. The method of the matrix exponential, which constitutes the basis of the state-space approach of modern control theory, is applied to the non-dimensional equations. The resulting formulation together with the Laplace transform technique is applied to solve a problem of a semi-infinite piezoelectric rod subjected to a continuous heat flux with constant time rates. The inversion of the Laplace transforms is carried out using a numerical approach. Some comparisons of the impacts of nonlocal parameters and time-delay constants for various forms of kernel functions on thermal spreads and thermo-viscoelastic response are illustrated graphically.