• Journal of Integrative Natural Science
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• v.6 no.1
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• pp.39-45
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• 2013

Robust design is an approach to reducing performance variation of quality characteristic values in quality engineering. Taguchi has an idea that mean and variation are handled simultaneously to reduce the expected loss in products and processes. In the Taguchi parameter design, the product-array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined-array approach, was studied. In these studies, only single quality characteristic (or response) was considered. In this paper we propose how to simultaneously optimize for multiple quality characteristics (or multiresponse) using desirability function when we used the combined-array approach to assign control and noise factors.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.287-296
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• 1998

We often have multiple quality characteristics to develop, improve and optimize industrial processes and products. It is not easy to find optimal control factor setting when there are multiple quality characteristics, since there will be conflict among the selected levels of the control factors for each individual quality characteristic. In this paper we propose a desirability function approach and devise a scheme which gives a systematic way of solving multiple quality characteristic problems. A numerical example is provided.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.99-108
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• 2022

Today, as technology advances and market competition for products intensifies, the product design to improve customer satisfaction by accurately identifying customer needs is emerging as a very important issue for company. Accordingly, the customer-oriented or customer-centered design that maximizes customer satisfaction by grasping and analyzing customer requirements is in the spotlight as an important design theory. In this study, the customer-oriented design is defined as finding the optimal value of design variable with the maximum overall customer satisfaction while minimizing the difference in individual customer satisfaction responded to various customers from multiple product quality characteristics from the perspective of robust design. Therefore, this study presents a new method for modeling the customer preference structure as the different sets of desirability functions for multiple quality characteristics and proposes a new customer-oriented design approach by applying the desirability functions to Taguchi's robust design process to deal with multi-characteristic design problem. Finally, the proposed method is illustrated with the Kansei engineering design problem of wine glass.

• Computers and Concrete
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• v.17 no.5
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• pp.629-638
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• 2016

Optimization of the concrete mixture design is a process of search for a mixture for which the sum of the cost of the ingredients is the lowest, yet satisfying the required performance of concrete. In this study, a statistical model was carried out to model a cost effective optimal mix proportioning of high strength self-compacting concrete (HSSCC) using the Response Surface Methodology (RSM). The effect of five key mixture parameters such as water-binder ratio, cement content, fine aggregate percentage, fly ash content and superplasticizer content on the properties and performance of HSSCC like compressive strength, passing ability, segregation resistance and manufacturing cost were investigated. To demonstrate the responses of model in quadratic manner Central Composite Design (CCD) was chosen. The statistical model showed the adjusted correlation coefficient R2adj values were 92.55%, 93.49%, 92.33%, and 100% for each performance which establish the adequacy of the model. The optimum combination was determined to be $439.4kg/m^3$ cement content, 35.5% W/B ratio, 50.0% fine aggregate, $49.85kg/m^3$ fly ash, and $7.76kg/m^3$ superplasticizer within the interest region using desirability function. Finally, it is concluded that multiobjective optimization method based on desirability function of the proposed response model offers an efficient approach regarding the HSSCC mixture optimization.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.39-44
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• 2004

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation as well as distance-to-target of response and response variance. The variation of process parameters amplifies the variance of responses. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameters, this variability should be considered in the optimization problem. The proposed method is illustrated using a rubber product case.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.730-739
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• 2005

A common problem encountered in product or process design is the selection of optimal parameter levels which involve simultaneous consideration of multiresponse variables. A multiresponse problem is solved through three major stages: data collection, model building, and optimization. To date, various methods have been proposed for the optimization stage, including the desirability function approach and loss function approach. In this paper, we first propose a framework classifying the existing studies and then propose some promising directions for future research.

• Knowledge Management Research
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• v.20 no.3
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• pp.39-47
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• 2019

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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.3
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• pp.450-454
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• 2003

It is important to control the processing conditions to maximize the replication quality of UV-molded microstructure. In the present study, the tip radius anil surface roughness of V-groove structure were measured to quantify the replication quality. UV-curing dose and the applied pressure were experimentally selected as the governing Processing conditions that affect the replication quality of the UV-molded part. Finally. an experimental optimization technique combining central composite design and desirability function approach was used to maximize the replication quality of UV-molded structure.

• Computers and Concrete
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• v.23 no.6
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• pp.421-431
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• 2019

The alarming rate of depletion of natural stone based coarse aggregates is a cause of great concern. The coarse aggregates occupy nearly 60-70% by volume of concrete being produced. Research efforts are on to look for alternatives to stone based coarse aggregates from sustainability point of view. Response surface methodology (RSM) is adopted to study and address the effect of ferrochrome slag (FCS) replacement to coarse aggregate replacement in the ordinary Portland cement (OPC) based concretes. RSM involves three different factors (ground granulated blast furnace slag (GGBS) as binder, flyash (FA) as binder, and FCS as coarse aggregate), with three different levels (GGBS (0, 15, and 30%), FA (0, 15, and 30%) and FCS (0, 50, and 100%)). Experiments were carried out to measure the responses like, workability, density, and compressive strength of FCS based concretes. In order to optimize FCS replacement in the OPC based concretes, three different traditional optimization techniques were used (grey relational analysis (GRA), technique for order of preference by similarity (TOPSIS), and desirability function approach (DFA)). Traditional optimization techniques were accompanied with principal component analysis (PCA) to calculate the weightage of responses measured to arrive at the final ranking of replacement levels of GGBS, FA, and FCS in OPC based concretes. Hybrid combination of PCA-TOPSIS technique is found to be significant when compared to other techniques used. 30% GGBS and 50% FCS replacement in OPC based concrete was arrived at, to be optimal.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.685-696
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• 2001

Robust design is an approach to reducing performance variation of response values in products and processes. In the Taguchl parameter design, the product-array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined-array approach, was suggested by Welch et. al. (1990) and studied by others. In these studies, only single response variable was considered. We propose how to simultaneously optimize multiple responses when there are correlations among responses, and when we use the combined-array approach to assign control and noise factors. An example is illustrated to show the difference between the Taguchi's product-array approach and the combined-array approach.