• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Structural Engineering and Mechanics
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• v.86 no.1
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• pp.119-137
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• 2023

The purpose of this research is to assess the performance of CBN and ceramic tools during the dry turning of gray cast iron EN GJL-350. During the turning operation, the variable machining parameters are cutting speed, feed rate, depth of cut and type of the cutting material. This contribution consists of two sections, the first one deals with the performance evaluation of four materials in terms of evolution of flank wear, surface roughness (2D and 3D) and cutting forces. The focus of the second section is on statistical analysis, followed by modeling and optimization. The experiments are conducted according to the Taguchi design L₃₂ and based on ANOVA approach to quantify the impact of input factors on the output parameters, namely, the surface roughness (Ra), the cutting force (Fz), the cutting power (Pc), specific cutting energy (Ecs). The RSM method was used to create prediction models of several technical factors (Ra, Fz, Pc, Ecs and MRR). Subsequently, the desirability function approach was used to achieve a multi-objective optimization that encompasses the output parameters simultaneously. The aim is to obtain optimal cutting regimes, following several cases of optimization often encountered in industry. The results found show that the CBN tool is the most efficient cutting material compared to the three ceramics. The optimal combination for the first case where the importance is the same for the different outputs is Vc=660 m/min, f=0.116 mm/rev, ap=0.232 mm and the material CBN. The optimization results have been verified by carrying out confirmation tests.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.325-335
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• 2003

Robust design is to identify appropriate settings of control factors that make the system's performance robust to to changes in the noise factors that represent the source of variation. 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 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 we use the combined array approach.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.65-75
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• 2003

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 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 we use the combined array approach.

• Structural Engineering and Mechanics
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• v.63 no.3
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• pp.303-315
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• 2017

This paper presents a review on getting a Weighted Multi-Objective Optimization (WMO) of Tuned Mass Damper (TMD) parameters based on Response Surface Methodology (RSM) coupled central composite design and Weighted Desirability Function (WDF) to attenuate the earthquake vibration of a jacket supported Offshore Wind Turbine (OWT). To optimize the parameters (stiffness and damping coefficient) of damper, the frequency ratio and damping ratio were considered as a design variable and the top displacement and frequency response were considered as objective functions. The optimization has been carried out under only El Centro earthquake results and after obtained the optimal parameters, more two earthquakes (California and Northridge) has been performed to investigate the performance of optimal damper. The obtained results also compared with the different conventional TMD's designed by Den Hartog's, Sadek et al.'s and Warburton's method. From the results, it was found that the optimal TMD based on RSM shows better response than the conventional damper. It is concluded that the proposed response model offers an efficient approach regarding the TMD optimization.

• Journal of Korean Society for Quality Management
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• v.39 no.3
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• pp.377-390
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• 2011

A common problem encountered in product or process design is the selection of optimal parameter levels which involves simultaneous consideration of multiple response variables. This is called a multiresponse problem. A multiresponse problem is solved through three major stages: data collection, model building, and optimization. Up to date, various methods have been proposed for the optimization, including the desirability function approach and loss function approach. In this paper, the existing studies in multiresponse optimization are reviewed and a future research direction is then proposed.

• Journal of Dairy Science and Biotechnology
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• v.41 no.2
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• pp.45-56
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• 2023

This review serves two purposes: first, to promote the use of improved optimization techniques in response surface methodology (RSM); and second, to enhance the optimum conditions for the fermentation of probiotics. According to research in dairy science, Lactiplantibacillus plantarum K79 is a candidate probiotic that has beneficial health effects, such as lowering blood pressure. The optimum conditions for L. plantarumK79 to produce peptides with angiotensin-converting enzyme (ACE) inhibitory activity were proposed, through modeling each of ACE inhibitory activity and pH as a function of the four factors that are skim milk concentration (%), incubation temperature (℃), incubation time (hours), and starter added amount (%). To estimate optimum conditions, the researchers employed a desirability-based multi-response optimization approach, utilizing third-order models with a nonsignificant lack of fit. The estimated optimum fermentation conditions for L. plantarum K79 were as follows: a skim milk concentration of 10.76%, an incubation temperature of 36.9℃, an incubation time of 23.76 hours, and a starter added amount of 0.098%. Under these conditions, the predicted ACE inhibitory activity was 91.047%, and the predicted pH was 4.6. These predicted values achieved the objectives of the multi-response optimization in this study.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.1
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• pp.22-31
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• 2008

The operating window method is a novel approach in quality improvement. But it has not received deserved attention in academic research. If a critical factor for competing failure modes can be identified, the probability of failure can be reduced by widening the operating window of this factor. Traditional SN ratio for the operating window advocated by Taguchi has a critical shortcoming, which has been derived under the assumption that failure rates are determined by the operating window factor only. A new metric for robustness is given for the operating window method, which has relaxed the restrictive assumption of Taguchi's SN ratio. And procedures for determining optimal conditions based on the new metric is presented. The effectiveness of the proposed approach over the traditional practice is tested with the aid of a wave soldering process.

• Structural Engineering and Mechanics
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• v.73 no.5
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• pp.501-513
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• 2020

In the present work, the optimization of machining parameters to achieve the desired technological parameters such as surface roughness, tool radial vibration and material removal rate have been carried out using response surface methodology (RSM). The hard turning of EN19 alloy steel with coated carbide (GC3015) cutting tools was studied. The main problem faced in manufacturer of hard and high precision components is the selection of optimum combination of cutting parameters for achieving required quality of surface finish with maximum production rate. This problem can be solved by development of mathematical model and execution of experiments by RSM. A face centred central composite design (FCCD), which comes under the RSM approach, with cutting parameters (cutting speed, feed rate and depth of cut) was used for statistical analysis. A second-order regression model were developed to correlate the cutting parameters with surface roughness, tool vibration and material removal rate. Consequently, numerical and graphical optimization were performed to obtain the most appropriate cutting parameters to produce the lowest surface roughness with minimal tool vibration and maximum material removal rate using desirability function approach. Finally, confirmation experiments were performed to verify the pertinence of the developed mathematical models.

• Transactions of the KSME C: Technology and Education
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• v.2 no.2
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• pp.113-124
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• 2014

In developing an aircraft, configuration determination and requirement proofing depend on flight test results. Since the flight tests require much time and high cost, systematic flight test planning and analysis are needed to reduce cost and development time. This paper presents a desirability function approach to present an integrative measure of vibration levels at important positions and suggests a fractional factorial design which is one of the experimental design methods to help perform systematic flight tests. A method to perform flight tests in stages is also suggested to further reduce the number of flight tests.