• Journal of Aerospace System Engineering
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• v.8 no.4
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• pp.18-23
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• 2014

In preliminary design phase, the wing geometry of the civil aircraft was determined using the empirical equation and historical data. To make wing geometry more aerodynamically efficient, an aerodynamic shape optimization was conducted. For this purpose the parametric modeling, high fidelity CFD analysis and metamodel-based optimal design technique were adopted. The parametric modeling got the design process to achieve the improvement by generating the configuration outputs easily for the major design variables. The optimal design equations were formularized as the type of the multi-objective functions considering low/high speed and lift/drag coefficient. The optimal solution was explored with the help of the kriging metamodel and the desirability function, therefore the optimal wing planform was sought to be excellent at both low and high speed region. Additionally the optimal wing planform was validated that it was excellent not only at the specific AOA, but also all over the range of AOA.

• Applied Chemistry for Engineering
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• v.3 no.4
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• pp.678-685
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• 1992

The effects of functional monomers on the pressure sensitive adhesive proporties were studied. Acrylic acid and other monomers were copolymerized by radical solution polymerization and their properties were measured. The desirability function methodology was applied to obtain optimum pressure sensitive adhesive properties. Acrylic acid showed more effective than acrylamide on peel strength increase. On the other hand acrylamide showed more effective than acrylic acid on tack decrease. The optimum monomer ratio of the acrylic pressure sensitive adhesive recipe containing n-butylacrylate 81.7 mole%, acrylic acid 8.0 mole%, acrylamide 2.1 mole% and vinylacetate 8.2 mole% was obtained to result from the statistical analysis with the desirability function methodology. The estimated regression equation of desirability function(D) is as follows: $D=.857+.072X_1-.114X_2-.027X_3-.126X_1{^2}-.046X_1{\cdot}X_2-.063X_1{\cdot}X_3-.152X_2{^2}+.027X2{\cdot}X_3-.120X_3{^2}$ $X_1$:coded acylic acid, $X_2$:coded acylamide, $X_3$:coded vinylacetate

• The Journal of Society for e-Business Studies
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• v.15 no.1
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• pp.157-171
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• 2010

To effectively maintain manufacturing collaboration, the development of effective performance measurements for the collaboration is required. Most existing key performance indicators however were developed to measure the performances of internal activities or outsourcing of a company. For that reason, it is necessary to devise new key performance indicators that the partners participating in the collaboration can arrange and compromise with each other to reflect their common goals. In this paper, we propose collaborative Key Performance Indicators(cKPIs), which is used to measure the collaboration work of multiple manufacturing partners on the basis of the Supply Chain Operations Reference(SCOR) model. Also, a modified Sigmoid function is devised as a desirability function to reflect the characteristics of Service Level Agreement(SLA) between two partners. The proposed indicators and the desirability functions can be utilized to perform and maintain the successful collaboration by providing a way to the quantitative measurement.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.145-161
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• 2021

Dynamic compaction of Aluminum powder using gas detonation forming technique was investigated. The experiments were carried out on four different conditions of total pre-detonation pressure. The effects of the initial powder mass and grain particle size on the green density and strength of compacted specimens were investigated. The relationships between the mentioned powder design parameters and the final features of specimens were characterized using Response Surface Methodology (RSM). Artificial Neural Network (ANN) models using the Group Method of Data Handling (GMDH) algorithm were also developed to predict the green density and green strength of compacted specimens. Furthermore, the desirability function was employed for multi-objective optimization purposes. The obtained optimal solutions were verified with three new experiments and ANN models. The obtained experimental results corresponding to the best optimal setting with the desirability of 1 are 2714 kg·m-3 and 21.5 MPa for the green density and green strength, respectively, which are very close to the predicted values.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.3
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• pp.198-207
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• 2012

In product or process parameter design, the case of multiple performance characteristics appears more commonly than that of a single characteristic. Numerous methods have been developed to deal with such multi-characteristic parameter design (MCPD) problems. Among these, this paper considers three representative methods, which are respectively based on the desirability function (DF), grey relational analysis (GRA), and principal component analysis (PCA). These three methods are then used to solve the MCPD problems in ten case studies reported in the literature. The performance of each method is evaluated for various combinations of its algorithmic parameters and alternatives. Relative performances of the three methods are then compared in terms of the significance of a design parameter and the overall performance value corresponding to the compromise optimal design condition identified by each method. Although no method is significantly inferior to others for the data sets considered, the GRA-based and PCA-based methods perform slightly better than the DF-based method. Besides, for the PCA-based method, the compromise optimal design condition depends much on which alternative is adopted while, for the GRA-based method, it is almost independent of the algorithmic parameter, and therefore, the difficulty involved in selecting an appropriate algorithmic parameter value can be alleviated.

• 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.

• IE interfaces
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• v.23 no.2
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• pp.118-125
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• 2010

The determination of a regression model is important in using statistical designs of experiments. Generally, the exact regression model is not known, and experimenters suppose that a certain model form will be fit. Then an experimental design suitable for that predetermined model form is selected and the experiment is conducted. However, the initially chosen regression model may not be correct, and this can result in undesirable statistical properties. We develop model-robust experimental designs that have stable prediction variance for a family of candidate regression models over a cuboidal region by using genetic algorithms and the desirability function method. We then compare the stability of prediction variance of model-robust experimental designs with those of the 3-level face centered cube. These model-robust experimental designs have moderately high G-efficiencies for all candidate models that the experimenter may potentially wish to fit, and outperform the cuboidal design for the second-order model. The G-efficiencies are provided for the model-robust experimental designs and the face centered cube.

• Food Engineering Progress
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• v.21 no.3
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• pp.256-266
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• 2017

This study was designed to optimize ethanol extraction process of unfertilized corn silk (UCS) to maximize phytochemical contents and bioactivities. The response surface methodology (RSM) with central composite design (CCD) was employed to obtain the optimal extraction conditions. The influence of ethanol concentration, extraction temperature and extraction time on total polyphenol contents, total flavonoid contents, maysin contents, 2,2-diphenyl-1-picrylhydrazyl(DPPH) radical scavenging activities and tyrosinase inhibition were analyzed. For all dependable variables, the most significant factor was ethanol concentration followed by extraction temperature and extraction time. The following optimum conditions were determined by simultaneous optimization of several responses with the Derringer's desirability function using the numerical optimization function of the Design-Expert program: ethanol concentration 80.45%, extraction temperature $53.49^{\circ}C$, and extraction time 4.95 h. Under these conditions, the predicted values of total polyphenol contents, total flavonoid contents, maysin contents, DPPH radical scavenging activity and tyrosinase inhibition were $2758.74{\mu}g\;GAE/g$ dried sample, $1520.81{\mu}g\;QUE/g$ dried sample, 810.26 mg/100g dried sample, 56.86% and 43.49%, respectively, and the overall desirability (D) was 0.74.

• 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.

• 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.