• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.133-140
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• 2002

The object of this paper is to predict the surface roughness using the experiment equation of surface roughness, which is developed with a full factorial design in turning. $3^3$ full factorial design has been used to study main and interaction effects of main cutting parameters such as cutting speed, feed rate, and depth of cut, on surface roughness. For prediction of surface roughness, the arithmetic average (Ra) is used, and stepwise regression has been used to check the significance of all effects of cutting parameters. Using the result of these, the experimental equation of surface roughness, which consists of significant effects of cutting parameters, has been developed. The coefficient of determination of this equation is 0.9908. And the prediction ability of this equation was verified by additional experiments. The result of that, the coefficient of determination is 0.9718.

• Journal of Power System Engineering
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• v.22 no.1
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• pp.5-10
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• 2018

In order to employ factorial design on electronically-controlled diesel engine, effects of 5 factors on specific fuel consumption, nitrogen oxides and carbon monoxide were examined by fractional and full factorial design in this research. There were different results between fractional and full factorial design, then effect of variables as ambient condition and measurement of fuel consumption were confirmed. It was shown that ambient condition affected uniformly trend of nitrogen oxides and carbon monoxide. However, both ambient condition and measurement of fuel consumption had nothing to do with trend of specific fuel consumption and therefore it must be careful to employ factorial design on specific fuel consumption as response.

• Microbiology and Biotechnology Letters
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• v.48 no.1
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• pp.48-56
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• 2020

This study aimed to optimize the culture conditions to improve the crude biosurfactant activity of Bacillus pumilus IJ-1, using a 3³ full-factorial design of response surface methodology (RSM). It was found that submerged fermentation of B. pumilus improved the activity of the crude biosurfactant. The factors selected for optimization were NaCl concentration, temperature, and tryptone concentration. Response surface analysis revealed that the fitted quadratic model was statistically significant and produced an adequate R² value (0.9898) and a low probability value (<0.0001). The optimum level for each factor was found to be 0.567% (w/v) NaCl, 21.851℃ and 0.765% (w/v) tryptone, respectively. Crude biosurfactant activity was found to be most affected by tryptone concentration; then temperature, and finally NaCl concentration. Our results may potentially facilitate large-scale biosurfactant production from B. pumilus IJ-1.

• Proceedings of the KWS Conference
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• 2003.05a
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• pp.172-175
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. According to the conventional full factorial design, in order to find the optimal welding conditions, 16,384 experiments must be performed. The genetic algorithm however, found the near optimal welding conditions from less than 60 experiments.

• Computers and Concrete
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• v.12 no.5
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• pp.651-668
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• 2013

Concrete structures need repairing due to various reasons such as deteriorative effects, overloading, poor quality of workmanship and design failures. Cement based repair mortars are the most widely used solutions for concrete repair applications. Various factors may affect the bond strength between concrete substrate and repair mortars. In this paper, the effects of polymer additives, strength of the concrete substrate, surface roughness, surface wetness and aging on the bond between concrete substrate and repair mortar has been investigated. Full factorial experimental design is employed to investigate the main and interaction effects of these factors on the bond strength. Analysis of variance (ANOVA) under design of experiments (DOE) in Minitab 14 Statistical Software is used for the analysis. Results showed that the interaction bond strength is higher when the application surface is wet and strength of the concrete substrate is comparatively high. According to the results obtained from the analysis, the most effective repair mortar additive in terms of bonding efficiency was styrene butadiene rubber (SBR) within the investigated polymers and test conditions. This bonding ability improvement can be attributed to the self-flowing ability, high flexural strength and comparatively low air content of SBR modified repair mortars. On the other hand, styrene acrylate rubber (SAR) modified mortars was found incompatible with the concrete substrate.

• Steel and Composite Structures
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• v.21 no.6
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• pp.1327-1345
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• 2016

$Al_2O_3$/SiC particulate reinforced (Metal Matrix Composites) MMCs which were produced by using stir casting process, bending strength and hardening behaviour were obtained using an analysis of variance (ANOVA) technique that uses full factorial design. Factor variables and their ranges were: particle size $2-60{\mu}m$; the stirring speed 450 rpm, 500 rpm and the stirring temperature $620^{\circ}C$, $650^{\circ}C$. An empirical equation was derived from test results to describe the relationship between the test parameters. This model for the tensile strength of the hybrid composite materials with $R^2$ adj = 80% for the bending strength $R^2$ adj = 89% were generated from the data. The regression coefficients of this model quantify the tensile strength and bending strengths of the effects of each of the factors. The interactions of all three factors do not present significant percentage contributions on the tensile strength and bending strengths of hybrid composite materials. Analysis of the residuals versus was predicted the tensile strength and bending strengths show a normalized distribution and thereby confirms the suitability of this model. Particle size was found to have the strongest influence on the tensile strength and bending strength.

• Corrosion Science and Technology
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• v.21 no.4
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• pp.314-324
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• 2022

The objective of this investigation was to indentify major factors affecting surface roughness among various parameters of electropolishing process using the design of an experiment method (full factorial design) for UNS S31603. Factors selected included electrolyte composition ratio, applied current density, and electrolytic polishing time. They were compared through analysis of variance (ANOVA). Results of ANOVA revealed that all parameters could affect surface roughness, with the influence of electrolyte composition ratio being the highest. As a result of surface analysis after electropolishing, the specimen with the deepest surface damage was about 35 times greater than the condition with the smallest surface damage. The largest value of surface roughness after electropolishing was higher than that of mechanical polishing due to excessive processing. On the other hand, the smallest value of surface roughness after electropolishing was 0.159 ㎛, which was improved by more than 80% compared to the previous mechanical polishing. Taken all results together, it is the most appropriate to perform electrolytic polishing with a sulfuric acid and phosphoric acid ratio of 3:7, an applied current density of 300 mA/cm², and anelectrolytic polishing time of 5 minutes.

• IE interfaces
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• v.18 no.4
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• pp.465-476
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• 2005

Evolutionary operation is a useful tool for improving full-scale manufacturing process by systematically changing the levels of the process variables without jeopardizing the product. This paper presents a system design for the evolutionary operation software called 'evolutionary optimizer'. Evolutionary optimizer consists of four modules: factorial design, many variables, mixture, and mean/dispersion. Context diagram, data flow diagram and entity-relationship modelling are used to systematically design the evolutionary optimizer system.

• Journal of Microbiology and Biotechnology
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• v.21 no.12
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• pp.1250-1256
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• 2011

In this study, Bacillus licheniformis cells were immobilized by entrapment in calcium alginate beads and were used for production of alkaline protease by repeated batch process. In order to increase the stability of the beads, the immobilization procedure was optimized by statistical full factorial method, by which three factors including alginate type, calcium chloride concentration, and agitation speed were studied. Optimization of the enzyme production medium, by the Taguchi method, was also studied. The obtained results showed that optimization of the cell immobilization procedure and medium constituents significantly enhanced the production of alkaline protease. In comparison with the free-cell culture in pre-optimized medium, about 7.3-fold higher productivity was resulted after optimization of the overall procedure. Repeated batch mode of operation, using optimized conditions, resulted in continuous production of the alkaline protease for 13 batches in 19 days.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.6 no.4
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• pp.65-70
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• 2007

In this study, we analyzed about that after design form manufacture master pattern in Rapid Prototyping-RP through design program, processes to manufacture prototype using Vacuum Casting. In Rapid Prototyping-RP, there is an en-or by shrinkage of resin and, in Vacuum Casting, there is an error by shrinkage of silicon. To select condition which shrinkage become the minimum of each process, manufactured prototype after using Full Factorial Design of Design of Experiments, We could confirm shrinkage using reverse engineering and that result came into effect ANOVA 2-way. We applied errors of each process to master pattern, and then presented the method to improve flood control precision of prototype of Vacuum Casting.