• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.11
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• pp.69-75
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• 2019

Oil seals have a great effect on transmission performance and durability. In this study, the optimal rubber mix was derived using dispersion analysis to obtain excellent oil-seal rubber properties. ANOVA was performed twice. The factors were polymers, carbon, magnesium oxide, and calcium hydroxide, which were used as four factors in ANOVA. The response factors were four items (hardness, tensile strength, elongation rate, and compression deformation) obtained through an experiment with a confidence level of 95%. In the first ANOVA, 168 tests were performed, and in the secondary ANOVA, 24 physical tests were conducted using polymers and carbon derived from the primary ANOVA. Through the ANOVA, we derived a rubber mixture recipe.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.579-591
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• 2022

Functional data is collected in various fields. It is often necessary to test whether there are differences among groups of functional data. In this case, it is not appropriate to explain using the point-wise ANOVA method, and we should present not the point-wise result but the integrated result. Various studies on functional data analysis of variance have been proposed, and recently implemented those methods in the package fdANOVA of R. In this paper, I first explain ANOVA and multivariate ANOVA, then I will introduce various methods of analysis of variance for univariate and multivariate functional data recently proposed. I also describe how to use the R package fdANOVA. This package is used to test equality of weekly temperatures in Seoul and Busan through univariate functional data ANOVA, and to test equality of multivariate functional data corresponding to handwritten images using multivariate function data ANOVA.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.337-354
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• 2002

This paper derives three estimation methods for the between group variance component for serially correlated random model. To compare their estimation capability, three designs having different degree of unbalancedness are considered. The so-called empirical quantile dispersion graphs(EQDGs) used to compare estimation methods as well as designs. The proposed conditional ANOVA estimation is robust for design unbalancedness, however, ML estimation is preferred to the conditional AOVA and REML estimation regardless of design unbalancedness and correlation coefficient.

• Journal of Korean Ophthalmic Optics Society
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• v.9 no.2
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• pp.439-446
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• 2004

Many studies have reported that retinal defocus cause and increase refractive error specially myopia. Uncorrected astigmatism may be one factor of retinal defocus factors. To understand the relationship between myopia and astigmatism 62 college students were participated in this study. Spherical refractive error and astigmatism were measured using N-vision 5001 autorefractor (Shinnippon). Co-relations between spherical refractive error and astigmatism were high both in the with-the-rule astigmatism group(r=0.53; ANOVA F=32.40, N=87, P<0.05) and oblique astigmatism group (r=0.53ANOVA F=5.14, N=15, P<0.001). However it was very low (r=0.09; ANOVA F=0.18, N=22, P<0.001)in the against-the-rule stigmatism group. In the total group co-relation was also high (r=0.56: ANOVA F=77.80, N=173, P<0.001). Uncorrected astigmatism may cause and increase spherical refractive error.

• Journal of Power System Engineering
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• v.15 no.5
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• pp.72-76
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• 2011

In this work, the parameter optimization for thermal-sprayed Al/SiC composites have been designed by $L_9(3^4)$ orthogonal array and analysis of variance(ANOVA). Al/SiC composites were fabricated by flame spray process on steel substrate. The hardness of composites were measured using micro-vickers hardness tester, and these results were analyzed by ANOVA. The ANOVA results showed that the oxygen gas flow, powder feed rate and spray distance affect on the hardness of the Al/SiC composites. From the ANOVA results, the optimal combination of the flame spray parameters could be extracted. It was considered that experimental design using orthogonal array and ANOVA was efficient to determine optimal parameter of thermal-sprayed Al/SiC composites.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.975-982
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• 2005

The purpose of the Gage R&R study is to determine whether a measurement system is adequate for monitoring a process. If the measurement system variation is small relative to the process variation, then the measurement system is deemed 'adequate'. The sources of variation associated with the measurement system are compared using an analysis of variance (ANOVA) model, in general. A typical ANOVA model used in a standard Gage R&R study is the two-factor random effect model. Then, the ANOVA partitions the total variation into three categories: repeatability, reproducibility, part variation. However, if the process variation possesses the between group variation, within group variation, and within-part variation, these variations can cause the measurement system evaluation to provide misleading results. That is, in the standard Gage R&R study these variations affect the estimate of repeatability, reproducibility, or both. This paper presents a four-factor nested factorial ANOVA model which explicitly considers these variations for the Gage R&R study. The variance component estimates are derived by setting the EMS equations equal to the corresponding mean square from the ANOVA table and solving. And the proposed model is compared with the standard Gage R&R model.

• IE interfaces
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• v.18 no.4
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• pp.444-453
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• 2005

The purpose of the Gage R&R study is to determine whether a measurement system is adequate for monitoring a process. If the measurement system variation is small relative to the process variation, then the measurement system is deemed "adequate". The sources of variation associated with the measurement system are compared using an analysis of variance (ANOVA) model, in general. A typical ANOVA model used in a standard Gage R&R study is the two-factor random effect model. Then, the ANOVA partitions the total variation into three categories: repeatability, reproducibility, part variation. However, if the process variation possesses the between group variation, within group variation, and within part variation, these variations can cause the measurement system evaluation to provide misleading results. That is, in the standard Gage R&R study these variations affect the estimate of repeatability, reproducibility, or both. This paper presents a four-factor nested factorial ANOVA model which explicitly considers these variations for the Gage R&R study. The variance component estimators are derived by setting the EMS equations equal to the corresponding mean square from the ANOVA table and solving. And the proposed model is compared with the standard Gage R&R model.

• Journal of Ocean Engineering and Technology
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• v.24 no.1
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• pp.161-165
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• 2010

In the present study, process optimization for thermal-sprayed STS316 coating has been performed using $L_9(3^4)$ orthogonal array and analysis of variance (ANOVA). STS316 coatings were fabricated by flame spray process on steel substrate, and the hardness test and microstructure observation of the coatings were studied. The results of hardness test were analyzed by ANOVA. The ANOVA results showed that the spray distance had the greatest effect on hardness of the coating, on the other hands, the effects of oxygen gas flow and spray distance were ignorable. From these results, the optimal combination of the flame spray parameters could be derived, and confirmation experiment was carried out to verify these derived results. The calculated hardness of the coatings by ANOVA was found to approximately close to that of confirmation experimental result. Thus, it was considered that design of experiments using orthogonal array and ANOVA was effective for process optimization of thermal-sprayed STS316 coating.

• Journal of Power System Engineering
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• v.13 no.5
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• pp.89-94
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• 2009

In this work, the optimal process has been designed by $L_9(3^4)$ orthogonal array and analysis of variance(ANOVA) for thermal-sprayed Ni-based hard coating. Ni-based hard coatings were fabricated by flame spray process on steel substrate. Then, the hardness test and observation of microstructure of the coatings were performed. The results of hardness test were analyzed by ANOVA. The ANOVA results demonstrated that the acetylene gas flow had the greatest effect on hardness of the coatings. The oxygen gas flow was found to have a neglecting effect. From these results, the optimal combination of the flame spray parameters could be predicted. The calculated hardness of the coatings by ANOVA was found to lie close to that of confirmation experimental result. Thus, it was considered that design of experiments design using orthogonal array and ANOVA was useful to determine optimal process of thermal-sprayed Ni-based hard coating.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.3
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• pp.46-53
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• 2009

To find the working condition is one of the important factors in precision machining. In this study, we analyzed maximum working temperature by infra-red camera and surface roughness in side wall end milling using design of experiment (DOE): RSM(response surface methodology), ANOM(analysis of means) and ANOVA(analysis of variance) by table of orthogonal array. ANOM and ANOVA are well adapted to select sensitivity of design variables for maximum working temperature and surface roughness. The effective design variables and their levels should be determined using ANOM, ANOVA. RSM is presented 2nd order approximation polynomial of maximum working temperature and surface roughness is composed with design variables. Therefore, it is expected that the proposed procedure using design of experiment : table of orthogonal array, ANOM, ANOVA and RSM can be easily utilized to solve the problem of working condition.