• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.6
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• pp.9-16
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• 2020

This study aims to assess the effects of specimen thickness (ST) on fatigue crack growth in the early stages of crack propagation and near failure in magnesium alloys. The analysis of variance (ANOVA) method was adopted because fatigue crack propagation in magnesium alloys exhibits statistical behavior. The equality of variance test and residual diagnostics were performed on the grown cracks to confirm the validity of ANOVA by verifying the normal distribution and mutual independence of the residuals and their homoscedasticity. ANOVA confirmed that ST heavily impacts crack growth; i.e., when ST is smaller, cracks grow faster in the early crack propagation stage and break more quickly before the formation of larger cracks. We found that ST significantly affects fatigue crack growth in the early crack propagation stage and near the failure stage in magnesium alloys. The regression model was also used to predict crack formation near the failure stage.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.8
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• pp.961-967
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• 2014

In onshore and offshore plant engineering, a broad use of pipe system have been achieved and accordingly related technologies has been developed especially in the field of flow control valves. The aim of this study is to suggest the quadruple offset butterfly valve for bi-directional applications which show equivalent operating torque characteristics of the triple offset butterfly valve. Seat design parameters for the quadruple offset butterfly valve are determined by the proposed method utilizing both ANOVA (analysis of variance) and the orthogonal array. Through additive model considering the effect of design parameters on seating torque, mean estimation is performed and thus its optimization results are verified by design of experiment results. The insight obtained from the present study is beneficial for valve design engineers to develop reliable and integrated design of the quadruple offset butterfly valve.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.862-865
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• 2000

The object of this paper is to evaluate the surface roughness using the experimental equation of surface roughness, which is developed in turning by an orthogonal array method. $L_9{3^4}$ orthogonal array method, one of fractional factorial design has been used to study effects of main cutting parameters such as cutting speed, feed rate and depth of cut, on the surface roughness. And the analysis of variance (ANOVA)-test has been used to check the significance of cutting parameters. Using the result of ANOVA-test, the experimental equation of surface roughness, which consists of only significant cutting parameter - feed rate, has been developed. The coefficient of determination of this equation is 0.962.

• Journal of Power System Engineering
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• v.17 no.6
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• pp.108-114
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• 2013

This paper describes process optimization for thermal-sprayed Co-based self-flux alloy coating by Taguchi method. Co-based self-flux alloy coatings were fabricated according to $L_9(3^4)$ orthogonal array using flame spray process. Hardness test and wear test were performed, the results were analyzed by analysis of variance(ANOVA) considering a multi response signal to noise ratio(MRSN). From the results of ANOVA, the optimal combination of the flame spray parameters on Co-based self-flux alloy coating could be predicted. The calculated hardness and wear rate of the coatings by ANOVA were found to be close to that of confirmation experimental result.

• 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.

• 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.

• 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.

• 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.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1628-1633
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• 2016

This paper derives an effective shape of the ferromagnetic pole pieces (low-speed rotor) for the reduction of transmission torque ripple in a magnetic-geared machine based on a Box-Behnken design (BBD). In particular, using a non-linear finite element method (FEM) based on 2-D numerical analysis, we conduct a numerical investigation and analysis between independent variables (selected by the BBD) and reaction variables. In addition, we derive a regression equation for reaction variables according to the independent variables by using multiple regression analysis and analysis of variance (ANOVA). We assess the validity of the optimized design by comparing characteristics of the optimized model derived from a response surface analysis and an initial model.