• Journal of Platform Technology
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• v.8 no.2
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• pp.22-31
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• 2020

The construction and operation of industrial automation has been actively taking place from manufacturing plan to production for improving operational efficiency of production line and flexibility of equipment. ISO/TC184 is standardizing on operating methods that can share information of programmable device controllers such as PLC and IoT that are geographically distributed in the production line. In this study, the design of the cell controller consists of PLC group and IoT group that perform signals such as temperature sensors, gas sensors, and pressure sensors for thermal processes and corresponding motors or valves. The operation and analysis of the cell controller were performed using SDN(Software Defined Network) and the three types of process services performed in thermal processes are real-time transmission service, loss-sensitive large-capacity transmission service, and normal transmission service. The simulation result showed that the average loss rate improved by about 17% when the traffic increased before and after the application of the SDN route technique, and the delay in the real-time service was as low as 1 ms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.102-114
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• 2021

NDF (No Defect Found) is a phenomenon in which defects have been found in the manufacturing, operation and use of a product or facility, but phenomenon of defects is not reproduced in the subsequent investigation system or the cause of the defects cannot be identified. Recently, with the development of the fourth industrial revolution, convergence of hardware and software technologies in various fields is spreading to products such as aircraft, home appliances, and mobile devices, and the number of parts is increasing due to functional convergence. The application of such convergence technologies and the increase in the number of parts are major factors that lead to an increase in NDF phenomena. NDF phenomena have a significant negative impact on cost, reliability, and reliability for both manufacturers, service providers and operators. On the other hand, due to the nature of NDF phenomena such as difficult and intermittent cause identification and ambiguity in judgment, it is common to underestimate the cost of NDF or fail to take appropriate countermeasures in corporate management. Therefore, in this paper, we propose a methodology for estimating NDF costs by the PAF model which is a quality cost analysis model and ABC (Activity Based Costing) technique. The methodology of this study suggests a detailed procedure and the concept to accurately estimate the NDF costs, using ABC analysis, accounting system information, and IT system data. In addition case studies have validated the methodology. We think this could be a valid methodology to refer to when estimating the cost of other parts. And, it is meaningful to provide important judgment information in the decision-making process based on quality management and ultimately reduce NDF costs by visualizing them separately by major variable factors.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.996-1003
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• 2021

The purpose of this study is to evaluate the reliability of repeated measurements of several dental scanners. Blue-lighted scanners, white-light scanners and optical-type scanners are used in the study of repeatability in this study. The measurement results were calculated as root mean square (RMS) and the significance level was confirmed by applying the 1-way ANOVA statistical technique (𝛼=.05). According to the statistical analysis, the scanner with the largest RMS value was Z-opt group (38.2 ㎛. Next, D-white was 35.2 ㎛ and the group with the lowest RMS value was I-blue (34.1 ㎛). The comparison of RMS means between each group was not significant (p>.05). From this result, the blue light had the lowest error in repeatability of dental scanners, but no statistical significance. The conclusion of this study is that the study results are clinically acceptable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.641-648
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• 2022

This paper is a study to find out the factors affecting the defects that occur during the use of Electron Beam Melting (EBM), one of the 3D printer output methods, through data analysis. By referring to factors identified as major causes of defects in previous studies, log files occurring between processes were analyzed and related variables were extracted. In addition, focusing on the fact that the data is time series data, the concept of a window was introduced to compose variables including data from all three layers. The dependent variable is a binary classification problem with the presence or absence of defects, and due to the problem that the proportion of defect layers is low (about 4%), balanced training data were created through the SMOTE technique. For the analysis, I use XGBoost using Gridsearch CV, and evaluate the classification performance based on the confusion matrix. I conclude results of the stuy by analyzing the importance of variables through SHAP values.

• Steel and Composite Structures
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• v.43 no.1
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• pp.79-90
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• 2022

In this study, the dynamic behavior of functionally graded layered deep beams with viscoelastic core is investigated including the porosity effect. The material properties of functionally graded layers are assumed to vary continuously through thickness direction according to the power-law function. To investigate porosity effect in functionally graded layers, three different distribution models are considered. The viscoelastically cored deep beam is exposed to harmonic sinusoidal load. The composite beam is modeled based on plane stress assumption. The dynamic equations of motion of the composite beam are derived based on the Hamilton principle. Within the framework of the finite element method (FEM), 2D twelve -node plane element is exploited to discretize the space domain. The discretized finite element model is solved using the Newmark average acceleration technique. The validity of the developed procedure is demonstrated by comparing the obtained results and good agreement is detected. Parametric studies are conducted to demonstrate the applicability of the developed methodology to study and analyze the dynamic response of viscoelastically cored porous functionally graded deep beams. Effects of viscoelastic parameter, porosity parameter, graduation index on the dynamic behavior of porous functionally graded deep beams with viscoelastic core are investigated and discussed. Material damping and porosity have a significant effect on the forced vibration response under harmonic excitation force. Increasing the material viscosity parameters results in decreasing the vibrational amplitudes and increasing the vibration time period due to increasing damping effect. Obtained results are supportive for the design and manufacturing of such type of composite beam structures.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.4
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• pp.354-361
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• 2022

The development of digital technology is causing great changes in dentistry. This digital workflow combines various 3D data in the prosthetic treatment area for diagnosis and prosthetic manufacturing. The planned diagnosis and the fabrication of prosthesis in a virtual patient formed by synthesizing digital data can simulate the results of prosthetic treatment more intuitively than conventional methods, thereby increasing the predictability of aesthetic prosthetic treatment. In this case report, functionally and aesthetically satisfied clinical results were obtained by fabricating a fixed partial dentures through a digital workflow on congenital missing teeth in the maxillary anterior region.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.455-464
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• 2022

There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model.

• Advances in materials Research
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• v.11 no.4
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• pp.375-390
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• 2022

Carbon Fiber Reinforced Polymer (CFRP) composite provides outstanding mechanical capabilities and is therefore popular in the automotive and aerospace industries. Drilling is a common final production technique for composite laminates however, drilling high-strength composite laminates is extremely complex and challenging. The delamination of composites during the drilling at the entry and exit of the hole has a severe impact on the results of the holes surface and the material properties. The major goal of this research is to investigate contemporary industry solutions for drilling CFRP composites: enhanced edge geometries of cutting tools. This study examined the occurrence of delamination at the entry and exit of the hole during the drilling. For each of the 80°, 90°, and 118°point angle uncoated Brad point, Dagger, and Twist solid carbide drills, Taguchi design of experiments were undertaken. Cutting parameters included three variable cutting speeds (100-125-150 m/min) and feed rates (0.1-0.2-0.3 mm/rev). Brad point drills induced less delamination than dagger and twist drills, according to the research, and the best cutting parameters were found to be a combination of maximum cutting speed, minimum feed rate, and low drill point angle (V:150 m/min, f: 0.1 mm/rev, θ: 80°). The feed rate was determined to be the most efficient factor in preventing hole entry and exit delamination using analysis of variance (ANOVA). Regression analysis was used to create first-degree mathematical models for each cutting tool's entrance and exit delamination components. The results of optimization, mathematical modelling, and experimental tests are thought to be reasonably coherent based on the information obtained.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.233-239
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• 2022

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.291-309
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• 2023

This paper addresses the finite element modeling of functionally graded porous (FGP) beams for free vibration and buckling behaviour cases. The formulated finite element is based on simple and efficient higher order shear deformation theory. The key feature of this formulation is that it deals with Euler-Bernoulli beam theory with only three unknowns without requiring any shear correction factor. In fact, the presented two-noded beam element has three degrees of freedom per node, and the discrete model guarantees the interelement continuity by using both C⁰ and C¹ continuities for the displacement field and its first derivative shape functions, respectively. The weak form of the governing equations is obtained from the Hamilton principle of FGP beams to generate the elementary stiffness, geometric, and mass matrices. By deploying the isoparametric coordinate system, the derived elementary matrices are computed using the Gauss quadrature rule. To overcome the shear-locking phenomenon, the reduced integration technique is used for the shear strain energy. Furthermore, the effect of porosity distribution patterns on the free vibration and buckling behaviours of porous functionally graded beams in various parameters is investigated. The obtained results extend and improve those predicted previously by alternative existing theories, in which significant parameters such as material distribution, geometrical configuration, boundary conditions, and porosity distributions are considered and discussed in detailed numerical comparisons. Determining the impacts of these parameters on natural frequencies and critical buckling loads play an essential role in the manufacturing process of such materials and their related mechanical modeling in aerospace, nuclear, civil, and other structures.