• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.73-80
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• 2005

Injection molding is the most widely used process for the industrial forming of plastic articles. During an injection molding process of composites, the fiber-matrix separation and fiber orientation are caused by the flow of molten polymer/fiber mixture. As a result, the product tends to be nonhomogeneous and anisotropic. Hence, it is very important to clarify the relations between separation orientation and injection molding conditions. So far, there is no research on the measurement of fiber orientation using image processing. In this study, the effects of fiber content ratio and molding condition on the fiber orientation-angle distributions are studied experimentally. Using the image processing method, the fiber orientation distribution of welding parts in injection-molded products is assessed. And the effects of fiber content and injection mold shapes on the fiber orientation in case of fiber reinforced polymeric composites are studied experimentally.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.4
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• pp.1-7
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• 2006

Injection molding is a very important industrial process for the manufacturing of plastics objects. During an injection molding process of composites, the fiber-matrix separation and fiber orientation are caused by the flow of molten polymer/fiber mixture. As a result, the product tends to be nonhomogeneous and anisotropic. Hence, it is very important to clarify the relations between separation orientation and injection molding conditions. So far, there is no research on the measurement of fiber orientation using image processing. In this study, the effects of fiber content ratio and molding condition on the fiber orientation-angle distributions are studied experimentally. Using the image processing method, the fiber orientation distribution of weld-line in injection-molded products is assessed. And the effects of fiber content and injection mold-gate conditions on the fiber orientation are also discussed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.30-35
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• 2008

As the quality of a weld feint is strongly influenced by process parameters during the welding process, an intelligent algorithms that can predict the bead geometry and shape to accomplish the desired mechanical properties of the weldment should be developed. This paper focuses on the development of mathematical models fur the selection of process parameters and the prediction of bead geometry(bead width, bead height and penetration) in robotic GMA(Gas Metal Arc) welding. Factorial design can be employed as a guide for optimization of process parameters. Three factors were incorporated into the factorial model: arc current, welding voltage and welding speed. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters. The results obtained show that developed mathematical models can be applied to estimate the effectiveness of process parameters for a given bead geometry, and a change of process parameters affects the bead width and bead height more strongly than penetration relatively.

• Journal of the Korea Institute of Building Construction
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• v.13 no.4
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• pp.372-381
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• 2013

In the construction industry, a great deal of research has been focused on productivity improvement because a minor change in labor productivity can often make the difference between a profit and a loss. This study shows how productivity measurement methods can be applied in practice, step by step, to analyze and identify potential problems both in productivity and methods performance for masonry work. A work sampling technique was conducted to determine the nature and extent of an observable activity as an aid to measuring overall performance. Also, a method productivity delay model was used to identify non-productivity in individual cycle times. From the work sampling technique, it was found that the masonry crew had a Labor Utilization Factor of 47.1%, and from the videotape analysis, it was found that the material and dumpster location need to be adjusted to reduce the travelling distance. We have found that efforts to improve the productivity of masonry work should be focused almost exclusively on machine and labor delays, based on the result from the method productivity delay model.

• Journal of the Korean Society for Heat Treatment
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• v.31 no.2
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• pp.56-62
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• 2018

Many researchers have studied on the precipitation control after solution treatment to improve the damping capacity without decreasing the strength. However, studies on the damping capacity and microstructure changes after deformation in the solid solution strengthening alloys were inadequate, such as the Al-Zn series magnesium alloys. Therefore, in order to investigate the effect of annealing condition on microstructure change and damping a capacity of AZ61 magnesium alloy. In this study, it was confirmed that the microstructure changes affect the damping capacity and hardness when annealed AZ61 alloy. AZ61 magnesium alloy was rolled at $400^{\circ}C$ with rolling reduction of 30%. These specimens were annealed at $350^{\circ}C$ to $450^{\circ}C$ for 30-180 minutes. After annealing, microstructure was observed by using optical microscopy, and damping capacity was measured by using internal friction measurement machine. Hardness was measured by Vickers hardness tester under a condition of 0.3 N. In this study, static recrystallization was observed regardless of the annealing conditions. In addition, uniform equiaxed grain structure was developed by annealing treatment. Hardness is decreased with increasing grain size. This is associated with Hall-Petch equation and static recrystallization. In case of damping capacity, bigger grain size show the larger damping capacity.

• Smart Structures and Systems
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• v.29 no.4
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• pp.599-616
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• 2022

Bridge displacement contains vital information for bridge condition and performance. Due to the limits of direct displacement measurement methods, the indirect displacement reconstruction methods based on the strain or acceleration data are also developed in engineering applications. There are still some deficiencies of the displacement reconstruction methods based on strain or acceleration in practice. This paper proposed a novel method based on long short-term memory (LSTM) networks to reconstruct the bridge dynamic displacements with the strain and acceleration data source. The LSTM networks with three hidden layers are utilized to map the relationships between the measured responses and the bridge displacement. To achieve the data fusion, the input strain and acceleration data need to be preprocessed by normalization and then the corresponding dynamic displacement responses can be reconstructed by the LSTM networks. In the numerical simulation, the errors of the displacement reconstruction are below 9% for different load cases, and the proposed method is robust when the input strain and acceleration data contains additive noise. The hyper-parameter effect is analyzed and the displacement reconstruction accuracies of different machine learning methods are compared. For experimental verification, the errors are below 6% for the simply supported beam and continuous beam cases. Both the numerical and experimental results indicate that the proposed data fusion method can accurately reconstruct the displacement.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.374-379
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• 2016

On-line process monitoring schemes are designed to give early warnings of process faults. In the artificial intelligence and machine learning fields, reliable approaches have been utilized, such as kernel-based nonlinear techniques. This work presents a kernel-based empirical monitoring scheme with a small sample problem. The measurement data of normal operations are easy to collect, whereas special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing the process monitoring performance. This can be achieved by the preprocessing of raw process data and eliminating unwanted variations of data. In this work, the performance of several monitoring schemes was demonstrated using three-dimensional batch process data. The results showed that the monitoring performance was improved significantly in terms of the detection success rate.

• Tunnel and Underground Space
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• v.12 no.4
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• pp.237-247
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• 2002

A total of 18 stress measurements were performed in the rock and rock-like blocks in the laboratory to estimate the influence of anisotropy in rock. Full scale overcoring equipment, which consists of a coring machine and a biaxial loading system by flat jacks, was developed to simulate the in-situ rock stress condition in the laboratory By comparing the isotropic analysis with the anisotropic analysis in measuring the stress, conclusions have been drawn as to the influence of anisotropy. The maximum difference between the isotropic and the anisotropic analysis was 34% and it was shown that the stress measurement considering the anisotropy was needed. To confirm the validity of the observed data, a diagnostic analysis of stress relief curve by overcoring was conducted using the three dimensional finite difference program, FLAC 3D.

• Smart Media Journal
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• v.9 no.3
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• pp.41-45
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• 2020

Recently, artificial intelligence (AI) and the Internet of things (IoT), which are represented by machine learning and deep learning among IT technologies related to the Fourth Industrial Revolution, are applied to our real life in various fields through various researches. In this paper, IoT and AI using object recognition technology are applied to classify clothing. For this purpose, the image dataset was taken using webcam and raspberry pi, and GoogLeNet, a convolutional neural network artificial intelligence network, was applied to transfer the photographed image data. The clothing image dataset was classified into two categories (shirtwaist, trousers): 900 clean images, 900 loss images, and total 1800 images. The classification measurement results showed that the accuracy of the clean clothing image was about 97.78%. In conclusion, the study confirmed the applicability of other objects using artificial intelligence networks on the Internet of Things based platform through the measurement results and the supplementation of more image data in the future.

• The Journal of Korean Academy of Prosthodontics
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• v.37 no.6
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• pp.767-775
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• 1999

The purpose of this study was to evaluate the effect of wattage and curing time on surface hard-ness, three-point bending strength and internal porosity of microwave curing denture base resin. Two sizes of resin specimens were made of Acron $MC^{(R)}\;;\;3.5{\times}10{\times}60mm$ for surface hardness and three-point bending strength measurement and $5{\times}12{\times}60mm$ for internal porosity measurement. They were cured by microwave energy at varing wattages(500W, 700W) and curing times(2min., 3min., 4min.) to determine if a certain wattage/curing time combination would improve physical properties. Surface hardness was measured with Vikers hardness tester, three-point bend-ing strength with universal testing machine and internal porosity was calculated by measuring the weight in air and in water. The results obtained were as follows: 1. There was no significant difference in percent porosity among experimental groups(p>0.05). 2. 500W/3min. group showed the higher surface hardness than 700W/2, 3, 4min. groups(p<0.05), and 700W/4 min. group showed the lower surface hardness than 500W/2, 3, 4min. groups(p<0.05), but there was no significant difference among others(p>0.05). 3. 500W/3min. group yielded the higher value of bending strength than 500W/2min., 700W/3, 4min. groups(p<0.05), but there was no significant difference among others(p>0.05).