• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.597-598
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• 2012

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.293-294
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• 2012

• Explosives and Blasting
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• v.38 no.4
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• pp.16-25
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• 2020

Multi-setting smart-investigation of the ground and large uncharged hole boring (MSP) method to reduce the blast-induced vibration in a tunnel excavation is carried out over 50m of long-distance boring in a horizontal direction and thus has been accompanied by deviations in boring alignment because of the heavy and one-directional rotation of the rod. Therefore, the deviation has been adjusted through the boring machine's variable setting rely on the previous construction records and expert's experience. However, the geological characteristics, machine conditions, and inexperienced workers have caused significant deviation from the target alignment. The excessive deviation from the boring target may cause a delay in the construction schedule and economic losses. A deep learning-based prediction model has been developed to discover an ideal initial setting of the MSP machine. Dropout, early stopping, pre-training techniques have been employed to prevent overfitting in the training phase and, significantly improved the prediction results. These results showed the high possibility of developing the model to suggest the boring machine's optimum initial setting. We expect that optimized setting guidelines can be further developed through the continuous addition of the data and the additional consideration of the other factors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.44-51
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• 2019

Tunnel Boring Machine's Technology has depends mostly on imports, currently domestic technology development was proceeding. There are many technologies in this field, above all, the large-capacity motor drive technology required for excavation is one of the core technologies. In particular, when several large motors are simultaneously starting, there are many problems due to a large starting current at that time, and it is difficult to design and operate a power receiving facility. In this paper, A method of reducing the starting current by using the regenerative power generated by the deceleration of the motor has been studied. To verify this proposal, we designed the induction motor controller using CAE based power simulation tool and verified the results of the proposed method by applying the reduced model. As a result, it is possible to reduce the maximum starting current and shorten the start-up time. Moreover, even if several motors are connected to one bank, it is proved that the method can be efficiently operated by using the sequential braking / starting sequence. In the case of a power system in which a large capacity electric motor such as a tunnel excavation system is driven, the results of this study are expected to be a stable and effective method for solving the start-up current problem and designing the power receiving facility.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.147-153
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• 2012

The demands of the large scale machine tools, for instance, such as planomiller, turning machine, boring machine, NC machine, have been gradually increased in recent years. As the performances of machine tools and/or cutting tools are advanced, it is possible to perform high-speed and high-precision cutting works. The effective treatment of wet chip, which is discharged from cutting works, becomes very important problems. Therefore, this study is forced on the design of large scale machine tools using CATIA V5R18 and analysis of cutter, which is considered as essential equipment in large scale machine tools, using MSC.Nastran & MSC.Patran. Especially, the relations between tolerated load of cutter, driving horse power and rpm of driving shaft in chip processing system are investigated through analysis. As the results, the reliability of design could be improved by evaluating simulated numerical values, it showed that tolerated loads of supported part and edged part of cutter are 87,000N and 14,450N, respectively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.807-808
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• 2013

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.5
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• pp.641-656
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• 2019

In this study, a machine learning method was proposed to use in predicting optimal replacement period of shield TBM (Tunnel Boring Machine) disc cutter. To do this, a large dataset of ground condition, disc cutter replacement records and TBM excavation-related data, collected from a shield TBM tunnel site in Korea, was built and they were used to construct a disc cutter replacement period prediction model using a machine learning algorithm, SVM (Support Vector Machine) and to assess the performance of the model. The results showed that the performance of RBF (Radial Basis Function) SVM is the best among a total of three SVM classification functions (80% accuracy and 10% error rate on average). When compared between ground types, the more disc cutter replacement data existed, the better prediction results were obtained. From this results, it is expected that machine learning methods become very popularly used in practice in near future as more data is accumulated and the machine learning models continue to be fine-tuned.

• Tunnel and Underground Space
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• v.23 no.1
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• pp.54-65
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• 2013

The LCM (Linear Cutting Machine) test is one of the most powerful and reliable methods for designing the disc cutter and for predicting the TBM (Tunnel Boring Machine) performance. It has an advantage to predict the actual load on disc cutter from the laboratory test on the real-size large rock samples, however, it also has a disadvantage to transport and/or prepare the large rock samples and to need an extra cost for experiment. In order to overcome this problem, lots of numerical studies have been performed. In this study, the PFC3D (Particle Flow Code in 3 Dimension) has been adopted for numerical analysis on optimum cutter spacing and failure aspects of Busan Tuff. The optimum cutting condition with s/p ratio of 16 and minimum specific energy of $14MJ/m^3$ was derived from numerical analyses. The cutter spacing for Busan Tuff had the good agreements with those of LCM test and numerical analysis by finite element method.

• Geomechanics and Engineering
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• v.24 no.4
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• pp.323-335
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• 2021

This paper aims to estimate the range of the excavation damaged zone (EDZ) formation caused by the tunnel boring machine (TBM) advancement through dynamic three-dimensional large deformation finite element analysis. Large deformation analysis based on Coupled Eulerian-Lagrangian (CEL) analysis is used to accurately simulate the behavior during TBM excavation. The analysis model is verified based on numerous test results reported in the literature. The range of the formed EDZ will be suggested as a boundary under various conditions - different tunnel diameter, tunnel depth, and rock type. Moreover, evaluation of the integrity of the tunnel structure during excavation has been carried out. Based on the numerical results, the apparent boundary of the EDZ is shown to within the range of 0.7D (D: tunnel diameter) around the excavation surface. Through series of numerical computation, it is clear that for the rock of with higher rock mass rating (RMR) grade (close to 1st grade), the EDZ around the tunnel tends to increase. The size of the EDZ is found to be direct proportional to the tunnel diameter, whereas the depth of the tunnel is inversely proportional to the magnitude of the EDZ. However, the relationship between the formation of the EDZ and the stability of the tunnel was not found to be consistent. In case where the TBM excavation is carried out in hard rock or rock under high confinement (excavation under greater depth), large range of the EDZ may be formed, but less strain occurs along the excavation surface during excavation and is found to be more stable.

• Journal of the Korean Wood Science and Technology
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• v.21 no.2
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• pp.23-30
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• 1993

As the demand of wood furniture becomes diverse and large-sized, artificial intelligence technique is required to design the expert system which can promote the efficiency of the operations management in wood furniture industry. This study was carried out to develop the expert scheduler, which was applied to the scheduling in chair-manufacturing process to evaluate its validity. The expert scheduler could show the results of scheduling must faster than Gantt chart method with ease. Maximum tardiness in the current chair-manufacturing process could be reduced from 29 seconds to 5 seconds by the addition of a spindle sander, a 12 spindle universal boring machine, and a moulding sander to sanding, boring, and moulding process, respectively.