• Tribology and Lubricants
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• v.20 no.1
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• pp.21-26
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• 2004

The average frictional coefficient was used generally to analyze the working process of the lubricated machine parts. But it is difficult of taking the correct frictional coefficient because the average frictional coefficient of it is progressed always unstably with a large amplitude on working condition. If the correct analysis of frictional coefficient on working condition for the lubricated machine parts can be possible, it can be effect on condition diagnosis of lubricated machine system. This study was carried out to get the optimum working condition of the hydraulic actuator parts using statistical techniques. From this result, it has the lowest friction coefficient in case of applied load of 3 kg and sliding speed of 120 mm/sec.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.2
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• pp.202-209
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• 2005

A new effective thick-layered RP process, Transfer-type Variable Lamination Manufacturing using expandable polystyrene foam (VLM-ST) has been developed with thick layers and sloped surfaces. VLM-ST has the innate advantages by virtue of its working principle: high building speed, low cost for introduction and maintenance of VLM-ST apparatus, little staircase surface irregularities of parts. Despite these advantages in VLM-ST, the surface roughness of VLM-ST parts is still inadequate to be used as RP master patterns for rapid tooling (RT). This paper describes the systematic and effective methodology to remove the laminated pattern and improve the surface roughness for VLM-ST parts. From the results of surface finishing of VLM-ST parts, it can be seen that the laminated pattern is completely removed and the surface characteristics such as surface roughness, surface hardness, and paintability are improved.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.10a
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• pp.128-131
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• 2007

It is very important that there are saving resource and energy in the future as well as in these day. Weight saving of structural parts, which are formed by extrusion, plays a key role in manufacturing field. Especially these aluminum parts used in the car need other processes to vary the cross section in the axial direction. Thus, applications of these parts are limited by high cost. if the cross section of the parts is variable by only extrusion, application of extruded aluminum parts will more increase. Therefore, a new CNC extruder which can control the section area of a car part was invented the nation's first. Using the extrusion machine, the experiment and FE analysis were performed during the varied section extrusion process.

• Journal of the Korea Safety Management & Science
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• v.9 no.4
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• pp.121-127
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• 2007

CSP(Concurrent Spare Parts) is supplied with the procurement of new equipment or weapon system and is used to sustain the equipment without resupply during the initial coverage period. This study is concerned with a problem of determining the near optimal inventory level of the spare parts, especially Concurrent Spare Parts. For this, we utilize the mixed periodic and continuous review polices considering the CSP and (r,Q) Policies concurrently in a two-echelon distribution system. We propose the mathematical model to minimize the total cost which is composed with ordering cost, purchasing cost, holding cost, and stickout cost. If the mixed policy is compared to other policies(CSP, (r,Q)), the proposed methodology performs well and is best policy in the equipment maintenance expenses.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.1-8
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• 2013

Remanufacturing of automobile parts is important for increasing the recycling resources. ECU(Electronic Control Unit) systems are becoming an increasingly important part of an automobile. But production company are reported in the ECU remanufacturing industry of domestic automobile parts due to a lack of remanufacturing technology and marketability. This paper introduces the ECU of remanufacturing, also grasps the structure and the analyzes failure cause that is external factors and fault in the characteristic design and steps in the Independent ECU remanufacturing process. The ECU manufacturing industry of the US was examined. The results in this paper could be used as the system information for further remanufacturing industry of domestic automobile parts.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.10
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• pp.104-109
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• 2019

Reverse engineering techniques using 3D scanners and 3D printing technologies are being used in various industries. In this paper, the three-dimensional model is designed for automotive hub parts through 3D scanning and reverse engineering, and the design of hub parts is intended to be printed on FDM-style 3D printers to measure and analyze the dimensions of hub parts designed for reverse design and 3D printed hub parts. Experimental result have shown that the dimensions of 3D printed hub parts are small compared to those of the reverse-engineered dimensions, which are due to the shrinkage of filament materials in 3D printing.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.245-253
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• 2022

Commenced in 1954 by IBM, machine translation has expanded immensely, particularly in this period. Machine translation can be broken into seven main steps namely- token generation, analyzing morphology, lexeme, tagging Part of Speech, chunking, parsing, and disambiguation in words. Morphological analysis plays a major role when translating Indian languages to develop accurate parts of speech taggers and word sense. The paper presents various machine translation methods used by different researchers for Indian languages along with their performance and drawbacks. Further, the paper concentrates on parts of speech (POS) tagging in Marathi dialect using various methods such as rule-based tagging, unigram, bigram, and more. After careful study, it is concluded that for machine translation, parts of speech tagging is a major step. Also, for the Marathi language, the Hidden Markov Model gives the best results for parts of speech tagging with an accuracy of 93% which can be further improved according to the dataset.

• YAKHAK HOEJI
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• v.45 no.5
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• pp.442-447
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• 2001

Aerial parts of Corydalis ochotensis Turcz. (Papaveraceae) has been used as a folk medicine in China for its antipyretic, analgesic and diuretic properties, which is widely distributed in Korea. Phytochemical study of the aerial parts of C. ochotensis led to the isolation of three isoquinoline alkaloids, (+)adlumidine, govadine and (-)severtzine, together with quercetin, rutin and tridecanoic acid. These compounds were established by conventional methods of analysis and identified by $^{1}$H,$^{13}$ C-NMR, and MS data.

• Korean Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.195-210
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• 1997

In Rapid Prototyping process, the time required to build multiple prototype parts can be reduced by packing several parts optimally in a work volume. Interactive arrangement of the multiple parts is a tedious process and does not guarantee the optimal placement of all the parts. In this case, packing is a kind of 3-D nesting problem because parts are represented by STL files with 3-D information. 3-D nesting is well known to be a problem requiring an intense computation and an efficient algorithm to solve the problem is still under investigation. This paper proposes that packing 3-D parts can be simplified into a 2-D irregular polygon nesting problem by using the characteristic of rapid prototyping process that the process uses 2-dimensional slicing data of the parts and that slice of the STL parts are composed of polygons. Our algorithm uses no-fit-polygon (NFP) to place each slice without overlapping other slices in the same z-level. The allowable position of one part at a fixed orientation for given parts already packed can be determined by obtaining the union of all NFP's that are obtained from each slice of the part. Genetic algorithm is used to determine the order of parts to be placed and orientations of each part for the optimal packing. Optimal orientation of a part is determined while rotating it about the axis normal to the slice by finite angles and flipping upside down. This algorithm can be applied to any rapid prototyping process that does not need support structures.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.