• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.109-114
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• 2005

The large injection molded parts technology such as instrument panel, front and rear bumper are presented for a precision molding. Some lead time and cost are required to product these part from design to mass product. Recently, CAE is widely used in product design, mold design and analysis of molding conditions to reduce time and cost. The optimal molding conditions can be obtained by DOE(Design of Experiment). The optimal design applications with CAE and DOE have been used in small molded parts. However, application to the large molded body is not reported. In this paper, optimization of injection molding process is studied for quality control in mass production of automobile bumper. Mold temperature difference is chosen through robust design of injection molding process, the molding process being optimized in term of shrinkage and deflection. The optimal conditions through DOE are validated by using injection molding analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1761-1768
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• 2013

Digital shipbuilding is a technology to reduce the total cost and lead time inevitably made by reschedule and rework in a shipyard. Strategic planning should be undertaken in order to have an effect on the applicable field. We aim at planning a strategy of digital shipbuilding technology by analysis of production planning workflow in this paper. In the basis of BPR methodology, the as-is business process is analyzed to build an workflow model, and derive the bottleneck business process. We dig into the inside details of the process to illustrate an diagram of the core improvement opportunities, and perform process simulation not only to create the application scenarios but also to expect the main effects. The application strategy will make a basic sketch to save both the production cost and time for high quality products in the shipyards.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1186-1190
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• 1995

This paper is focused on the formulation of explicit closed-form functions describing the performance measures of the general flexible manufacturing system (FMS)according to the strategy of material handling system(MHS). the performance measures such as the production rate, the production lead-time and the utilization rate of the general FMS are expressed, respectively, as the explicit closed-form functions of the part processing time, the service rate of the material handling system (MHS) and the number of machine tools in the FMS. For this, the gensral FMS is presented as a generalized stochastic Petri net model, then, the moment generating function (MGF) based approach is applied to obtain the steady-state probabity formulation. Based on the steady-state formulation, the explicit closed-form functions for performance measures of the probability FMS can be obtained. Finally, the analytical results are compared with the Petri net simulation results to verify the validity of the suggested method. The paper is of significance in the sense that it provides a comprehensive formula for performance measures of the FMS even to the industry engineers and academic reademic resuarchers who have no background on Markov chain analysis method or Petrinet modeling

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.181-188
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• 2019

A production system is a management system that supports all activities to perform production operations at the manufacturing site. From the point-of-view of a smart factory, smart manufacturing systems redesigned the concept of onsite production systems to fit the entire system and its necessary functional composition. In this study, we select the key functions needed to build a smart factory for a PCB line and propose a new six-step model for the deployment of a smart manufacturing system by integrating essential functions. The smart manufacturing system newly classified the production and operation tasks of PCB manufacturing and selected necessary functions through requirement analysis and benchmarking of advanced companies. The selected production operation tasks are mapped to the functions of the system and configured into seven modules, and the optimal deployment model is presented to allow flexible responses to the characteristics of the tasks. These methodologies are first presented in this study, and the proposed model was applied to the PCB line to confirm that they had significant changes in the work method, qualitative effects, and quantitative effects. Typically, lead time and WIP have reduced by about 50%.

• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• Korean Journal of Metals and Materials
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• v.50 no.11
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• pp.829-838
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• 2012

The microstructure, chemical composition, and lead isotope ratio of the Buddhist bell of Yongmoon Mountain Sangwonsa temple, which was selected as one of the three great bells of Korea by Japanese historians, were analyzed in order to estimate the origin of the material and the time of casting. The microstructure of the temple bell was composed of a copper matrix phase with ${\alpha}$, a face centered cubit lattice structure, a ${\delta}$ phase with $Cu_{41}$ $(Sn,Ag,Sb)_{11}$ as the chemical structural formula, dispersed lead and $Cu_2S$ particles, and locally agglomerated fine particles. Through analysis of the chemical composition of the bell, a criterion (Pb: 0-3.0 wt%, Sn: 10-15 wt%) for distinguishing the bells of the Shilla dynasty from the bells of the Koryo Chosun dynasty is proposed. Examining the lead isotope ratio of $^{207}Pb/^{206}Pb$ and $^{208}Pb/^{206}Pb$ of the Buddhist bell of Sangwonsa temple proved that the bell was fabricated using raw materials in South Korea, which led to the conclusion that the bell was cast in Korea and the top board of the bell has been damaged by an unknown individual. The criteria of distinguishing the bells from the Shilla dynasty from the bells of the Koryo Chosun dynasty presented for the first time in this research is expected to aid in identifying and estimating the previously unclear production years of other bells.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.47-60
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• 1997

Today, more and more information is processed in the shop floor The main function of the shop floor is more enlarged and enriched by the integration of information processing tasks. So, we have designed the shop floor control information system(SFCIS) considered using the IDEF methodology. The SFCIS consists of 5 sub-systems, which are the manufacturing data base, the order release, the dynamic scheduling, the process control, and the output analysis sub-system. And we have constructed the SFCIS for long-cycle products, which have production lead time longer than the period of production planning horizon.

• Electronics and Telecommunications Trends
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• v.36 no.4
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• pp.81-91
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• 2021

Recently, discussions on Metaverse, which represents the transcendent world, have been dominant for some time. Cases related to the Metaverse are introduced through various media and are continuously attracting attention as the next generation of the Internet. This study reviews the business model and the ecosystem overview, focusing on service cases related to the Metaverse. The widely used business models include content production and sales, media brokerage fee, and marketing fee. The Metaverse ecosystem is formed around games, with major players in game production, authoring tool & support SW, intelligent cloud service, and game platform expected to lead the market. Results show that a strategy to secure the leadership of the Metaverse, such as the business model expansion conditions, a strategy to foster a game-oriented Metaverse ecosystem, and technology development for the realization of the ultra-realistic Metaverse, is necessary.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.149-156
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• 2022

Manufacturing process mining performs various data analyzes of performance on event logs that record production. That is, it analyzes the event log data accumulated in the information system and extracts useful information necessary for business execution. Process data analysis by process mining analyzes actual data extracted from manufacturing execution systems (MES) to enable accurate manufacturing process analysis. In order to continuously manage and improve manufacturing and manufacturing processes, there is a need to structure, monitor and analyze the processes, but there is a lack of suitable technology to use. The purpose of this research is to propose a manufacturing process analysis method using process mining and to establish a manufacturing process mining system by analyzing empirical data. In this research, the manufacturing process was analyzed by process mining technology using transaction data extracted from MES. A relationship model of the manufacturing process and equipment was derived, and various performance analyzes were performed on the derived process model from the viewpoint of work, equipment, and time. The results of this analysis are highly effective in shortening process lead times (bottleneck analysis, time analysis), improving productivity (throughput analysis), and reducing costs (equipment analysis).