• Journal of Industrial Technology
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• v.20 no.A
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• pp.229-238
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• 2000

Simulation analysis for an auto body manufacturing system has been performed in this study. The major goal is to figure out the condition yielding the production rate, 70 per hour. It is, however, very difficult to maintain this rate due to inherent system factors such as machine failure rates, machine repair rates, number of carriers between manufacturing lines(shops), carrier speed etc. We first carefully examined the system and developed a simulation model using ARENA. We then applied statistical experimental design concepts for performance analysis. Our results indicate that the buffer size of 30 and quick repair of failed robots are required for the desired production rate. Other factors, on the other hand, are seen to have minor effects on the throughput. The approach taken in this study and the results obtained may provide a practical guideline for performance analysis and thus be applied without trepidation for similar cases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.469-478
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• 2022

This paper contains the results of developing smart devices and applications to monitor the load power of the industrial manufacturing machine and evaluate its performance. The smart devices in this paper are divided into two functionalities, which are collecting load power along with operating environment data of industrial manufacturing machines and transmitting the data to servers. Load power data collected from the smart devices are uploaded to MariaDB inside the Amazon Web Service (AWS) server. Using the RESTFul API, the uploaded power data can be retrieved and shown on the web and mobile application in the form of a graph to provide monitoring capability. To evaluate the performance of the developed system, the response time from MariaDB to web and mobile applications was measured. The results is ranging from 0.0256 to 0.0545 seconds in a 4G (LTE) network environment and from 0.6126 to 1.2978 seconds in a 3G network environment, which is considered a satisfactory result.

• Journal of the Korea Safety Management & Science
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• v.9 no.1
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• pp.77-84
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• 2007

This study was planned to investigate what the main factor of the regulation compliance of inspection on harmful machine, instrument and equipment by industrial safety and health act is. This study subject was composed of three groups as employers, employees of manufacturing and using the harmful machine and safety inspectors. Manufacturing workplace were 236 places, using workplace were 201 places and the safety inspectors were 100 people. The study subject was sampled by stratified random sampling considering the type of harmful Machine. Data for analysis is collected from each sample using interview with structured questionnaires. Compliance is measured by 2, 3, and 4 point scale composed by 8 sub items such as general perception, understanding, clearness, necessity, relevancy, implementation, penalty, and general compliance of the regulation. The level of 8 items of employer's compliance are not differentiated among three groups. The determining factors for inspection observance of the workplace using the harmful Machine were understanding, penalty and cognized compliance. The determining factors for inspection observance of the workplace manufacturing the harmful Machine were understanding and object conformity. These results show that the strategy to adapt the regulated group to inspection regulation will be the elevation of understanding for regulation first of all.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.171-180
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• 1999

Industrial equipment reliability improvement and maintenance is gaining attention as the next great opportunity for manufacturing productivity improvement. Reactive maintenance is expensive because of extensive unplanned downtime and damage to machinery. To avoid such an unplanned machine downtime, it is needed to use proactive maintenance approach by either using historical maintenance data or by sensing machine conditions. This paper discusses failure diagonosis and prediction based on the condition-based maintenance and reliability technique. Thus, by enabling such a framework, it can bring us more efficient planning and execution of maintenance to reduce costs and/or increase profits.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.4
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• pp.339-344
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• 2015

Machine tools are one of the energy-intensive equipment used in the manufacturing industry. The importance of energy has increased and the machine tools are required to be energy-efficient. The servo systems of the machine tool consume electrical power to rotate a spindle and to feed a tool during machining. Servo system consumes a lot of energy when the machine tool is operated. The energy consumption pattern of each axis needs to be investigated in order to optimize the machining process with regard to energy cost. In this paper, an energy monitoring system is developed considering various measuring points of servo system in order to grasp the energy consumption pattern of each axis.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.4
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• pp.285-293
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• 2004

In this study, a machine grouping problem for the formation of manufacturing cells is considered. We constructed the problem as minimizing manufacturing leadtime consisting of parts' processing, moving, and waiting time. Specifically, the main objective of the defined problem is established as minimizing inter-cell traffic in order to minimize the part's moving time. In addition, to reduce the waiting time of parts, the load balance among cells is implicitly included as constraints. Since this problem is well known as NP-complete and cannot be solved in polynomial time, a genetic algorithm is implemented to obtain solutions. Also, a local optimization algorithm is applied in order to improve the solution by the genetic algorithm. Several experiments show that the suggested algorithms guarantee near optimal solutions in a few seconds.

• IE interfaces
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• v.8 no.1
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• pp.53-60
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• 1995

The purpose of this paper is to develop a procedure and a system for the design of manufacturing cells. First, a procedure is developed to create machine cells, to identify part families and to allocate part families to machine cells so that the intercellular movement of part is minimized. Next, a system for solving this procedure is developed and an application example is demonstrated.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.392-400
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• 2015

Monitoring of plasma etch processes for fault detection is one of the hallmark procedures in semiconductor manufacturing. Optical emission spectroscopy (OES) has been considered as a gold standard for modeling plasma etching processes for on-line diagnosis and monitoring. However, statistical quantitative methods for processing the OES data are still lacking. There is an urgent need for a statistical quantitative method to deal with high-dimensional OES data for improving the quality of etched wafers. Therefore, we propose a robust relevance vector machine (RRVM) for regression with statistical quantitative features for modeling etch rate and uniformity in plasma etch processes by using OES data. For effectively dealing with the OES data complexity, we identify seven statistical features for extraction from raw OES data by reducing the data dimensionality. The experimental results demonstrate that the proposed approach is more suitable for high-accuracy monitoring of plasma etch responses obtained from OES.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.3
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• pp.45-50
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• 2005

This paper proposed an efficient manufacturing system using the OMM (on-machine measurement) system. The OMM system is software-based 3D modeler for inspection on machine, and it is interfaced with machine tools via RS232C. The software is composed of two inspection modules; one is touch probe operating module, and the other is laser displacement sensor operating module. The module for touch probe needs the inspection feature extracted from CAD data. The touch probe moves to workpiece by three operating modes as follows: manual, general and automatic mode. The operating module of the laser displacement sensor is used to inspect profiles and very small holes. An advantage of this inspection method is the ability to execute on-line inspection during machining or afterward. The efficiency of proposed system which can predict and define the machining errors of each process was verified, so the developed system was applied to inspect a mold-base (cavity, core).

• Journal of the Korean Society for Precision Engineering
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• v.33 no.6
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• pp.431-438
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• 2016

Recently, concerns about the environment are becoming more important because of global warming and the exhaustion of earth's resources. In the aviation and automobile industries, the application of light materials is increasingly important for eco-friendly and effective. Carbon Fiber Reinforced Plastics is a composite material which great formability and the high strength of carbon fiber. CFRP, which is both light and strong, is hard to manufacture. In addition, CFRP machining has a high chance of defects. This research discusses the development of a manufacturing system package for CFRP machining. It involving CFRP Drilling/Water-jet Manufacturing Machines, Inspection/Post-processing Systems, CNC platform for an EtherCAT servo Communication, Flexible Manufacturing Systems and CFRP machining Processes.