• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.803-804
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• 2016

With the advent of Germany's Industry 4.0, research of smart factory to applying the ICT in manufacturing industries is in progress. But the current system controlled equipment using the data declared in the embedded systems. In this paper, we proposed equipment control method to implement smart factory based on IoT. This method is create D/B table of data declared in equipment. and equipment shall call all of control unit parameters. When using the present method, it is possible to efficiently control the number of equipment as less network resource. Also It can operating a factory efficiently.

• IE interfaces
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• v.20 no.3
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.87-95
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• 2012

Up to now, industrial cranes play important roles as the effective machines to carry heavy loads in the manufacturing premise, in the construction field and so on. And, a crane is widely used not only to daily work but also to carry heavy materials efficiently in a construction site for prevention of accident. However, the crane operation is highly complicated even for experts. In this paper, we developed the content of the crane mounted on the controller. This content overload conditions in the operating environment for the crane operator to warn, and the operation of equipment has the capability to limit automatically. The content for crane controller is to alert the operator overload and to limit the operation of equipment for stabilizing capabilities. The content of the flexible algorithm is based on stabilizing controllers, PLC (Programmable Logic Controller) to connect for using the equipment and electrical control systems to ensure the safety of workers and to improve the ability to work possible.

• Electronics and Telecommunications Trends
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• v.36 no.4
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• pp.110-117
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• 2021

The development of core technologies in the 4th Industrial Revolution, such as artificial intelligence, big data, and the intelligent Internet of Things, promote digital transformation and intelligence of the manufacturing industry. To realize them, there is an increasing demand for materials, components, and equipment needed for final goods. In particular, the expansion of global value chain instability due to changes in the external environment, such as the U.S.-China trade dispute, Japan's export regulations, and Covid-19 pandemic, increases the importance of strengthening the materials, components, and equipment industry in the global market. Thus, this study presents a strategic direction for securing global industrial competitiveness of materials, components, and equipment using Michael Porter's diamond model approach.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.6 no.1
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• pp.7-12
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• 2007

The optic industry is a high value-added advanced technology industry combined with the precision machine industry and the digital electronics industry. The aspheric lens, one of optic parts, is a key technology having a significant influence on the performance of optic equipment. So this study relates to designing an aspheric lens to which a ray-tracing method is applied. In the ray-tracing method, a refractive index of material is used, which take an advantage that the location of a light source and incident angle can be fixed, unlike the ray back-tracing scheme.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.480-484
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• 2004

Despite is product that existent higher hrust engine, ship, vehicles, development equipment and Metal Bearing for plant equipment Cast White Metal Lining Bearing that is Bimetal Bearing standing 2 generation is accomplishing master and servant and this is foreseen to be used widely on industry whole in hereafter but Cast White Metal Bearing need minuteness processing, price competitive power is depending on income from superior another thing area than itself manufacture already in advanced nation to lowdown that the technique is generalized widely.

• Korean Management Science Review
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• v.13 no.3
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• pp.143-161
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• 1996

For an industry, production capacity is defined as the maximum level of output that plants can maintain within the framework of a realistic work schedule, taking account of normal downtime, and assuming sufficient availability of inputs to operate machinery and equipment in place. Such capacity is one of the important and basic due to measure, manage and evaluate the production performance and ability of any industrial bodies. However, the estimating methods now in use in Korea are seemed far from the definition above. And there are not any standard estimating method suggested even in the same sort of manufacturing and also no applicable theory for objective and exact measurement. Thus, in this paper, a new measuring model is suggested as standard and supporting theories are developed for general measurement purpose to any manufacturing industries.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.6
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• pp.54-60
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• 2014

In industry, several casting process are widely used to manufacture complex and accurate blank part of hard materials such as aluminum, casting steels, bronze and magnesium alloys which are difficult to manufacture in a blank shape. Even if the casting process does not high accuracy superior surface characteristics other machining process, the casting process is widely used in manufacturing blank part. Furthermore, it is difficult to select appropriate casting process a part among several casting process. for effective selection different process, a careful decision given casting application is necessary. An appropriate casting for a given material and shape condition must be selected for novice engineers in industry. In this paper, an expert system based on an analytic network process(ANP) is suggested for best selection of casting considering a prior interdependency effect among various factors such as material, geometry, process capability, economy and equipment.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1085-1096
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• 2022

The manufacturing industry is continuously pursuing advanced technology and smartization as it converges with innovative technology. Improvement of manufacturing productivity is achieved by monitoring, analyzing, and controlling the facilities and processes of the manufacturing site in real time through a network. In this paper, we proposed a new OPC-UA based digital twin model for smart factory facilities. A testbed system for USB flash drive packaging facility was implemented based on the proposed digital twin model and OPC-UA data communication scheme. Through OPC-UA based digital twin model, equipment and process status information is transmitted and received from PLC to monitoring and control 3D digital models and physical models in real time. The usefulness of the developed digital twin testbed system was evaluated through usability test.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.99-114
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• 2021

With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.