• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.2
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• pp.209-216
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• 1997

A feature is well known as a medium to integrate CAD, CAPP and CAM systems. For a part drawing including both simple geometry and compound geometry, a process plan such as the selection of process, machine tool, cutting tool etc. normally needs simple geometry data and non-geometry data of the feature as the input. However, a extended process plan such as the generation of process sequence, operation sequence, jig & fixture, NC program etc. necessarily needs the compound geometry data as well as the simple geometry data and non-geometry data. In this paper, we propose a feature data model according to the result of analyzing necessary data, including the compound geometry data, the simple geometry data and the non-geometry data. Also, an example of the feature data model in milling process planning is described.

• 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).

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.823-833
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• 2005

This paper deals the TFT-LCD process and quality, process control problems of the process. For improvement of the process quality and yield, we apply a data mining technique to the LCD industry. And some unique quality features of the LCD process are also described. We describe some preceding researches first and relate to the TFT-LCD process and the problems of data mining in the process. Also we tried to observe the problems which need to solve first and the features from description below hazard must be considered a quality mining in LCD industry.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2001.11a
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• pp.142-147
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• 2001

Recently, as the field of construction industrial is enlarged and the blanket control system is formed, the process plan considered from initial process formation to construction cost must be systematic and rational. So we have to propose and compose the new process formation system that divides the intention decisive property of construction data, its relationship and the data property according to process definitely and can express and control easily the public ownership of construction data and network process under the peculation control. So this study decides the field formation and construction data of the work according to the ordering and contract way to establish the process data system for the process plan, shows that the lot theory is necessary to form the economical construction field and proposes that the integrated formation system of construction data that is made by code system must be established.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.166-171
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• 2005

With development of the database, there are too many data on process variables and the manufacturing process for the traditional statistical process control methods to identify the process variables related with assignable causes. Data mining is useful in this situation and provides variety of approaches for improving the process. In this paper, we applied control charts to monitor the process and if assignable causes are detected, then we applied the SVM technique and the sequence pattern analysis to find out the process variables suspected. These techniques made possible to predict the behavior of process variables. We illustrated our proposed methods with real manufacturing process data.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.2
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• pp.143-151
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• 2019

This study describes a method for analyzing log data generated in a process using process mining techniques. A system for collecting and analyzing a large amount of log data generated in the process of manufacturing an offshore plant piping material was constructed. The analyzed data was visualized through various methods. Through the analysis of the process model, it was evaluated whether the process performance was correctly input. Through the pattern analysis of the log data, it is possible to check beforehand whether the problem process occurred. In addition, we analyzed the process performance data of partner companies and identified the load of their processes. These data can be used as reference data for pipe production allocation. Real-time decision-making is required to cope with the various variances that arise in offshore plant production. To do this, we have built a system that can analyze the log data of real - time system and make decisions.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.83-105
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• 2013

There are two ways to assess data quality : measurement of data itself and assessment of data quality management process. Recently maturity assessment of data quality management process is used to ensure and certify the data quality level of an organization. Following this trend, the paper presents the process reference model which is needed to assess data quality management process maturity. First, the overview of assessment model for data quality management process maturity is presented. Second, the process reference model that can be used to assess process maturity is proposed. The structure of process reference model and its detail processes are developed based on the process derivation approach, basic principles of data quality management and the basic concept of process reference model in SPICE. Furthermore, characteristics of the proposed model are described compared with ISO 8000-150 processes.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.53-58
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• 2023

The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process and sensor data are required, such as optical emission spectroscopy (OES), quadrupole mass spectrometer (QMS), and equipment control state. Among them, OES is usually used for plasma diagnostic. However, OES data can be distorted by viewport contamination, leading to misunderstandings in plasma diagnosis. This issue is particularly emphasized in multi-dielectric deposition processes, such as oxide and nitride (ON) stack. Thus, it is crucial to understand the potential misunderstandings related to OES data distortion due to viewport contamination. This paper explores the potential for misunderstanding OES data due to data distortion in the ON stack process. It suggests the possibility of excessively evaluating process drift through comparisons with a QMS. This understanding can be utilized to develop diagnostic models and identify the effects of viewport contamination in ON stack processes.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.135-142
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• 2024

Recently, along with digital transformation, technologies such as cloud computing, big data, and artificial intelligence have been actively introduced. In a situation where these technological changes are progressing rapidly, it is often difficult to manage processes efficiently using existing simple workflow management methods. Companies providing current cloud services are adopting virtualization technologies, including virtual machines (VMs) and containers, in their distributed system infrastructure for automated application deployment. Accordingly, this paper proposes a process-based orchestration system for integrated execution of corporate process-oriented workloads by integrating the potential of big data and machine learning technologies. This system consists of four layers as components for performing workload processes. Additionally, a common information model is applied to the data to efficiently integrate and manage the various formats and uses of data generated during the process creation stage. Moreover, a standard metadata protocol is introduced to ensure smooth exchange between data. This proposed system utilizes various types of data storage to store process data, metadata, and analysis models. This enables flexible management and efficient processing of data.

• 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.