• 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.

• IE interfaces
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• v.25 no.1
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• pp.71-78
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• 2012

Most manufacturing companies participate in various types of active collaboration to enhance competitive advantages in their arenas. In this paper, we introduce a data warehouse system that is designed for manufacturing collaboration. Just as enterprise information systems, collaboration support systems also need functions of performance measurement and monitoring. For this reason, we devise a new approach to measuring and evaluating performance of manufacturing collaboration. Specifically, we first present a concept of process warehouses for manufacturing collaboration. Next, we design a data schema of collaborative process warehouses to store and monitor collaboration performances. Finally, we implement a prototype system to support performance management of manufacturing collaboration. The proposed system can be used to effectively maintain and continuously improve collaboration in manufacturing industry.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.787-788
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• 2007

• Journal of Institute of Convergence Technology
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• v.3 no.1
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• pp.41-44
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• 2013

Process and manufacturing data are numerously accumulated to the enterprise database in industries but little of those data are utilized. Data mining can support a decision to manager in process from the data. However, it is not easy to field managers because a proper adoption of various schemes is very difficult. In this paper, six scenarios are conducted using data mining schemes for the various situations of field claims such as yield problem, trend analysis and prediction of yield according to changes of operating conditions, etc. Scenarios, like templates, of various analysis situations are helpful to users.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.3
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• pp.67-73
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• 2003

Manufacturing technologies of compound semiconductor are similar to the process of memory device, but management technology of manufacturing process for compound semiconductor is not enough developed. Semiconductor manufacturing environment also has been emerged as mass customization and open foundry service so integrated manufacturing system is needed. In this study we design the integrated manufacturing system for compound semiconductor fabrication t hat has monitoring of process, reduction of lead-time, obedience of due-dates and so on. This study presents integrated manufacturing system having database system that based on web and data acquisition system. And we will implement them in the actual compound semiconductor fabrication.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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• pp.10-16
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• 2004

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing Process. The Purpose of this Paper is to model the recognition of defect type Patterns and Prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.118-122
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• 2005

Data mining is the process of autonomously extracting useful information or knowledge from large data stores or sets. For analyzing data of manufacturing processes obtained from database using data mining, source data should be collected form production process and transformed to appropriate form. To extract those data from database, a computer program should be made for each database. This paper presents a program to extract easily data form database in industry. The advantage of this program is that user can extract data from all types of database and database table and interface with Teamcenter Manufacturing.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.237-247
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• 2004

The condition of the manufacturing process in a factory should be diagnosed and maintained efficiently because any unexpected disorder in the process will be reason to decrease the efficiency of the overall system. However, if an expert experienced in this system leaves, there will be a problem for the efficient process diagnosis and maintenance, because disorder diagnosis within the process is normally dependent on the expert's experience. This paper suggests a process diagnosis using data mining based on the collected data from the coil-spring manufacturing process. The rules are generated for the relations between the attributes of the process and the output class of the product using a decision tree after selecting the effective attributes. Using the generated rules from decision tree, the condition of the current process is diagnosed and the possible maintenance actions are identified to correct any abnormal condition. Then, the appropriate maintenance action is recommended using the decision network.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.2
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• pp.124-135
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• 2018

In order to secure manufacturing competitiveness of offshore plants, piping process is one of the most important processes. This study is about the design of management system for piping materials manufacturing of the offshore plant. As a result of the study, we analyzed the system and algorithms needed for the processing of piping material products and designed the structure of the entire management system. We conducted a process analysis of the design, manufacturing and installation processes. And also we proposed a system structure to improve the various problems that have come out. We also proposed an algorithm to determine the delivery order of the pipe spools, and proposed a raw material management system for the manufacturing of the pipe spools. And we designed a manufacturing process management system to manage the risk of pipe materials delivery. And finally we proposed a data structure for the installation process management system. The data structures and algorithms were actually implemented, and applied the actual process data to verify the effect of the system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.133-138
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• 2005

Automotive pulleys play a key role in driving the cooling pump, oil pump, air-conditioner and so on by using an engine power. Researches on design processes and technologies of the pulleys can be found in many literatures. On the other hand, the areas related to manufacturing processes of the pulleys have been treated negligently. Vast data extracted from various information systems are transformed, integrated, and summarized to become a special database for helping users make a decision. The database, namely the data warehouse has been popularly used in the marketing and customer management of enterprises and recently applied to improve the design and manufacturing processes. In this study the manufacturing process of pulleys were analyzed through the intensive investigation of shop-floors and the interviews with workers and managers. The defects generated during a manufacturing process were categorized in a few types and the causes of defects examined for extracting the dominant parameters in the setup process for producing pulleys. As the first step to construct the data warehouse for the manufacturing processes of pulleys, authors proposed its architecture focused on the reduction of defect rate during the setup process.