• The Journal of Bigdata
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• v.3 no.1
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• pp.95-103
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• 2018

In recent years, many people in the manufacturing field have been making efforts to increase efficiency while analyzing manufacturing data generated in the process according to the development of ICT technology. In this study, we propose a data mining based manufacturing process using decision tree algorithm (CHAID) as part of a smart factory. We used 432 sensor data from actual manufacturing plant collected for about 5 months to find out the variables that show a significant difference between the stable process period with low defect rate and the unstable process period with high defect rate. We set the range of the stable value of the variable to determine whether the selected final variable actually has an effect on the defect rate improvement. In addition, we measured the effect of the defect rate improvement by adjusting the process set-point so that the sensor did not deviate from the stable value range in the 14 day process. Through this, we expect to be able to provide empirical guidelines to improve the defect rate by utilizing and analyzing the process sensor data generated in the manufacturing industry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.78-81
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• 2021

Through the smart factory construction project, sensors can be installed in manufacturing production facilities and various process data can be collected in real time. Through this, research on real-time facility anomaly detection is being actively conducted to reduce production interruption due to facility abnormality in the manufacturing process. In this paper, to detect abnormalities in production facilities, the manufacturing data was applied to deep learning models Autoencoder(AE), VAE(Variational Autoencoder), and AAE(Adversarial Autoencoder) to derive the results. Manufacturing data was used as input data through a simple moving average technique and preprocessing process, and performance analysis was conducted according to the window size of the simple movement average technique and the feature vector size of the AE model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.2
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• pp.55-61
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• 2014

Recently, the manufacturing system is being changed in a mass customization and small quantity batch production. MES is a powerful production management tool supporting production optimization from the process initiation to the final shipment. It is a production management system which plans and executes based on the production data in the shop floor. This study deployed the utilization of production data and web HMI system to process real-time production data through the collection with the shop floor. The developed system was applied to the equipment operating time and other production data could be processed with the real-time. The proposed system and web HMI can be applied for various production systems by using different logic.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.4 s.181
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• pp.99-108
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• 2006

Since mold industries are regarded as belonging to three types of bad business, capable young people are reluctant to work in this field. The industries are hard to employ skilled workers who have much experience and knowledge On the mold manufacturing. Thus, effective CAM systems are required for unskilled workers to create process plans and NC data for the manufacturing, and process plans play important roles in the downstream manufacturing processes, such as NC machining, polishing, and final assembly. This study proposes a decision support system that facilitates unskilled workers to easily select high quality NC-data, as well as to increase productivity. The proposed system is assumed to follow a CAM operation scenario that consists of next three steps: 1) identifying several process plans and enumerating feasible unit machining operations (UMOs) from material and part surface information, 2) creating all feasible NC-data based on UMOs using a commercial CAM system, 3) selecting the best NC data among the feasible NC data using four screening criteria, such as machining accuracy, machining allowance, cutting load, and processing time. A case study on the machining of a camera core mold is provided to demonstrate the proposed system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.377-378
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• 2012

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.243-260
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• 2008

To prevent low yields in the semiconductor industry is crucial to the success of that industry. However, to prevent low yields is difficult because of too many factors to affect yield variation and their complex relation in the semiconductor manufacturing process. This study presents a new efficient detection methodology for detecting abnormal yields including high and low yields, which can forecast the yield level of a production unit (namely a lot) based on yield-related feature variables' behaviors. In the methodology, we use C5.0 to identify the yield-related feature variables that are the combination of correlated process variables associated with yield, use SOM (Self-Organizing Map) neural networks to extract and classify significant patterns of past abnormal yield lots and finally use C5.0 to generate classification rules for detecting abnormal yield lot. We illustrate the effectiveness of our methodology using a semiconductor manufacturing company's field data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.6 no.4
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• pp.28-35
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• 2007

Currently, most of all valves using at the semiconductor & LCD manufacturing process are packless type. Such valves are needed to have the functions to protect perfectly from the leakage of fluid by using bellows and diaphragm instead of the grand packing. But not only those valves made in Korea are not available at this point, but also advanced foreign manufacturers do not open enough their data on the basic technology. Otherwise although they open the data a little, they are almost data about stainless steel for pneumatic valves. In this paper, it was focused on the fluid valves for chemical & DI water based on the data of steel valves which are already using commercially. And also this study concentrated to collect the basic developing data of Eco-On/Off diaphragm valve by ourselves.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• Journal of Korea Society of Industrial Information Systems
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• v.1 no.1
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• pp.111-125
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• 1996

This paper presents a statistical process control(SPC) system in the electronic parts manufacturing process. In this system, an SPC method is integrated into the automated inspection technology on a real time base. It shows how the collected data can be analyzed with the SPC to provide process information. also presented are stuided of subpixel image processing technology to improve the accuracy of parts mearements , and the cumulative-sum(CUSUM) control chart for fraction defectives.An application of the developed system to connector manufacturing process as a part of computer integrated manufacturing (CIM) is presented.