• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.85-88
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• 2021

There are many irregularly-sampled time series in the manufacturing data which are collected from manufacturing facilities by short intervals. Those time series often have large variance. In this paper, we propose irregularly-sampled time series correction method based on simple moving average. This method corrects time intervals between neighboring values in time series regularly and reduces the variance of the values at the same time. We examine that this method improves performance of anomaly detection in manufacturing facility.

• The Journal of Information Systems
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• v.26 no.2
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• pp.105-121
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• 2017

Purpose It is a major goal to improve the product yields during production operations in the manufacturing industry. Therefore, factory is trying to keep the good quality materials and proper production resources, also find the proper condition of facilities and manufacturing environment for yields improvement. Design/methodology/approach We propose the hybrid framework to analyze to dataset extracted from MES. Those data is about the alarm information generated from equipment, both measurement and equipment process value from production and cycle/pitch time measured from production data these covered products during production. We adapt a data warehousing techniques for organizing dataset, a logistic regression for finding out the significant factors, and a association analysis for drawing the rules which affect the product yields. And then we validate the framework by applying the real data generated from the discrete process in secondary cell battery manufacturing. Findings This paper deals with challenges to apply the full potential of modeling and simulation within CPPS(Cyber-Physical Production System) and Smart Factory implementation. The framework is being applied in one of the most advanced and complex industrial sectors like semiconductor, display, and automotive 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.615-616
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• 2006

Many manufacturing companies manage their production-related data for quality management and production management. Nevertheless, production related-data should be closely related to each other Stored data is mainly used to monitor their process and products' error. In this paper, we provide an enterprise-wide production data model for decision support system and product automation. Process data, quality-related data, and test data are integrated to identify the process inter or intra dependency, the yield forecasting, and the trend of process status. In addition, it helps the manufacturing decision support system to decide critical manufacturing problems.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.124-125
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• 2023

Defect detection during liquid crystal display (LCD) manufacturing has always been a critical challenge. This study aims to address this issue by proposing a data augmentation method based on generative adversarial networks (GAN) to improve defect identification accuracy in LCD production. By leveraging synthetically generated image data from GAN, we effectively augment the original dataset to make it more representative and diverse. This data augmentation strategy enhances the model's generalization capability and robustness on real-world data. Compared to traditional data augmentation techniques, the synthetic data from GAN are more realistic, diverse and broadly distributed. Experimental results demonstrate that training models with GAN-generated data combined with the original dataset significantly improves the detection accuracy of critical defects in LCD manufacturing, compared to using the original dataset alone. This study provides an effective data augmentation approach for intelligent quality control in LCD production.

• Korean Journal of Computational Design and Engineering
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• v.9 no.2
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• pp.175-182
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• 2004

As a bridge between design and manufacturing, process planning is to generate a sequenced set of instructions to manufacture the specified part. Automatic interpretation of manufacturing information incorporated in the design documentation such as CAD file has been a knotty subject for manufacturing engineers since no current data exchange format for product data provides a perfect interface between heterogeneous systems. The recent neutral data exchange format STEp, standard for the exchange of product model data, includes not only geometry but also technical and managerial information. STEP AP(Application Protocol) 224 is specifically dedicated to the mechanical product definition for process planning using machining features. Given a design information in STEP AP 224 format, process planning can be made without human intervention. This paper describes a method to determine the sequence of machining features by using the machining features and the manufacturing information expressed in STEP AP224.

• 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 Institute of Industrial Engineers
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• v.41 no.1
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• pp.25-33
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• 2015

From the viewpoint of applications to manufacturing, data mining is a useful method to find the meaningful knowledge or information about states of processes. But the data from manufacturing processes usually have two characteristics which are multicollinearity and imbalance distribution of data. Two characteristics are main causes which make bias to classification rules and select wrong variables as important variables. In the paper, we propose a new data mining procedure to solve the problem. First, to determine candidate variables, we propose the multiple hypothesis test. Second, to make unbiased classification rules, we propose the decision tree learning method with different weights for each category of quality variable. The experimental result with a real PDP (Plasma display panel) manufacturing data shows that the proposed procedure can make better information than other data mining procedures.

• 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 Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.82-84
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• 2021

Through the smart factory construction project, sensor data such as power, vibration, pressure, and temperature are collected from production facilities, and services such as predictive maintenance, defect prediction, and abnormality detection are developed through data analysis. In general, in the case of manufacturing data, because the imbalance between normal and abnormal data is extreme, an anomaly detection service is preferred. In this paper, FFT method is used to extract feature data of manufacturing data as a pre-stage of the anomaly detection service development. Using this method, we classified the produced products and confirmed results. In other words, after FFT of the representative pattern for each product, we verified whether product classification was possible or not, by calculating correlation coefficient.