• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.723-724
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.741-742
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• 2009

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.73-83
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• 2011

In the manufacturing system, production-planning is very important in effective management for expensive production facilities and machineries. To enhance efficiency of Manufacturing Execution System(MES), a manufacturing system that reduces the difference between planning and execution, certain production-planning needs a dispatching rule that is properly designed for characteristic of work information and there should be a appropriate selection for the rule as well. Therefore, in this paper dispatching rule will be selected by several simulations based on characteristics of work information derived from process planning data. By constructing information that are from simulation into ontology, one of the knowledge-based-reasoning, production planning platform based on the selection of dispatching rule will be demonstrated. The platform has strength in its wider usage that is not limited to where it is applied. To demonstrate the platform, RacerPro and Prot$\acute{e}$g$\acute{e}$ are used in parts of ontology reasoning, and JAVA and FlexChart were applied for production-planning simulation.

• International Journal of Quality Innovation
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• v.6 no.3
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• pp.70-94
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• 2005

The IC packaging industry heavily relies on shop floor information, necessitating the development of a model to flexibly define shop floor information and timely handle manufacturing data. This study presents a novel data model of product manufacturing flow to define shop floor information to effectively respond to accelerated developments in IC package industry. The proposed data model consists of four modules: operation template setup, general process setup, enhanced bill of manufacture (EBOMfr) setup, and work-order process setup. The data model can flexibly define the required shop floor information and decision rules for shop floor product manufacturing flow, allowing one to easily adopt changes of the product and on the shop floor. However, to handle floor dynamics of the IC packaging industry, this work also proposes a WIP (i.e. work-in-process) system for monitoring and controlling the product manufacturing flow on the shop floor. The WIP system integrates the data model with a WIP execution module. Furthermore, an illustrative example, the MIRL WIP System, developed by Mechanical Industrial Research Laboratories of Industrial Technology Research Institute, demonstrates the effectiveness of the proposed model.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1239-1242
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• 2003

The target of this paper is the manufacturing system of mould plant and the object of system development is the reduction of lead times and the improvement of product quality. From the existing MES framework. we found the module to apply in the mould plant, designed and developed the function of the module. Also. as the environment of development is based on internet, we can check and analysis correctly the status of mould plant from remote site.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.121-128
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• 2021

In order to design a real time big data collection and analysis system of manufacturing data in a smart factory, it is important to establish an appropriate wired/wireless communication system and protocol. This paper introduces the latest communication protocol, OPC-UA (Open Platform Communication Unified Architecture) based client/server function, applied user interface technology to configure a network for real-time data collection through IoT Integration. Then, Database is designed in MES (Manufacturing Execution System) based on the analysis table that reflects the user's requirements among the data extracted from the new cutting process automation process, bush inner diameter indentation measurement system and tool monitoring/inspection system. In summary, big data analysis system introduced in this paper performs SPC (statistical Process Control) analysis and visualization analysis with interface of OPC-UA-based wired/wireless communication. Through AI learning modeling with XGBoost (eXtream Gradient Boosting) and LR (Linear Regression) algorithm, quality and visualization analysis is carried out the storage and connection to the cloud.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.483-484
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• 2010

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.213-221
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• 2002

e-비지니스환경에서의 SCM의 성공적인 추진을 위해서는 우선 e-business 전략과 Supply Chain 관리 전략의 상하 연계, 조직측면에 있어서의 경영자의 변화에 대한 혁신 능력과 리더쉽, 조직 구성원들과의 비전 공유, 저보기술(IT)을 활용한 보다 차별화된 서비스가 제공되어야 한다. 본 연구는 C기업의 사례로 통해, 기존의 SCM 기법에서 정보기술(IT)의 급속한 발달과 인터넷의 급속한 보급, 그리고 e-비지니스의 등장으로 인하여 경영환경의 변화라는 새로운 패러다임 하에서 다수의 경쟁기업, 정보 네트워크시스템 기반에서 보다 효과적으로 기업과 고객에게 대응하기 위한 기업의 구체적이고 실무적인 전략 측면에서 다루어 보았다는 데 그 의의가 있다. 소비재, 생활용품에 있어서 보다 빠른 납기, 보다 전문적이고 다양화되고 있는 고객의 욕구를 충족시키기 위해 제조기업 측면에서 어떻게 기존의 조직구조 및 업무 프로세스를 혁신 할 것인가를 실제 사례를 통하여 연구함으로써 동종 산업체나 또는 기업의 실무자들에게 있어 큰 도움이 될 것이다.

• Proceedings of the CALSEC Conference
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• 1999.07b
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• pp.607-614
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• 1999

In this paper, we demonstrate the implementation case using component based development tool under development process for application developments. The tool suggested provides the programming environment for the development of distributed manufacturing applications primarily. The development tool is classified into visual component, logic component, data component, knowledge component, neural net component, and service component which is a core component for the support component edit and execution. We applied the tool to the domain of the design and routing plan to retrieve existing similar design models in database, initiate a model, generate a process plan, and store the new model in the database automatically. Utilizing the tool, it integrates a geometric modeler, engineering/manufacturing database, and knowledge sources over the Internet.