The goal of this research is to develop intelligence data analytics system for quality enhancement of die-casting process. Targeting a die-casting factory in Korea, we first constructed an edge device-based infrastructure with wireless communication environment for data collection and a processing infrastructure to support the intelligence data analytics system. Using the real quality regarding data of the target factory, we developed two data analytics models for defect prediction and defect cause diagnosis using AdaBoostC2 algorithm. Accuracy of the developed data analytics model for defect prediction was verified as 86%. To use the developed data analytics model efficiently and produce a sequential process of data analytics model generation, execution, and update were conducted automatically. The edge device and integrated server-based dualized analysis system was proposed. The developed intelligence data analytics system was applied to the target factory, and the effectiveness was demonstrated.
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Development of AI-based Bearing Machining Process Defect Monitoring System Dae-Youn Kim, Dongwoo Go, Seunghoon Lee Journal of Society of Korea Industrial and Systems Engineering.2025; 48(3): 112. CrossRef
Development of a Quality Prediction Algorithm for an Injection Molding Process Considering Cavity Sensor and Vibration Data Jun Kim, Ju Yeon Lee International Journal of Precision Engineering and Manufacturing.2023; 24(6): 901. CrossRef
Data-analytics-based factory operation strategies for die-casting quality enhancement Jun Kim, Ju Yeon Lee The International Journal of Advanced Manufacturing Technology.2022; 119(5-6): 3865. CrossRef
Development of a cost analysis-based defect-prediction system with a type error-weighted deep neural network algorithm Jun Kim, Ju Yeon Lee Journal of Computational Design and Engineering.2022; 9(2): 380. CrossRef
Development of Prognostics and Health Management System for Rotating Machine and Application to Rotary Table Mingyu Kang, Chibum Lee Journal of the Korean Society for Precision Engineering.2022; 39(5): 337. CrossRef
Server-Edge dualized closed-loop data analytics system for cyber-physical system application Jun Kim, Ju Yeon Lee Robotics and Computer-Integrated Manufacturing.2021; 67: 102040. CrossRef
Die-Casting Defect Prediction and Diagnosis System using Process Condition Data Ji Soo Kim, Jun Kim, Ju Yeon Lee Procedia Manufacturing.2020; 51: 359. CrossRef
Development of Fault Diagnosis Models Based on Predicting Energy Consumption of a Machine Tool Spindle Won Hwa Choi, Jun Kim, Ju Yeon Lee Procedia Manufacturing.2020; 51: 353. CrossRef
In recent years, the main concerns of enterprise management activities is how to strengthen the competitiveness to quickly respond to changes and sustain the growth in business environment. In order to achieve competitiveness and sustainability, a large variety of production informatization systems, such as MES (Manufacturing Execution System), ERP (Enterprise Resource Planning), have been introduced to manufacturing companies. However, there have been many problems owing to the reckless introduction of those systems. Therefore, it is necessary to evaluate the informatization level of the manufacturing company before introducing production informatization systems. This paper presents methodology and system to evaluate the informatization level of manufacturing company. The proposed evaluation method, based on the traditional questionnaire approaches, considers the various characteristics of the company. And then, we proposed models for calculation and production informatization level using 450 results of conducting a survey. Finally, a web-based system was developed for the evaluation of informatization level.
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