• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.181-189
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• 2023

In this paper, we present a case study of developing MVIS (Machine Vision Inspection System) designed for exterior quality inspection of stamping dies used in the production of automotive exterior components in a small to medium-sized factory. While the primary processes within the factory, including machining, transportation, and loading, have been automated using PLCs, CNC machines, and robots, the final quality inspection process still relies on manual labor. We implement the MVIS with general-purpose industrial cameras and Python-based open-source libraries and frameworks for rapid and low-cost development. The MVIS can play a major role on improving throughput and lead time of stamping dies. Furthermore, the processed inspection images can be leveraged for future process monitoring and improvement by applying deep learning techniques.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.29-41
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• 2022

As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.199-206
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• 2022

A malfunction or breakdown of a manufacturing facility leads to product defects and the suspension of production lines, resulting in huge financial losses for manufacturers. Due to the spread of smart factory services, a large amount of data is being collected in factories, and AI-based research is being conducted to predict and diagnose manufacturing facility breakdowns or manufacturing site efficiency. However, because of the characteristics of manufacturing data, such as a severe class imbalance about abnormalities and ambiguous label information that distinguishes abnormalities, developing classification or anomaly detection models is highly difficult. In this paper, we present an deep learning algorithm for anomaly detection of a manufacturing facility using reconstruction loss of CNN-based model and ananlyze its performance. The algorithm detects anomalies by relying solely on normal data from the facility's manufacturing data in the exclusion of abnormal data.

• Journal of Practical Engineering Education
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• v.16 no.5_spc
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• pp.619-633
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• 2024

This study analyzes the competency model of the Learning Factory at TU Darmstadt, Germany, aiming to derive implications for developing competency-based curricula in South Korean universities. It proposes an integrated model that combines theoretical knowledge with practical experience, allowing for the transformation of existing knowledge into necessary competencies for task execution. Additionally, it advocates for a learner-centered approach that enhances active engagement in the learning process, empowering students to take charge of their education. By implementing this integrated and learner-centered model, students can develop the transformative competencies required for future societal demands. This approach is expected to act as a catalyst for advancing competency-based education within South Korea's higher education system, ultimately fostering a more effective and responsive educational environment.

• 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.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.92-100
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• 2023

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM usinga mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.17-18
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• 2017

본 논문은 스마트 팩토리의 중요 요소인 무인반송차(AGV)를 자율 주행시키기 위해 오픈 소스 자율 주행차 시뮬레이터인 udacity를 이용해 머신 러닝시키는 시스템을 개발하였다. 공장의 운행 루트를 자율주행 시뮬레이터의 전경으로 가공하고, 3개의 카메라를 부착시킨 AGV를 운행시키면서 머신 러닝시킨다. AGV를 주행하여 얻어진 여러 학습 데이터를 통해 도출된 결과들을 각각 비교하여 우수한 모델을 선정하고 운행시킨 결과 AGV가 정해진 운행 루트를 정확하게 주행하는 것을 확인하였다. 이를 통해, 가상 운행 환경에서 저비용으로 AGV 운행 학습이 가능하다는 것을 보였다.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.4
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• pp.415-425
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• 2019

This study proposes a new approach to control Moving Stuff Action Through Iterative Learning robot with dual arm for smart factory. When robot moves object with dual arm, not only position of each hand but also contact force at surface of an object should be considered. However, it is not easy to determine every parameters for planning trajectory of the an object and grasping object concerning about variety compliant environment. On the other hand, human knows how to move an object gracefully by using eyes and feel of hands which means that robot could learn position and force from human demonstration so that robot can use learned task at variety case. This paper suggest a way how to learn dynamic equation which concern about both of position and path.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.479-482
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• 2022

Recently, various artificial intelligence technologies are being applied to smart factory, finance, healthcare, and so on. When handling data requiring protection of privacy, distributed learning techniques are used. For distribution of information with privacy protection, encoding private information is required. Minimal codes has been used in such a secret-sharing scheme. In this paper, we explain the relationship between the characteristics of the minimal codes for application in distributed systems. We briefly deals with previously known construction methods, and presents extension methods for minimal codes. The new codes provide flexibility in distribution of private information. Furthermore, we discuss application scenarios for the extended codes.

• The Journal of Korean Institute for Practical Engineering Education
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• v.2 no.2
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• pp.10-15
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• 2010

The subject of basic fluid power practice which is used in various industries requiring factory automation aims at understanding of the composition and operating principles of pneumatic components and programming of electric sequential circuits, building the design ability of pneumatic system. This subject goes by 3 hour classes with theory and practice side by side. So it is not enough time to instruct students various contents related in this subject. In this research a supplementary learing using E-learning is proposed as a solution for this problem. The off-line classes of this subject went with E-learning side by side and analyzed the effects of E-learning as a supplementary learning through the students survey who attended the class. And further needed research is presented.