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- [일반] SMATEC 2023 학과 부스 참가 안내 [온라인초청장 공유]
- 안녕하세요, 성균관대학교 스마트팩토리융합학과 입니다. 제5회 스마트공장구축 및 생산자동화전 SMATEC 2023 전시회 참가 안내드립니다. 학과에서도 부스를 운영 중이오니 관심있는 분들은 방문하시어 많은 참여부탁드립니다. 홈페이지를 통해 사전신청(무료입장)을 할 수 있습니다. 행사명 SMATEC 2023 기 간 2023년 11월 8일(수) ~ 11월 10일(금), 3일간 장 소 경기도 수원시 영통구 광교중앙로 140(하동) 수원컨벤션센터 주 최 SMATEC 2023 추진위원회 예상규모 130업체 320부스 전시구성 컨퍼런스, 참가업체세미나, 전시장 관람(자동화설비관,공장자동화관,스마트공장솔루션관) 등 자세한 사항 홈페이지(http://www.smatec.or.kr/) 및 붙임의 온라인초청장 참고부탁드립니다. 감사합니다.
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- 작성일 2023-10-24
- 조회수 1969
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- [일반] 우수 제조기업 연수 : 삼성전자(구미공장) 견학 참가자 모집
- 안녕하세요. 성균관대학교 스마트팩토리융합학과입니다. 월드베스트 제조기업인 삼성전자(구미공장-스마트폰 생산)의 연수 참가신청을 안내드립니다. 학생 및 교원, 유관기업 재직자 분들의 많은 관심을 부탁드립니다. 감사합니다. **일정변경** 10/26(목) -> 10/31(화)로 일정을 변경하였음 ▷ 신청기간 : 2023. 10. 06.(금) ~ 2023. 10. 19.(목) ▷ 신청대상 : 성균관대학교 교원 및 학생, 유관기업 재직자 ▷ 견학장소 : 삼성전자(구미공장) ▷ 견학일정 : 2023. 10. 31.(화) 09:00 ~ 12:00 - 집합 및 이동(수원▷구미) 12:00 ~ 13:00 - 중식 13:00 ~ 17:00 - 삼성전자 구미공장 견학 [스마트갤러리(홍보관), 메카팀전시룸, 자동화 검증 랩, 제조라인 투어] 15:00 ~ 18:00 - 1차 귀가자 집합 및 이동(구미▷수원) ---------------------------- 이후 일정은 저녁일정 참가자에 한함 -------------------------- 17:00 ~ 18:00 - 특강(삼성전자 제조 관련) 18:00 ~ 19:00 - 석식 19:00 ~ 20:00 - 간담회 20:00 ~ 23:00 - 2차 귀가자 집합 및 이동(구미▷수원) ▷ 이동안내 : 성균관대학교 자연과학캠퍼스 삼성학술정보관 앞(학교 버스로 이동) ▷ 신청방법 : 온라인 폼 작성(https://docs.google.com/forms/d/1qss55_BdbDRxbXduIDVi7HPPW_1tnCFAeaKMulcq9-Q/edit)
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- 작성일 2023-10-05
- 조회수 1325
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- [연구] 박사과정 기인종, SCIE 논문지(MDPI applied sciences/Q2) 게재
- 박사과정 기인종 학생(지도교수: 정종필)의 연구(Production Improvement Rate with Time Series Data on Standard Time at Manufacturing Sites)가 MDPI applied sciences(Impact Factor: 2.7 (2022); 5-Year Impact Factor: 2.9 (2022))에 게재됐다. https://www.mdpi.com/2076-3417/13/19/10937 / https://doi.org/10.3390/app131910937 논문요약 - Amid the changes brought about by the 4th Industrial Revolution, numerous studies have been undertaken to develop smart factories, with a strong emphasis on knowledge-based manufacturing through smart factory construction. Advances in manufacturing data collection, fusion, and mining technologies have significantly bolstered the utilization of knowledge-based manufacturing. Data mining technology is widely employed for facility maintenance and failure prediction. Smart factory operations are pursuing automation and autonomization. Automation of production planning is also essential to achieve automation and autonomy in factory operations, from planning to execution. With the advancement of data mining technology, it is possible to automate production planning for the production planning and prediction of future production through information based on current conditions based on the past. The baseline information generated based on the current situation is suitable for automating short-term operational planning. If we generate time series reference information based on data from the past to the present, we can also automate long-term operation planning. By measuring the results of productivity improvements in mass-produced products from the past to the present and extrapolating them to future products, time series baseline information on production time is generated. If the baseline information is used for long-term planning, it can be used to predict future production capacity and facility shortages. This study presents a methodology and utilization method for calculating the rate of change in production time, which can be applied to production plan prediction and equipment investment capacity forecasting in future factory operations, using historical time series production time data.
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- 작성일 2023-10-03
- 조회수 2611
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- [연구] 석사과정 문지상, SCIE 논문지(MDPI Processes/Q2) 게재
- 석사과정 문지상 학생(지도교수 : 정종필)의 연구(Design and Implementation of Defect Detection System Based on YOLOv5-CBAM for Lead Tabs in Secondary Battery Manufacturing)가 MDPI Processes(Impact Factor: 3.5 (2022); 5-Year Impact Factor: 3.4 (2022))에 게재됐다. https://www.mdpi.com/2227-9717/11/9/2751 / https://doi.org/10.3390/pr11092751 논문요약 - According to QYResearch, a global market research firm, the global market size of secondary batteries is growing at an average annual rate of 8.1%, but fires and casualties continue to occur due to the lack of quality and reliability of secondary batteries. Therefore, improving the quality of secondary batteries is a major factor in determining a company’s competitive advantage. In particular, lead taps, which electrically connect the negative and positive electrodes of secondary batteries, are a key factor in determining the stability of the battery. Currently, the quality inspection of secondary battery lead tab manufacturers mostly consists of visual inspection after vision inspection with a rule-based algorithm, which has limitations on the types of defects that can be detected, and the inspection time is increasing due to overlapping inspections, which is directly related to productivity. Therefore, this study aims to automate the quality inspection of lead tabs of secondary batteries by applying deep-learning-based algorithms to improve inspection accuracy, improve reliability, and improve productivity. We selected the YOLOv5 model, which, among deep-learning algorithms, has a benefit for object detection, and used the YOLOv5_CBAM model, which replaces the bottleneck part in the C3 layer of YOLOv5 with the Convolutional Block Attention Module (CBAM) based on the attention mechanism, to improve the accuracy and speed of the model. As a result of applying the YOLOv5_CBAM model, we found that the parameter was reduced by more than 50% and the performance was improved by 2%. In addition, image processing was applied to help segment the defective area to apply the SPEC value for each defective object after detection.
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- 작성일 2023-09-14
- 조회수 2546
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- [연구] 박사과정 이혁수, SCIE 논문지(Frontiers in Neurorobotics/Q3) 게재
- 박사과정 이혁수 학생(지도교수: 정종필)의 연구(Velocity Range-based Reward Shaping Technique for Effective Mapless Navigation with LiDAR Sensor and Deep Reinforcement Learning)가 Frontiers in Neurorobotics(Impact Factor: 3.1 (2022); CiteScore: 5.0 (2022))에 게재됐다. https://www.frontiersin.org/articles/10.3389/fnbot.2023.1210442 / https://doi.org/10.3389/fnbot.2023.1210442 논문요약 - In recent years, sensor components similar to human sensory functions have been rapidly developed in the hardware field, enabling the acquisition of information at a level beyond that of humans, and in the software field, artificial intelligence technology has been utilized to enable cognitive abilities and decision-making such as prediction, analysis, and judgment. These changes are being utilized in various industries and fields. In particular, new hardware and software technologies are being rapidly applied to robotics products, showing a level of performance and completeness that was previously unimaginable. In this paper, we researched the topic of establishing an optimal path plan for autonomous driving using LiDAR sensors and deep reinforcement learning in a workplace without map and grid coordinates for mobile robots, which are widely used in logistics and manufacturing sites. For this purpose, we reviewed the hardware configuration of mobile robots capable of autonomous driving, checked the characteristics of the main core sensors, and investigated the core technologies of autonomous driving. In addition, we reviewed the appropriate deep reinforcement learning algorithm to realize the autonomous driving of mobile robots, defined a deep neural network for autonomous driving data conversion, and defined a reward function for path planning. The contents investigated in this paper were built into a simulation environment to verify the autonomous path planning through experiment, and an additional reward technique “Velocity Range-based Evaluation Method” was proposed for further improvement of performance indicators required in the real field, and the effectiveness was verified. The simulation environment and detailed results of experiments are described in this paper, and it is expected as guidance and reference research for applying these technologies in the field.
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- 작성일 2023-09-03
- 조회수 2800
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- [연구] 석사과정 임주빈, SCIE 논문지(MDPI applied sciences/Q2) 게재
- 석사과정 임주빈 학생(지도교수: 정종필)의 연구(Factory Simulation of Optimization Techniques Based on Deep Reinforcement Learning for Storage Devices)가 MDPI applied sciences(Impact Factor: 2.7 (2022); 5-Year Impact Factor: 2.9 (2022))에 게재됐다. https://www.mdpi.com/2076-3417/13/17/9690 / https://doi.org/10.3390/app13179690 논문요약 - In this study, reinforcement learning (RL) was used in factory simulation to optimize storage devices for use in Industry 4.0 and digital twins. Industry 4.0 is increasing productivity and efficiency in manufacturing through automation, data exchange, and the integration of new technologies. Innovative technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are smartly automating manufacturing processes and integrating data with production systems to monitor and analyze production data in real time and optimize factory operations. A digital twin is a digital model of a physical product or process in the real world. It is built on data and real-time information collected through sensors and accurately simulates the behavior and performance of a real-world manufacturing floor. With a digital twin, one can leverage data at every stage of product design, development, manufacturing, and maintenance to predict, solve, and optimize problems. First, we defined an RL environment, modeled it, and validated its ability to simulate a real physical system. Subsequently, we introduced a method to calculate reward signals and apply them to the environment to ensure the alignment of the behavior of the RL agent with the task objective. Traditional approaches use simple reward functions to tune the behavior of reinforcement learning agents. These approaches issue rewards according to predefined rules and often use reward signals that are unrelated to the task goal. However, in this study, the reward signal calculation method was modified to consider the task goal and the characteristics of the physical system and calculate more realistic and meaningful rewards. This method reflects the complex interactions and constraints that occur during the optimization process of the storage device and generates more accurate episodes of reinforcement learning in agent behavior. Unlike the traditional simple reward function, this reflects the complexity and realism of the storage optimization task, making the reward more sophisticated and effective.The stocker simulation model was used to validate the effectiveness of RL. The model is a storage device that simulates logistics in a manufacturing production area. The results revealed that RL is a useful tool for automating and optimizing complex logistics systems, increasing the applicability of RL in logistics. We proposed a novel method for creating an agent through learning using the proximal policy optimization algorithm, and the agent was optimized by configuring various learning options. The application of reinforcement learning resulted in an effectiveness of 30–100%, and the methods can be expanded to other fields.
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- 작성일 2023-08-28
- 조회수 2747
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- [연구] 석사과정 안지수, SCIE 논문지(MDPI Sensors/Q1) 게재
- 석사과정 안지수 학생(지도교수 : 정종필)의 연구(Federated Learning for Predictive Maintenance and Anomaly Detection Using Time Series Data Distribution Shifts in Manufacturing Processes)가 MDPI Sensors(Impact Factor: 3.9 (2022); 5-Year Impact Factor: 4.1 (2022))에 게재됐다. https://www.mdpi.com/1424-8220/23/17/7331/ or https://doi.org/10.3390/s23177331 논문요약 - In the manufacturing process, equipment failure is directly related to productivity, so predictive maintenance plays a very important role. Industrial parks are distributed, and data heterogeneity exists among heterogeneous equipment, which makes predictive maintenance of equipment challenging. In this paper, we propose two main techniques to enable effective predictive maintenance in this environment. We propose a 1DCNN-Bilstm model for time series anomaly detection and predictive maintenance of manufacturing processes. The model combines a 1D convolutional neural network (1DCNN) and a bidirectional LSTM (Bilstm), which is effective in extracting features from time series data and detecting anomalies. In this paper, we combine a federated learning framework with these models to consider the distributional shifts of time series data and perform anomaly detection and predictive maintenance based on them. In this paper, we utilize the pump dataset to evaluate the performance of the combination of several federated learning frameworks and time series anomaly detection models. Experimental results show that the proposed framework achieves a test accuracy of 97.2%, which shows its potential to be utilized for real-world predictive maintenance in the future.
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- 작성일 2023-08-22
- 조회수 2725
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- [연구] 석사과정 이지은, SCIE 논문지(MDPI Electronics/Q2) 게재
- 석사과정 이지은 학생(지도교수 : 정종필)의 연구(Real-Time Pose Estimation Based on ResNet-50 for Rapid Safety Prevention and Accident Detection for Field Workers)가 MDPI Electronics(Impact Factor: 2.9 (2022); 5-Year Impact Factor: 2.9 (2022))에 게재됐다. https://www.mdpi.com/2079-9292/12/16/3513 / https://doi.org/10.3390/electronics12163513 논문요약 - The present study proposes a Real-Time Pose Estimation technique using OpenPose based on ResNet-50 that enables rapid safety prevention and accident detection among field workers. Field workers perform tasks in high-risk environments, and accurate Pose Estimation is a crucial aspect of ensuring worker safety. However, it is difficult for Real-Time Pose Estimation to be conducted in such a way as to simultaneously meet Real-Time processing requirements and accuracy in complex environments. To address these issues, the current study uses the OpenPose algorithm based on ResNet-50, which is a neural network architecture that performs well in both image classification and feature extraction tasks, thus providing high accuracy and efficiency. OpenPose is an algorithm specialized for multi-human Pose Estimation that can be used to estimate the body structure and joint positions of a large number of individuals in real time. Here, we train ResNet-50-based OpenPose for Real-Time Pose Estimation and evaluate it on various datasets, including actions performed by real field workers. The experimental results show that the proposed algorithm achieves high accuracy in the Real-Time Pose Estimation of field workers. It also provides stable results while maintaining a fast image processing speed, thus confirming its applicability in real field environments.
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- 작성일 2023-08-19
- 조회수 3540