This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

This study selected two labor-intensive processes in harsh environments among domestic food production processes. It analyzed their improvement effectiveness using 3-dimensional (3D) simulation. The selected processes were the “frozen storage source transfer and dismantling process” (Case 1) and the “heavily loaded box transfer process” (Case 2). The layout, process sequence, man-hours, and output of each process were measured during a visit to a real food manufacturing factory. Based on the data measured, the 3D simulation model was visually analyzed to evaluate the operational processes. The number of workers, work rate, and throughput were also used as comparison and verification indicators before and after the improvement. The throughput of Case 1 and Case 2 increased by 44.8% and 69.7%, respectively, compared to the previous one, while the utilization rate showed high values despite the decrease, confirming that the actual selected process alone is a high-fatigue and high-risk process for workers. As a result of this study, it was determined that 3D simulation can provide a visual comparison to assess whether the actual process improvement has been accurately designed and implemented. Additionally, it was confirmed that preliminary verification of the process improvement is achievable.

In the era of the 4th industrial revolution driven by the convergence of ICT(information and communication technology) and manufacturing, research on smart factories is being actively conducted. In particular, the manufacturing industry prefers smart factories that autonomously connect and analyze data. For the efficient implementation of smart factories, it is essential to have an integrated production system that vertically integrates separately operated production equipment and heterogeneous S/W systems such as ERP, MES. In addition, it is necessary to double-verify production data by using automatic data collection technology so that the production process can be traced transparently. In this study, we want to show a case of data-centered integration of a large aircraft parts processing factory that requires high precision, takes a long time, and has the characteristics of processing large raw materials. For this, the components of the data-oriented integrated production system were identified and the connection structure between them was explained. And we would like to share the experience gained through the design and implementation case. The integrated production system proposed in this study integrates internal components based on data, which is expected to serve as a basis for SMEs to develop into an advanced stage, and traces materials with RFID technology.

In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

This study studied a system that can redesign the production site layout and respond with dynamic simulation through fabric production process innovation for smart factory promotion and digital-oriented decision making of the production process. We propose to reflect the required throughput and throughput per unit facility of fabric production process as probability distribution, and to construct data-driven metabolism such as data collection, data conversion processing, data rake generation, production site monitoring and simulation utilization. In this study, we demonstrate digital-centric field decision smartization through architectural design for the smartization of fabric production plants and dynamic simulations that reflect it.

As the development environment is changing with the development of information communication technology, the systems that were used by each service became used with integration. In the process of integrating from existing legacy systems to new system, it should be smoothly integrated or shared, however, it cannot help holding existing technology or component due to significant cost burden for conversion. In this paper, it was not only classified by types with analyzing the various elements that make up legacy system but an approach and monitoring system were developed to each type. After System application results, data's information generated in each process is provided to other system in real time, so that it has not only secured the work efficiency and reliability but also it is made possible by integrating data in various formats for efficient data management, rapid search and tracking to history. With real-time monitoring system developed in this study, It can be very useful in a variety of industries which require real-time monitoring of distributed legacy system data.

Recently, the manufacturing system is being changed in a mass customization and small quantity batch production. MES is a powerful production management tool supporting production optimization from the process initiation to the final shipment. It is a production management system which plans and executes based on the production data in the shop floor. This study deployed the utilization of production data and web HMI system to process real-time production data through the collection with the shop floor. The developed system was applied to the equipment operating time and other production data could be processed with the real-time. The proposed system and web HMI can be applied for various production systems by using different logic.

무용은 한번 공연을 하면 뜬 구름과도 같이 사라져 버리는 휘발성이 강한 예술이다. 이 순간성을 극복하 는 길은 미약하나마 무용행위에 관한 다양한 데이터를 기록․생산하고 관리․보존하는 것이다. 그렇게 생산 되고 보존된 데이터는 무용인들에게 공유되어서 창작과 교육 그리고 연구의 기초자료로 활용된다. 그러나 다수의 무용가들은 연습실에서 연습을 할 때나 공연을 할 때 이러한 데이터의 구축에 대한 개념이 없는 경우 가 많다. 본 연구의 목적은 무용가들에게 데이터의 중요성을 인식하게 하게 하는 것과 창작현장에서 데이터를 생성 하고 관리하는 방법을 탐색하는 것이다. 한국의 무용가들이 창작 현장에서 생성하는 창작데이터의 유형은 일 반 학문의 데이터와는 많이 차이가 있다. 본 연구의 내용은 첫째, 무용연구에서 창작데이터의 특성은 어떤 것들이 있는가. 둘째, 무용가들은 어떻게 창작데이터를 생산하는가. 셋째, 무용데이터의 유형은 어떤 것들이 있나. 넷째, 무용에서 연구․창작데이터는 어떻게 보관․관리되는가. 마지막으로 이런 데이터는 어떻게 공유 되고 활용되는가이다. 무용연구자들이나 안무가들은 작품을 공연할 때 주로 동영상을 촬영하여 자료를 만들 고 그것을 보관하는 정도에서 창작데이터를 관리하고 있다. 다른 학문과는 다르게 ‘실기기반 무용연구’에서의 데이터는 창작자가 이에 대한 개념을 갖고 직접 생산하는 것이 중요하다. 직접 생산한 창작데이터와 다른 유 형의 데이터의 관리체계를 위해서 이의 장기보존 방식과 활용 방식에 대한 탐구들이 후속연구로 진행될 필요 가 있다.