The ISO 9000 Quality Management System(QMS) standard is an effective tool that can consistently meet the various requirements of stakeholders and promote customer satisfaction management. However, in the recent business environment, not only quality issues but also risks in various other areas are increasing. In particular, risks related to product safety problems which are arised from product convergence, diversification of methods of production and globalization of supply chain pose a great threat to the sustainability of companies. Accordingly, ISO published standards of Product Safety Management System(PSMS) for suppliers such as ISO 10377 and ISO 10393. This study aims to suggest an integration framework of QMS and PSMS based on ISO international standards. To this end, the relationship of QMS and PSMS are analyzed and the requirements of the both systems are integrated based on the Plan-Do-Check-Act(PDCA) cycle and risk-based thinking. In addition, guidelines that can systemize the integrated requirements are presented in the aspect of processes and documentation. This study is expected to be used as a guideline that helps companies that have already acquired QMS certification to build an international level product safety management system early.
The diaphragm is an important part because it plays an important role in changing the flow direction of hightemperature and high-pressure steam in the steam turbine. Because it is subjected to high pressure by high temperature steam, there should be great concerns about breakage of parts, runouts due to vibration by rotating parts, and deformation due to creep effect and fatigue breakage due to long-term use in high temperature environments. In order to ensure the safety of turbine components in such a harsh environment, structural analysis should be prioritized prior to manufacturing prototypes. In this study, in order to verify the design stability of the diaphragm, physical safety is checked through static analysis, vibration analysis, and fatigue analysis, and the fatigue life is predicted. The total deformation, equivalent stress, and strain are determined by static analysis, and the stress and total deformation by the harmonic response are obtained through vibration analysis, and the stability is judged by comparing it with the characteristic value. We intend to verify the safety of the design and propose a complementary diaphragm design.
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.
This study suggests a machine learning model for predicting the production quality of free-machining 303-series stainless steel small rolling wire rods according to the manufacturing process's operation condition. The operation condition involves 37 features such as sulfur, manganese, carbon content, rolling time, and rolling temperature. The study procedure includes data preprocessing (integration and refinement), exploratory data analysis, feature selection, machine learning modeling. In the preprocessing stage, missing values and outlier are removed, and variables for the interaction between processes and quality influencing factors identified in existing studies are added. Features are selected by variable importance index of lasso regression, extreme gradient boosting (XGBoost), and random forest models. Finally, logistic regression, support vector machine, random forest, and XGBoost is developed as a classifier to predict good or defective products with new operating condition. The hyper-parameters for each model are optimized using k-fold cross validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963 and logarithmic loss of 0.0209. In this study, the quality prediction model is expected to be able to efficiently perform quality management by predicting the production quality of small rolling wire rods in advance.
Strong naval power is needed to protect the sea, the lifeline of the national economy and people's lives. The navy has been operating with the increase of forces and the selection of officers to achieve its mission, and long-term service officers are selected every year. In this study, problems were identified through the analysis of relative influence of the long-term service selection system for naval officers by evaluation factors. As a result of relative influence on the selection data for long-term service of 203 officers over the past 3 years(2018∼2020) showed education results(25.05%) > english ability(23.33%) > work evaluation(11.23%) > prize(10.91%). In order to equal the relative influence and the rate of allocation by evaluation factors, higher the score of work evaluation, command recommendation, and physical strength, lower the score of education results, english ability, and prize were required. Sensitivity analysis was conducted after suggesting the alternatives that adjusted the scoring by ±2∼5, ±2∼10 points. As a result of calculating the relative influence on the alternatives, rankings of the score and the relative influence gradually became similar.