본 연구는 시멘트 산업의 대체연료(폐합성수지 등) 사용량 증대에 따라 이를 활용한 탄소배출 저감 및 시멘트/콘크리트 제조 적용 기술 및 방안에 대해 검토하고자 했으며, 향후 시멘트 산업의 탄소중립 실현을 위한 기초 자료로써 활용하고자 한다. 시멘트 제조 에 있어 폐합성수지 사용은 경제적 장점과 높은 발열량으로 인해 연료로서의 가치가 높은 것으로 나타났으며, 열경화성 수지는 부가가 치가 높은 저탄소 시멘트 복합체의 비반응성 골재로 작용할 수 있는 것으로 확인되었으며, 감마선 조사는 다양한 폐플라스틱의 성능 평가에 적용되는 것으로 확인되었다.

This study develops a machine learning-based tool life prediction model using spindle power data collected from real manufacturing environments. The primary objective is to monitor tool wear and predict optimal replacement times, thereby enhancing manufacturing efficiency and product quality in smart factory settings. Accurate tool life prediction is critical for reducing downtime, minimizing costs, and maintaining consistent product standards. Six machine learning models, including Random Forest, Decision Tree, Support Vector Regressor, Linear Regression, XGBoost, and LightGBM, were evaluated for their predictive performance. Among these, the Random Forest Regressor demonstrated the highest accuracy with R2 value of 0.92, making it the most suitable for tool wear prediction. Linear Regression also provided detailed insights into the relationship between tool usage and spindle power, offering a practical alternative for precise predictions in scenarios with consistent data patterns. The results highlight the potential for real-time monitoring and predictive maintenance, significantly reducing downtime, optimizing tool usage, and improving operational efficiency. Challenges such as data variability, real-world noise, and model generalizability across diverse processes remain areas for future exploration. This work contributes to advancing smart manufacturing by integrating data-driven approaches into operational workflows and enabling sustainable, cost-effective production environments.

MES(manufacturing execution system) plays a critical role in improving production efficiency by managing operations across the entire manufacturing system. Conventional manufacturing systems employ a centralized control structure, which has limitations in terms of the flexibility, scalability and reconfigurability of the manufacturing system. Agent-based manufacturing systems, on the other hand, are better suited to dynamic environments due to their inherent high autonomy and reconfigurability. In this study, we propose an agent-based MES and present its collaboration model between agents along with a data structure. The agent-based MES consists of three types of core agents: WIPAgent, PAgent(processing agent), and MHAgent(material handling agent). The entire manufacturing execution process operates through collaboration among these core agents, and all collaboration is carried out through autonomous interactions between the agents. In particular, the order-by-order dispatching process and the WIP(work-in-process) routing process are represented as respective collaboration models to facilitate understanding and analyzing the processes. In addition, we define data specifications required for MES implementation and operation, and their respective structures and relationships. Moreover, we build a prototype system employing a simulation model of an exemplary shop-floor as a simulation test bed. The framework proposed in this study can be used as a basis for building an automated operating system in a distributed environment.

This study explores the utilization level of smart manufacturing systems in the value chain processes of manufacturing and empirically examines the effect of the utilization level of these systems on manufacturing competitiveness in SMEs. Smart manufacturing systems in the value chain processes are categorized into Sales, Purchasing, Production & Logistics, and Support systems. By analyzing the research model using structural equation modeling, this study identifies that Sales systems, Purchasing systems, Production & Logistics systems, and Support systems have a significant impact on manufacturing process efficiency. Additionally, Production & Logistics systems and manufacturing process efficiency positively and significantly influence manufacturing competitiveness. The findings suggest that the utilization of information is directly and positively related to manufacturing process efficiency, including reducing lead-time, decreasing work performance man-hours (M/H), and improving work accuracy. These improvements ultimately have a significant impact on manufacturing competitiveness. In conclusion, the use of smart manufacturing systems is becoming an integral part of the manufacturing industry. To gain a competitive edge, it will be necessary to introduce and utilize optimal smart manufacturing systems, taking into account the size of manufacturing firms.

Recently, in the manufacturing industry, changes in various environmental conditions and constraints appear rapidly. At this time, a dispatching system that allocates work to resources at an appropriate time plays an important role in improving the speed or quality of production. In general, a rule-based static dispatching method has been widely used. However, this static approach to a dynamic production environment with uncertainty leads to several challenges, including decreased productivity, delayed delivery, and lower operating rates, etc. Therefore, a dynamic dispatching method is needed to address these challenges. This study aims to develop a reinforcement learning-based dynamic dispatching system, in which dispatching agents learn optimal dispatching rules for given environmental states. The state space represents various information such as WIP(work-in-process) and inventory levels, order status, machine status, and process status. A dispatching agent selects an optimal dispatching rule that considers multiple objectives of minimizing total tardiness and minimizing the number of setups at the same time. In particular, this study targets a multi-area manufacturing system consisting of a flow-shop area and a cellular-shop area. Thus, in addition to the dispatching agent that manages inputs to the flow-shop, a dispatching agent that manages transfers from the flow-shop to the cellular-shop is also developed. These two agents interact closely with each other. In this study, an agent-based dispatching system is developed and the performance is verified by comparing the system proposed in this study with the existing static dispatching method.

This study optimized the gelling agent and rice protein ratio for developing elderly friendly jelly using a response surface methodology. Response surface analysis was conducted with a gelling agent (0.1, 0.2, and 0.3%) and rice protein (3, 6, and 9%) set as independent variables. Increasing the gelling agent and rice protein ratio raised the pH while lowering the total acidity. The sugar content decreased nonlinearly with a higher gelling agent ratio. The lightness (L) and yellowness (b) differed according to the addition ratios of each ingredient, and the hardness peaked at 0.3% gelling agent and 6% rice protein, but excessive rice protein addition led to a decrease in hardness. Response surface analysis indicated an optimal formulation of 0.16% gelling agent and 6.41% rice protein, with all response variables aligning within the predicted ranges, validating the model.

This study was conducted to verify the impact of hazardous risk factors in manufacturing workplaces on worker safety behaviors, focusing on the mediating effect of safety climate, and to establish safety management strategies in manufacturing workplaces and to suggest practical measures to improve worker safety. For this study, the results of the ‘10th Occupational Safety and Health Survey’ conducted by the Korea Occupational Safety and Health Agency’s Occupational Safety and Health Research Institute in 2021 were used as analysis data for 3,255 manufacturing workplaces with 20 or more regular workers. The data were analyzed using the SPSS 24.0 program for descriptive statistical analysis, validity and reliability verification, correlation and multiple regression analysis, and hierarchical regression analysis. As a result of the study, first, hazardous risk factors were confirmed to have a negative effect on workers' safety behaviors. Second, hazardous risk factors were confirmed to have a negative effect on safety climate. Third, safety climate was confirmed to have a positive effect on workers' safety behaviors. Fourth, it was verified that the safety climate had a partial mediating effect in the relationship between hazardous risk factors and workers’ safety behavior in the workplace. Through this study, it was found that hazardous risk factors had a negative effect on workers’ safety behavior. This emphasizes that efforts to systematically manage and minimize hazardous risk factors in the workplace are important in promoting workers’ safety behavior. In addition, it was confirmed that the safety climate had an important mediating effect in the relationship between hazardous risk factors and workers’ safety behavior. In other words, it can be seen that the safety climate can alleviate the negative effect of hazardous risk factors on workers’ safety behavior. These research results suggest that reducing hazardous risk factors in the workplace and improving the safety climate can have a positive effect on workers’ safety behavior practice, thereby preventing industrial accidents.

Organic-inorganic hybrid coating films have been used to increase the transmittance and enhance the physical properties of plastic substrates. Sol-gel organic-inorganic thin films were fabricated on polymethylmethacrylate (PMMA) substrates using a dip coater. Metal alkoxide precursor tetraethylsilicate (TEOS) and alkoxy silanes including decyltrimethoxysilane (DTMS), 3-glycidoxypropyltrimethoxysilane (GPTMS), phenyltrimethoxysilane (PTMS), 3-(trimethoxysilyl)propyl methacrylate (TMSPM) and vinyltrimethoxysilane (VTMS) were used to synthesize sol-gel hybrid coating solutions. Sol-gel synthesis was confirmed by the results of FT-IR. Cross-linking of the Si-O-Si network during synthesis of the sol-gel reaction was confirmed. The effects of each alkoxy silane on the coating film properties were investigated. All of the organicinorganic hybrid coatings showed improved transmittance of over 90 %. The surface hardness of all coating films on the PMMA substrate was measured to be 4H or higher and the average thickness of the coating films was measured to be about 500 nm. Notably, the TEOS/DTMS coating film showed excellent hydrophobic properties, of about 97°.

In this paper, we aim to improve the output quality of a food 3D printer through optimized component design and implementation. Existing 3D printers produce customized outputs according to consumer needs, but have problems with output speed and poor quality. In this paper, we aim to solve this problem through optimized design of unit parts such as the extruder, nozzle, guide, and external case. Fusion 360 was used for element design, and in the performance evaluation of the implemented system, the average precision was 0.06mm, which is higher than the non-repeatable precision of ±0.1㎜ of other products, and the feed speed of the existing system was evaluated to be more than twice as fast, from 70mm/s to 140mm/s. In the future, we plan to continuously research output elements that can produce texture and color and device control methods for convenience.

In this paper, the goal is to produce a target wheel that integrates the plate and CPS wheel among the components of the drive plate mounted on an automobile engine. We attempted to develop a manufacturing process technology for incremental forming of a target wheel with the desired thickness by rotating a disk-shaped thin plate material and deforming the plate using a forming device and tools. Incremental forming system was set up by establishing a forming process and designing and manufacturing the device and parts required for processing. It consists of a total of 4 stages of molding process, and the optimal roll design that can properly collect materials to prevent cracks or reverse steps at each stage is primarily important. After manufacturing the prototype, a material test was performed to confirm whether the mechanical properties of the deformed part were sufficient to make gear teeth.

In response to the global interest and efforts towards reducing plastic use and promoting resource recycling, there is a growing need to establish methods for recycling discarded fishing gear. In Korea, various technologies are being developed to recycle discarded fishing gear, but significant technical and policy challenges still remain. In particular, biodegradable gill nets require a pre-treatment process to separate biodegradable materials from other substances and to remove salt before recycling. Therefore, this study aims to develop a pre-treatment device for recycling biodegradable gill nets and to evaluate the feasibility of recycling them.

High-entropy alloys (HEAs) have been reported to have better properties than conventional materials; however, they are more expensive due to the high cost of their main components. Therefore, research is needed to reduce manufacturing costs. In this study, CoCrFeMnNi HEAs were prepared using metal injection molding (MIM), which is a powder metallurgy process that involves less material waste than machining process. Although the MIM-processed samples were in the face-centered cubic (FCC) phase, porosity remained after sintering at 1200°C, 1250°C, and 1275°C. In this study, the hot isostatic pressing (HIP) process, which considers both temperature (1150°C) and pressure (150 MPa), was adopted to improve the quality of the MIM samples. Although the hardness of the HIP-treated samples decreased slightly and the Mn composition was significantly reduced, the process effectively eliminated many pores that remained after the 1275°C MIM process. The HIP process can improve the quality of the alloy.

Additive Manufacturing (AM) is a process that fabricates products by manufacturing materials according to a three-dimensional model. It has recently gained attention due to its environmental advantages, including reduced energy consumption and high material utilization rates. However, controlling defects such as melting issues and residual stress, which can occur during metal additive manufacturing, poses a challenge. The trial-and-error verification of these defects is both time-consuming and costly. Consequently, efforts have been made to develop phenomenological models that understand the influence of process variables on defects, and mechanical/ electrical/thermal properties of geometrically complex products. This paper introduces modeling techniques that can simulate the powder additive manufacturing process. The focus is on representative metal additive manufacturing processes such as Powder Bed Fusion (PBF), Direct Energy Deposition (DED), and Binder Jetting (BJ) method. To calculate thermal-stress history and the resulting deformations, modeling techniques based on Finite Element Method (FEM) are generally utilized. For simulating the movements and packing behavior of powders during powder classification, modeling techniques based on Discrete Element Method (DEM) are employed. Additionally, to simulate sintering and microstructural changes, techniques such as Monte Carlo (MC), Molecular Dynamics (MD), and Phase Field Modeling (PFM) are predominantly used.

The growing significance of sustainable energy technologies underscores the need for safe and efficient management of spent nuclear fuels (SNFs), particularly via deep geological disposal (DGD). DGD involves the long-term isolation of SNFs from the biosphere to ensure public safety and environmental protection, necessitating materials with high corrosion resistance for DGD canisters. This study investigated the feasibility of a Cu–Ni film, fabricated via additive manufacturing (AM), as a corrosion-resistant layer for DGD canister applications. A wire-fed AM technique was used to deposit a millimeter-scale Cu–Ni film onto a carbon steel (CS) substrate. Electrochemical analyses were conducted using aerated groundwater from the KAERI underground research tunnel (KURT) as an electrolyte with an NaCl additive to characterize the oxic corrosion behavior of the Cu–Ni film. The results demonstrated that the AM-fabricated Cu–Ni film exhibited enhanced corrosion resistance (manifested as lower corrosion current density and formation of a dense passive layer) in an NaCl-supplemented groundwater solution. Extensive investigations are necessary to elucidate microstructural performance, mechanical properties, and corrosion resistance in the presence of various corroding agents to simplify the implementation of this technology for DGD canisters.

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.