Light-weight ceramic insulation materials and high-emissivity coatings were fabricated for reusable thermal protection systems (TPS). Alumina-silica fibers and boric acid were used to fabricate the insulation, which was heat treated at 1250 °C. High-emissivity coating of borosilicate glass modified with TaSi2, MoSi2, and SiB6 was applied via dip-and-spray coating methods and heat-treated at 1100°C. Testing in a high-velocity oxygen fuel environment at temperatures over 1100 °C for 120 seconds showed that the rigid structures withstood the flame robustly. The coating effectively infiltrated into the fibers, confirmed by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction analyses. Although some oxidation of TaSi2 occurred, thereby increasing the Ta2O5 and SiO2 phases, no significant phase changes or performance degradation were observed. These results demonstrate the potential of these materials for reusable TPS applications in extreme thermal environments.

This study evaluated the suitability of using Wickerhamomyces anomalus A1-5 isolated from solid grain fermentation broth for winemaking by comparing the quality and functionality of Campbell Early wine produced with single and mixed inoculations. The pH ranged from 3.43 to 3.68, with the highest value in treatment B. Soluble solids ranged from 5.0 to 7.7 °Brix. Total acidity was measured at 0.42% to 0.47%. Color analysis indicated a significant decrease in lightness with an increase in redness across all treatment groups compared to the control. Among aroma compounds, 8 alcohols, 6 esters, 3 acids, and 11 other compounds were identified, with the control having the highest alcohol content and treatment D having the highest ester content. Tannin and total polyphenol contents ranged from 46.46 mg% to 95.92 mg% and from 87.66 mg% to 147.21 mg%, respectively. Antioxidant activities measured by DPPH and ABTS assays ranged from 33.84% to 69.02% and from 42.43% to 89.18%, respectively, with treatment B exhibiting the highest activities. These results suggest that W. anomalus A1-5 may positively influence the quality and functionality of Campbell Early wine, presenting potential as a novel yeast strain for winemaking.

세계적인 탄소중립 정책 추진과 수소 에너지 수요 증가에 따라 고분자 전해질 수전해 및 연료전지 기술 개발이 활발히 이루어지고 있다. 해당 기술의 핵심 소재인 과불소계 술폰산 이오노머는 우수한 전기화학적 특성과 화학적 안정성을 가지고 있지만, 높은 제조비용, 한정된 공급망, 강화되는 환경 규제와 같은 문제로 인해 효과적인 재활용 및 재제조 기술이 요구되고 있다. 본 연구에서는 초임계 분산 기술을 통해 전해질막 및 막-전극접합체의 제조과정에서 발생하는 고활성을 갖는 전해질막 스크랩을 연료전지 전극바인더로 재제조하는 방법을 제시하고자 한다.

To utilize textured vegetable protein (TVP) instead of meat in kimchi stew, TVP of different sizes were added to kimchi stew under different cooking conditions. Canned Kimchi stew was prepared by adding processed TVP. Physicochemical quality characteristics and antioxidant activities of the broth, kimchi, and meat (or TVP) were measured. The pH and salinity did not show a significant difference between treatment groups in the broth or kimchi. However, the TVP treatment group showed higher pH and lower salinity than the control group. There was no significant difference in color between control group and TVP-treated groups. In terms of texture, the control group had the lowest hardness, gumminess, and chewiness, followed by TVP-1 and TVP-2 manufactured after pre-cooking, which showed lower hardness, gumminess, and chewiness. The smaller the size of the TVP, the lower the hardness, gumminess, and chewiness. Results of shear force were consistent with those of hardness. Contents of flavonoid and polyphenol compounds as antioxidant components did not increase or decrease with the addition of TVP. There were no significant differences in antioxidant activities among experimental groups.

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.

본 연구는 한국 정부의 코로나 19 지원정책이 한국 제조 기업의 혁신활동에 미 치는 영향을 실증분석한 연구이다. 선행연구에 따르면, 정부는 조세, 자금, 금융 지원 등 다양 한 정책을 통해 기업의 혁신활동을 촉진할 수 있으며, 실제 이러한 정책들이 기업의 혁신 활 동 및 성과에 긍정적 영향을 미치는 것으로 평가되어왔다. 그러나, 정부 지원 정책의 효과는 지원 유형과 기간에 따라 다를 수 있으며, 동일한 정책이라도 지원 분야 및 산업에 따라 그 효과가 달라질 수 있다. 본 연구는 과학기술정책연구원(STEPI)에서 제공하는 2020 한국기업 혁신조사(KIS)자료를 사용하여 팬데믹 기간 코로나19 지원정책의 효과를 실증분석하였다. 2017-2020년 한국 제조 산업에 속한 3,941개 기업을 대상으로 프로빗 및 성향점수매칭 방법 을 통해 분석한 결과, 팬데믹 기간 정부의 코로나 19 지원정책은 한국 제조 기업의 혁신활동 을 유지 및 증가시킬 확률을 높이는데 양의 유의미한 영향을 주었으며, 지원정책의 유형을 조세, 금융, 자금지원 정책으로 분리하여 분석해 보았을 때에도 개별 정책은 모두 기업의 혁 신활동을 유지 및 증가시킬 확률을 유의미하게 높이는 것으로 분석되었다.

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.

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.

2-hydroxyethyl methacrylate(HEMA)와 ethylene glycol dimethacrylate(EGDMA) 그리고 di(ethylene glycol) ethyl ether acrylate(DGA)와 polyethylene glycol methacrylate(PGM)를 이용하여 다양 한 콘택트렌즈를 제조하였다. 그 결과, DGA함량이 증가할수록 렌즈의 함수율이 증가하였고 접촉각도 증 가하였다. 인장 강도를 측정한 결과에서는 DGA함량이 증가할수록 강도는 감소하였다. DGA를 함유한 콘 택트 렌즈는 친수성 설질을 나타내므로 단백질 흡착에 대한 저항성이 HEMA만 사용했을 때 보다 DGA함 량이 높을수록 증가하였다. 렌즈의 미세 상분리 여부를 확인하기 위하여 렌즈 단면을 SEM으로 측정한 결 과 표면이 균일하게 일정하며 상분리 현상은 발견되지 않았다. 열 중합대신 에너지효율이 높은 광중합을 적용한 결과, PGM-1의 경우 40초안에 80%의 전환율을 나타냄을 확인할 수 있었다.

Silicon-based anode materials have attracted significant interest because of their advantages, including high theoretical specific capacity (~4,200 mAh/g), low working potential (0.4 V vs Li/Li+), and abundant sources. However, their significant initial capacity loss and large volume changes during cycling impede the application of silicon-based anodes in lithium-ion batteries. In this work, we propose a silicon oxide (SiOx) anode material for lithium-ion batteries produced with a magnesio-thermic reduction (MTR) process adopting Boryeong mud as a starting material. Boryeong mud contains various minerals such as clinochlore [(Mg,Fe)6(Si,Al)4O10(OH)8], anorthite (CaAl2Si2O8), illite [K0.7Al2(Si,Al)4O10(OH)2], and quartz (SiO2). The MTR process with Boryeong mud generates a mixture of amorphous silicon oxides (SiOx and SiO2), and magnesium aluminate which helps to alleviate the volume expansion of the electrode during charge/discharge. To observe the effects of these oxides, we conducted various analyses including X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-Transformation infrared spectroscopy (FT-IR), Brunauer-Emmett-Teller (BET) and cyclic voltammetry (CV) galvanic cell testing. The amorphous SiO2 and MgAl2O4 suppressed the volume expansion of the silicon-based anode, and excellent cycle performance was achieved as a result.