Carbon black is a material in the form of fine black powder obtained by incomplete combustion or pyrolysis of hydrocarbons, and is composed of 90-99% carbon, and the rest is composed of hydrogen and oxygen. In the event of an emergency during the manufacture of carbon black, the generated tail gas should be safely discharged through an emergency line to prevent fire, explosion, and environmental pollution accidents caused by the tail gas. If the pressure continues to rise, the pressure control valve shall operate and the rupture plate shall be ruptured sequentially and the tail gas shall be discharged to the vent stack through the emergency line. As an emergency emission system, even if some untreated substances in the tail gas are released into the atmosphere, they are lighter than air, so it is safe to discharge them to a safe place through the Vent Stack. If the gas pressure is rising or worse, it is discharged from the Vent Stackine, and discharging fuel.

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.

한국의 전통 과자인 유과는 거의 모든 공정이 수작업으로 제조되기 때문에 공정 중 작업자 및 작업도구 등에 의한 교차오염으로 인한 안전문제가 제기되고 있다. 본 연구에서는 유과 제조 공정 중 찹쌀의 불림 단계에서 증가된 미생물을 제어하기 위하여 항균 활성이 있는 용액 9가지 용액(40% 자몽종자 추출물, 100% 계피 추출물, 70% 에탄올, 100% 식초, 0.2% 차아염소산나트륨, 40% 초산나트륨, 40% 탄산수소나트륨, 40% 사과산, 40% 구연산)을 이용하여 Escherichia coli, Staphylococcus aureus, Listeria monocytogenes, Salmonella typhimurium, Bacillus cereus에 대해 항균 효과를 비교 확인하였다. Disk diffusion법을 통해 40% 사과산과 구연산에서 높은 항균 효과를 확인할 수 있었고, 추가로 항균용액의 농도, 처리방법 및 시간을 최적화한 결과 1% 사과산-구연산을 10분 동안 처리 시 세균 5종을 모두 사멸시켜 높은 항균효과를 확인할 수 있었다. 불림단계의 찹쌀에 1% 사과산-구연산으로 정치 및 교반하면서 10분 동안 세척하였을 때 일반세균과 대장균군의 수가 각각 4.0 Log CFU/g와 5.0 Log CFU/g씩 감소하였다. 이상의 결과로 볼 때 1% 사과산-구연산으로 불린 찹쌀에 대해 침지와 교반으로 10분 동안 처리함으로써 전통 유과 제조과정 중 쌀의 불림단계에서 증가된 미생물을 효과적으로 저감화 할 수 있음을 확인하였다.

The global demand for raw lithium materials is rapidly increasing, accompanied by the demand for lithiumion batteries for next-generation mobility. The batch-type method, which selectively separates and concentrates lithium from seawater rich in reserves, could be an alternative to mining, which is limited owing to low extraction rates. Therefore, research on selectively separating and concentrating lithium using an electrodialysis technique, which is reported to have a recovery rate 100 times faster than the conventional methods, is actively being conducted. In this study, a lithium ion selective membrane is prepared using lithium lanthanum titanate, an oxide-based solid electrolyte material, to extract lithium from seawater, and a large-area membrane manufacturing process is conducted to extract a large amount of lithium per unit time. Through the developed manufacturing process, a large-area membrane with a diameter of approximately 20 mm and relative density of 96% or more is manufactured. The lithium extraction behavior from seawater is predicted by measuring the ionic conductivity of the membrane through electrochemical analysis.

There is a considerable amount of research on metal material product worker’s hearing loss caused by noise that comes from manufacturing process. A further investigation that characterizes the sound that comes from manufacturing process of metal material products. however. To do this, a noise management plan is needed. It should include a generated sound process from the main sources of disturbance at manufacturing process areas. And a soundproof measurement will identify the amount of noise reduction needed for a hearing-safe working environment. Finally, researchers in this study measured tests on the noise and the vibration process, and the noise caused by operations allowed for an investigation on the suitability of certain environmental conditions. Noise-related programs can be used to predict the noise distribution of the noise level characteristic. This can help identify and reduce the presence of sound interference through sound proofing measures.

Manufacturing process mining performs various data analyzes of performance on event logs that record production. That is, it analyzes the event log data accumulated in the information system and extracts useful information necessary for business execution. Process data analysis by process mining analyzes actual data extracted from manufacturing execution systems (MES) to enable accurate manufacturing process analysis. In order to continuously manage and improve manufacturing and manufacturing processes, there is a need to structure, monitor and analyze the processes, but there is a lack of suitable technology to use. The purpose of this research is to propose a manufacturing process analysis method using process mining and to establish a manufacturing process mining system by analyzing empirical data. In this research, the manufacturing process was analyzed by process mining technology using transaction data extracted from MES. A relationship model of the manufacturing process and equipment was derived, and various performance analyzes were performed on the derived process model from the viewpoint of work, equipment, and time. The results of this analysis are highly effective in shortening process lead times (bottleneck analysis, time analysis), improving productivity (throughput analysis), and reducing costs (equipment analysis).

본 연구에서는 비가열 떡 제조업체 3곳을 대상으로 원 재료, 제조설비 및 계절별 제조공정에 대한 미생물 오염 도를 분석하였고, 여름철 불림 시간에 따른 미생물 오염 도 및 불림 수 온도 조절을 통한 미생물 저감 효과를 확 인하고자 하였다. 3업체의 원재료 일반세균수는 2.69-5.08 log CFU/g 범위로 검출되었으나 제조공정 중 불림공정에 서 미생물 오염도가 급격히 증가함을 확인하였다. 계절에 따른 제조공정별 미생물 오염도 분석결과, 여름철 불림공 정에서 일반세균 및 대장균군이 7.01 및 3.96 log CFU/g 로 다른 계절에 비해 유의적으로 높게 나타났고, 이후 공 정에서도 높은 오염도를 유지하여 냉동공정에서 일반세균 이 6.24 log CFU/g로 법적인 기준을 초과하여 검출되었다. 여름철 불림 초기 수온은 19.1oC에서 불림 12시간 후 26.8oC까지 상승하였고, 불림시간에(3, 6, 9, 12 h) 따른 제조공정별 미생물 오염도 분석결과, 불림시간이 길어질수 록 미생물 오염도가 유의적으로 높게 나타났고, 불림 9시 간 이후부터 냉동공정의 일반세균 수치가 냉동식품의 법 적인 기준을 초과하여 검출되었다. 여름철 불림 수의 온 도 상승을 억제하기 위하여 얼음팩을 이용하여 불림 수온 을 조절한 결과, 불림 12시간까지 20.1oC로 유지되어 조 절 전보다 약 7oC가량 낮게 나타났다. 이에 따른 제조공 정별 미생물 오염도 분석결과, 3업체의 불림 12시간 이후 냉동공정 일반세균 평균값이 4.42 log CFU/g로 조절 전보 다 1.77 log CFU/g 감소한 것으로 확인되었다. 이상의 결 과로 볼 때, 안전한 비가열 냉동떡 생산을 위해서는 업체 에 맞는 불림시간 및 불림 수 온도조절 등의 선행요건 관 리기준 설정이 필요하며, 이러한 선행요건 관리기준의 적 용으로 비가열 냉동떡 제조 HACCP system의 안전성이 확립될 수 있을 것으로 판단되었다.

This study investigated the method of continuous improvement of small-medium company production processes through POSCO's QSS(Quick Six Sigma) activities. QSS is a field operation technique that encompasses the advantages of Six Sigma, TPS(Toyota Production System), TQM (Total Quality Management), and IE(Industrial Engineering). Through this, POSCO not only encourages activities centered on related small and medium-sized partners, etc., but is also expected to contribute to the continuous improvement of the company's own production process through QSS activities. In this study, rather than unconditionally carrying out activities according to the needs of large companies, the research is to help the continuous improvement of the actual production process of small and medium-sized enterprises by effectively applying and spreading QSS activities in consideration of the characteristics and environment of the company. For this purpose, empirical research is conducted on the process improvement activities and QSS activities of company Y, which has less than 100 assembly and production quality and inspection processes among SMEs. The changes in the production process improvement of SMEs through the application of the final QSS were investigated through empirical studies.

The utilization of carbonaceous reinforcement-based polymer matrix composites in structural applications has become a hot topic in composite research. Although conventional carbon fiber-reinforced polymer composites (CFRPs) have revolutionized the composite industry by offering unparalleled features, they are often plagued with a weak interface and lack of toughness. However, the promising aspects of carbon fiber-based fiber hybrid composites and hierarchical composites can compensate for these setbacks. This review provides a meticulous landscape and recent progress of polymer matrixbased different carbonaceous (carbon fiber, carbon nanotube, graphene, and nanodiamond) fillers reinforced composites’ mechanical properties. First, the mechanical performance of neat CFRP was exhaustively analyzed, attributing parameters were listed down, and CFRPs’ mechanical performance barriers were clearly outlined. Here, short carbon fiber-reinforced thermoplastic composite was distinguished as a prospective material. Second, the strategic advantages of fiber hybrid composites over conventional CFRP were elucidated. Third, the mechanical performance of hierarchical composites based on carbon nanotube (1D), graphene (2D) and nanodiamond (0D) was expounded and evaluated against neat CFRP. Fourth, the review comprehensively discussed different fabrication methods, categorized them according to performance and suggested potential future directions. From here, the review sorted out three-dimensional printing (3DP) as the most futuristic fabrication method and thoroughly delivered its pros and cons in the context of the aforementioned carbonaceous materials. To conclude, the structural applications, current challenges and future prospects pertinent to these carbonaceous fillers reinforced composite materials were elaborated.

Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF6) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF6) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF6) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF6) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF6 manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

목적: 본 연구는 국내의 기성 돋보기가 광학적 기준에 부합여부와 조제가공 상태의 대칭성에 대하여 측정 평가하고자 하였다. 방법 : 국내에 유통 중인 한 종류의 기성 돋보기 100개(+1.00, +2.00,+3.00 와 +4.00 D)를 대상으로 측정하였다. 기성 돋보기의 광학적 품질은 렌즈 굴절력, 광학중심점간 거리, 광학중심점높이가 측정되었다. 측정된 값의 허용오차는 ISO 8980-1, ISO 16034:2002 그리고 RAL-RG-915를 기준으로 분석되었다. 인위적으로 발생한 수평수직 프리즘은 광학중심점간 거리와 남녀 평균동공간 거리의 오차로 프렌티스 공식으로 계산하였다. 추가적으로 기성 돋보기의 조제과정상 오류로 인한 비대칭성은 단안광학중심점간 거리와 두 렌즈간의 광학중심점간 높이의 오차로 측정하였다. 결과 : 100개의 기성 돋보기 중 23%가 ISO 8980-1의 렌즈굴절력 기준을 만족하지 못하였다. 본 연구의 기성 돋보기의 광학중심점간 거리는 62.06±1.41 mm이었으며, 남녀의 평균 동공간 거리와의 오차로 발생하는 수평프 리즘은 남성은 0.53±0.54 △ BI(0.18~1.06 △), 여성은 0.98±0.68 △ BI(0.37~1.78 △). 테의 중심에서 각 렌 즈광학중심점까지의 거리에서는 85%가 최소 1 mm이상의 오차를 보였다. 기성 돋보기 양쪽 렌즈 간 광학중심점 높이 차이는 1.26±0.83 mm이었으며, 이로 인한 발생되는 수직프리즘은 0.12~0.45 △이었다. 결론 : 많은 수의 기성 돋보기가 요구되는 광학적 품질에 미달되었다. 기성 돋보기는 착용자 개인별 안면형상을 고려하지 못하기 때문에, 광학중심점간 거리와 동공간거리의 오차가 발생하고 이로 인한 많은 정도의 수평프리 즘이 발생하였다. 따라서 광학적으로 잘못된 기성 돋보기의 사용은 시각적 편안함을 제공하는 것보다는 시각적 부담을 야기할 수 있을 것이며, 이런 문제를 방지하기 위하여 전문가를 통한 확인 과정이 반드시 필요하다.

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.