In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module ‘Matminer’, 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the ‘Fe’ compositional ratio. The feature importance method provided by ‘scikit-learn’ was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

In this study, based on the saturation magnetic flux density experimental values (Bs) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict Bs using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R2 score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict Bs above the threshold value of Bs (1.4 T). After obtaining two ANN models optimized for the R2 and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

Although the Ti–6Al–4V alloy has been used in the aircraft industry owing to its excellent mechanical properties and low density, the low formability of the alloy hinders broadening its applications. Recently, laser-powder bed fusion (L-PBF) has become a novel process for overcoming the limitations of the alloy (i.e., low formability), owing to the high degree of design freedom for the geometry of products having outstanding performance used in hightech applications. In this study, to investigate the effect of bulk shape on the microstructure and mechanical properties of L-PBFed Ti-6Al-4V alloys, two types of samples are fabricated using L-PBF: thick and thin samples. The thick sample exhibits lower strength and higher ductility than the thin sample owing to the larger grain size and lower residual dislocation density of the thick sample because of the heat input during the L-PBF process.

In this study, an Al82Ni7Co3Y8 (at%) bulk metallic glass is fabricated using gas-atomized Al82Ni7Co3Y8 metallic glass powder and subsequent spark plasma sintering (SPS). The effect of powder size on the consolidation of bulk metallic glass is considered by dividing it into 5 m or less and 20–45 m. The sintered Al82Ni7Co3Y8 bulk metallic glasses exhibit crystallization behavior and crystallization enthalpy similar to those of the Al82Ni7Co3Y8 powder with 5 m or less and it is confirmed that no crystallization occurred during the sintering process. From these results, we conclude that the Z-position-controlled spark plasma sintering process, using superplastic deformation by viscous flow in the supercooled liquid-phase region of amorphous powder, is an effective process for manufacturing bulk metallic glass.

Double-layer capacitors (DLCs) are developed with high surface electrodes to achieve a high capacitance value. In the present work, the initial bulk concentration of 1 mol/m3 and 3 mol /m3 are selected to show the consequential effects on the performance of a double-layer capacitor. A 1D model of COMSOL Multiphysics has been developed to analyze the electric field and potential in cell voltage, the electric displacement field and polarization induced by the field, and energy density in a double-layer structure. The electrostatics and the electric circuit modes in COMSOL are used to simulate the electrochemical processes in the double-layer structure. The analytical analysis of a double-layer capacitor with different initial bulk concentrations is investigated by using Poisson-Nernst-Plank equations. From the simulation results, the differential capacitance changes as a function of compact layer thickness and initial bulk concentration. The energy density varies with the differential capacitance and voltage window. The values of energy density are dominated by the interaction of ions in the solution and electrode surface.

Artificial graphites have been used in various applications, for example, as anode materials for Li-ion batteries, C/C composites, and electrodes for aluminum smelting, due to their unique mechanical strength and high thermal and electrical conductivity. Artificial graphites can be manufactured by a series of kneading, molding, carbonization and graphitization processes with an additional impregnation process. In this study, the influence of the process variables in the kneading and carbonization/graphitization process on the properties of the resulting carbon block was systemically investigated. During the kneading process, the optimum kneading temperature was 90 °C higher than the softening point of the binder pitch; thus, the binder pitch reached its maximum fluidity. On the other hand, during the carbonization and graphitization process, the structural properties of carbon blocks prepared at different heat treatment temperatures were examined and their structural change and evolution were closely described according to the temperature and divided into low-temperature carbonization and high-temperature carbonization/graphitization. Based on this study, we expect to provide a better understanding of setting the parameters for thermally conductive carbon block manufacturing.

The high level of lithium storage in synthetic porous carbons has necessitated the development of accurate models for estimating the specific capacity of carbon-based lithium-ion battery (LIB) anodes. To date, various models have been developed to estimate the storage capacity of lithium in carbonaceous materials. However, these models are complex and do not take into account the effect of porosity in their estimations. In this paper, a novel model is proposed to predict the specific capacity of porous carbon LIB anodes. For this purpose, a new factor is introduced, which is called normalized surface area. Considering this factor, the contribution of surface lithium storage can be added to the lithium stored in the bulk to have a better prediction. The novel model proposed in this study is able to estimate the lithium storage capacity of LIB anodes based on the porosity of porous carbons for the first time. Benefiting porosity value (specific surface area) makes the predictions quick, facile, and sensible for the scientists and experts designing LIBs using porous carbon anodes. The predicted capacities were compared with that of the literature reported by experimental works. The remarkable consistency of the measured and predicted capacities of the LIB anodes also confirms the validity of the approach and its reliability for further predictions.

Bulk graphite is manufactured using graphite scrap as the filler and phenolic resin as the binder. Graphite scrap, which is the by-product of processing the final graphite product, is pulverized and sieved by particle size. The relationship between the density and porosity is analyzed by measuring the mechanical properties of bulk graphite. The filler materials are sieved into mean particle sizes of 10.62, 23.38, 54.09, 84.29, and 126.64 μm. The bulk graphite density using the filler powder with a particle size of 54.09 μm is 1.38 g/cm3, which is the highest value in this study. The compressive strength tends to increase as the bulk graphite density increases. The highest compressive strength of 43.14 MPa is achieved with the 54.09 μm powder. The highest flexural strength of 23.08 MPa is achieved using the 10.62 μm powder, having the smallest average particle size. The compressive strength is affected by the density of bulk graphite, and the flexural strength is affected by the filler particle size of bulk graphite.

In the manufacturing of bulk graphite, pores produced by vaporization and discharge of volatile materials in binders during carbonization reduce the density of bulk graphite, which adversely affects the electrical conductivity, strength and mechanical properties. Therefore, an impregnation process is introduced to fill the pores and increase the density of bulk graphite. In this study, bulk graphite is prepared by varying the viscosity of the impregnant. The microstructure of bulk graphite is observed. The flexural strength and electrical resistivity are measured. As the viscosity of the impregnants decreases and the number of impregnations increases, it is shown that the number of pores decreases. The density before impregnation is 1.62 g/cm3. The density increases to 1.67 g/cm3 and porosity decreases by 18.6 % after three impregnations using 5.1 cP impregnant, resulting in the best pore-filling effect. After three times of impregnation with a viscosity of 5.1 cP, the flexural strength increases by 55.2 % and the electrical resistivity decreases by 86.76 %. This shows that a slight increase in density due to the pore-filling effect improves the properties of bulk graphite.

Pores produced by carbonization in bulk graphite process degrade the mechanical and electrical properties of bulk graphite. Therefore, the pores of bulk graphite must be reduced and an impregnation process needs to be performed for this reason. In this study, bulk graphite is impregnated by varying the viscosity of the impregnant. The pore volume and pore size distribution, according to the viscosity of the impregnant, are analyzed using a porosimeter. The total pore volume of bulk graphite is analyzed from the cumulative amount of mercury penetrated. The volume for a specific pore size is interpreted as the amount of mercury penetrating into that pore size. This decreases the cumulative amount of mercury penetrating into the recarbonized bulk graphite after impregnation because the viscosity of the impregnant is lower. The cumulative amount of mercury penetrating into bulk graphite before impregnation and after three times of impregnation with 5.1cP are 0.144 mL/g and 0.125 mL/gm, respectively. Therefore, it is confirmed that the impregnant filled the pores of the bulk graphite well. In this study, the impregnant with 5.1 cP, which is the lowest viscosity, shows the best effect for reducing the total pore volume. In addition, it is confirmed by Raman analysis that the impregnant is filled inside the pores. It is confirmed that phenolic resin, the impregnant, exists inside the pores through micro-Raman analysis from the inside of the pore to the outside.

Escherichia coli (E. coli) can easily contaminate the bovine mammary gland; hence, monitoring of bulk tank milk (BTM) samples helps detect mastitis. In this study, we investigated the characteristics of E. coli from BTM of a dairy company. A total of 58 BTM batches were collected from 29 dairy farms of a dairy company, and 40 E. coli were tested in this study. E. coli showed the highest resistance to cephalothin (52.4%), followed by colistin (28.6%), ampicillin (19.0%) and tetracycline (19.0%). However, E. coli showed low resistance from 0% to 9.5% against other 14 antimicrobials. In the distribution of antimicrobial resistant gene, β-lactamases genes blaOXA and tetracyclines-resistant gene tetB were detected in 15 (37.5%) and 2 (5.0) isolates, respectively, and blaTEM, aac (6')-Ib, aac (3)-II, tetA, sul1 and sul2 genes were only detected once in each (2.5%) isolate. The O127 (22.5%) serogroup was the most predominant, but all E. coli isolates were divided into 20 serogroups. The occurrence of antimicrobial-resistant bacterial infection can be problematic as it not only deteriorates the milk quality but also limits the therapeutic choices of antimicrobials in humans. Therefore, continuous monitoring is recommended to track antimicrobial-resistant E. coli in BTM and raw milk.

The method of evaluating the forming limit of sheet metal is using the forming limit diagram(FLD), and the test method for measuring forming limit curve(FLC) is ISO standardized. On the other hand, in the case of metal bulk materials, it was confirmed that the forming limit was defined by using various predictive models based on the ductile fracture theory. However it did not show a constant forming limit (limit damage value) depending on the shape of the specimen. Therefore, a study was conducted on the derivation of the triaxial stress curve to predict the fracture of the material for various stress triaxiality, not the existing limit damage value.

덤프 트럭 데크의 경량화를 통한 연료 소비를 줄이고 에코 친화적인 설계를 위해서는 정확한 구조 분석이 필요하다. 지금까지 데크의 하중은 정수압 또는 토압 이론을 기반으로 계산되었다. 이 방법으로 데크의 하중 불균일을 계산할 수 없다. 하중 분포는 골재 입자의 크기 분포 및 상호 작용에 따라 달라진다. 이산요소법은 유한요소법보다 효과적으로 골재의 거동을 시뮬레이션할 수 있다. 본 논문에 서는 벌크 밀도와 안식각을 측정하여 주요 특성을 얻었다. 15톤 덤프 트럭 데크는 범핑, 브레이킹 및 회전 시의 운동 조건을 적용하여 얻은 하중을 사용했다. 시뮬레이션은 이산요소해석 소프트웨어인 EDEM을 사용했다. 데크의 응력 및 변형 분포는 NASTRAN에 의해 계산되었다. 측정된 값과 비교하였고, 이를 통해 DEM 시뮬레이션의 결과는 수학적 가정에 의한 결과보다 정확함을 확인하였다.