The demand for automated diagnostic facilities has increased due to the rise in high-risk infectious diseases. However, small and medium-sized centers struggle to implement full automation because of limited resources. An integrated molecular diagnostics automation system addresses this issue by integrating small-scale automated facilities for each diagnostic process. Nonetheless, determining the optimal number of facilities and human resources remains challenging. This study proposes a methodology combining discrete event simulation and a genetic algorithm to optimize job-shop facility layout in the integrated molecular diagnostics automation system. A discrete event simulation model incorporates the number of facilities, processing times, and batch sizes for each step of the molecular diagnostics process. Genetic algorithm operations, such as tournament, crossover, and mutation, are applied to derive the optimal strategy for facility layout. The proposed methodology derives optimal facility layouts for various scenarios, minimizing costs while achieving the target production volume. This methodology can serve as a decision support tool when introducing job-shop production in the integrated molecular diagnostics automation system

In the development of a digital multi-process welding machine, we aimed to analyze the heat dissipation effects resulting from changes in the transformer's shape. Two installation configurations for the transformer, vertical and horizontal, were proposed. Thermal-flow analysis was conducted for the welding machine, taking into account variations in spacing between each proposed configuration. The results indicated that the shape and spacing of the components did not significantly alter the airflow around the reactor coil, which is the main heat-generating component of the machine. When comparing the heat dissipation effects across models with different transformer spacings, it was observed that models with narrower spacing exhibited improved heat dissipation, while the vertical configuration demonstrated a slightly higher heat dissipation effect overall. Transient analysis revealed the irregularities in internal flow and the resulting scattered temperature distribution over time within the welding machine.

This study presents the synthesis, characterization, and utilization of marine macroalgae-derived bio-carbon catalysts (BC and KOH-AC) for the efficient conversion of waste cooking oil (WCO) into biodiesel. The biochar (BC) was produced through slow pyrolysis of macroalgal biomass, which was subsequently activated with potassium hydroxide (KOH) to produce a KOH-modified activated carbon (KOH-AC) catalyst. Advanced characterization techniques, including SEM, EDX, XRD, FTIR, and TGA, were used to examine the physicochemical characteristics of the catalysts. The synthesized catalysts were utilized to produce biodiesel from WCO, and the results revealed that the highest biodiesel yields, 98.96%, and 47.54%, were obtained using KOH-AC and BC catalysts, respectively, under optimal reaction conditions of 66 °C temperature, 12.3 M/O molar ratio, 130 min time, and 3.08 wt.% catalyst loading via RSM optimization. The kinetic and thermodynamic parameters, such as k, Ea, ΔH, ΔS, and ΔG, were determined to be 0.0346 min− 1, 43.31 kJ mol− 1, 38.98 kJ mol− 1, − 158.38 J K− 1 mol− 1, and 92.58 kJ mol− 1, respectively. The KOH-AC catalyst was recycled up to five times, with a significant biodiesel yield of 80.37%. The fuel properties of the biodiesel met ASTM (D6751) specifications, ensuring that it has excellent fuel characteristics and can be used as an alternative fuel.

Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

고품질의 아가콩 음료개발을 위해 반응표면분석법에 의한 추출조건을 최적화 하였다. 아가콩 의 최적 볶음조건은 250 ℃, 30분으로 설정하였다. 추출시간, 추출온도에 따른 아가콩의 품질지표인 pH, 색도 및 이소플라본 함량은 1% 이내에서의 유의적인 영향을 미치는 것으로 나타났다. 추출온도와 추출시간이 길어질수록 이소플라본 함량은 높은 것으로 나타났다. 반응표면분석의 이소플라본 추출 공정 최적화 결과 추출온도는 99.5 ℃, 추출시간은 1.7 h으로 나타났으며, 이 조건에서 이소플라본의 최적 수 율은 10.63 μg/mL로 예측되었다.

Forged part made of Cold heading quality wire materials are used for automotive brake systems. The cost reduction of forged products is a major issue because of the strict shape change. A series of studies were conducted to minimize the cost of EPB spindle process among brake parts. In order to reduce the material cost, heat treatment-abbreviated material was applied and the formability on the processes was verified by the ductile fracture theory. In addition, the causes of shape fixation and die life degradation were analyzed using the numerical simulation. The process cost has been minimized by re-designing process, changing the product shape, and the die material.