In laser powder bed fusion (L-PBF), a metal powder–based additive manufacturing process, pure titanium powders rely on expensive gas-atomized spherical powders, which poses a significant limitation of material cost. In contrast, non-spherical titanium powders are more cost-effective but their application in L-PBF is restricted their use due to poor flow property and high oxygen content. In this study, a powder mixing strategy with spherical titanium and hydrophobic SiO2 nanoparticle is proposed to improve the flowability and process stability of non-spherical Ti powders. After evaluating flow properties at various mixing ratios, a spherical-to-non-spherical Ti ratio of 4:6 was selected, with SiO2 nanoparticles added during mixing. The uniform distribution of oxide nanoparticles on the powder surfaces was confirmed by SEM and EDS. A maximum relative density of 99.7% was shown by specimens made with L-PBF under various processing parameters. The specimens obtained a tensile strength of 762.6 ± 3.8 MPa and an elongation of 22.1 ± 0.7% at a volumetric energy density of 71.4 J/mm³. This study demonstrates the application of low-cost non-spherical Ti powders in L-PBF is feasible and presents an effective way to simultaneously increase process stability and economic efficiency in titanium additive manufacturing.

The loss of soil available nutrients may affect soil quality and crop growth. Biochar can form a multi-level fixed network because of its rich pore structure and surface functional groups, which can effectively fix available nutrients in soil and maintain nutrient utilization rate. Because it is difficult to directly prepare biochar materials with good adsorption characteristics through experimental results. This study employed an XGBoost machine learning prediction model to determine the optimal nutrient-rich biochar preparation conditions. The R2 value ranged from 0.97 to 0.99. The results indicated that specific surface area was the primary factor influencing ammonium nitrogen adsorption, with a feature importance of 56.13%. Production conditions (hydrothermal temperature and time) significantly affected the adsorption of nitrate nitrogen and available phosphorus, with feature importances of 75.91% and 81.54%, respectively. Mean pore diameter was negatively correlated with potassium ion adsorption characteristics. Biochar prepared under hydrothermal conditions at 202.50–251.25 °C for 3 h exhibited favorable adsorption characteristics for multiple soil available nutrients. This study provides new insights into biochar’s application in the field of soil nutrient adsorption through data analysis. It is helpful to avoid the waste in the process of energy utilization from biomass to biochar.

In order to confirm the optimal conditions for the LED(Light Emitting Diode) wire bonding process, the lead bump ball process optimization was analyzed. In the wire bonding process, it is an important process in which electrical characteristics operate by connecting the Au wire to the LED chip and lead frame. In the wire bonding method, various wire bonding processes, including thermocompression and ultrasonic bonding, were dealt with, and various variables affecting the lead bump ball process of wire bonding were analyzed through process variable analysis. Key variables for wire bonding working conditions were identified and optimized using the Response Surface Method(RSM) of Design of Experiments(DOE), the interaction between variables was confirmed through factor setting experiments, and the process was optimized using the RSM. This paper aims to improve the performance of the lead bump ball process by designing experiments with 5 factors at 3 levels and analyzing 4 response variables to find optimal conditions. It was confirmed that the performance of the lead bump ball process improved under optimized conditions, and as a result, optimal conditions that satisfied the targets for most reaction values, with the exception of ball diameter (BD), were secured.

This study presents a cost-effective approach to fabricating near β-Ti alloys via in-situ alloying during laser powder bed fusion (L-PBF). A blend of non-spherical pure Ti, 3 wt.% Fe, and 0.1 wt.% SiO2 nanoparticles was used to induce β-phase stabilization and improve flowability. Twenty-five process conditions were evaluated across a volumetric energy density range of 31.75-214.30 J/mm3, achieving a maximum relative density of 99.21% at 89.29 J/mm3. X-ray diffraction analysis revealed that the β-Ti phase was partially retained at room temperature, accompanied by lattice contraction in the α’-Ti structure, indicating successful Fe incorporation. Elemental mapping confirmed that the Fe distribution was homogeneous, without significant segregation. Compared to pure Ti, the Ti-3Fe sample exhibited a 49.2% increase in Vickers hardness and notable improvements in yield and ultimate tensile strengths. These results demonstrate the feasibility of in-situ alloying with low-cost elemental powders to produce high-performance near β-Ti alloys using L-PBF.

The number of significant issues on many welding processes are often connected to high productivity and manufacturability at low costs. The research on welding processes in the literature has reported several research activities, but there is still scope for improvement in most industrial settings. The primary goal of this research is to determine the best super-TIG welding settings to use for groove welding. First, in order to determine the quality characteristics and risks associated with them, concepts and frameworks of quality by design (QbD) which is a new standard in pharmaceutical area in order to improve drug qualities were integrated into this process optimization. Second, stepwise experimental design approaches including a factorial design as well as a response surface methodology (RSM) were customized and performed for this specific automated super-TIG welding process. Third, based on experimental design results, the optimal operating conditions with both design space (i.e., acceptable range of operating conditions) and safe operating space (i.e., safe range of operating conditions) were obtained. Finally, a case study including QbD steps, stepwise experimental design approaches, design and operating spaces, the optimal factor settings, and their association validation results was conducted for verification purposes.

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.