With the recent advancement in the field of machine learning, health synthetic data has become a promising technique to address difficulties with time consumption when accessing and using electronic medical records for research and innovations. However, health synthetic data utility and governance have not been extensively studied. A scoping review was conducted to understand the status of evaluations and governance of health synthetic data following the PRISMA guidelines. The results showed that if synthetic health data are generated via proper methods, the risk of privacy leaks has been low and data quality is comparative to real data. However, the generation of health synthetic data has been generated on a case-by-case basis instead of being scaled up. Furthermore, regulations, ethics, and data sharing of health synthetic data have primarily been inexplicit, although common principles for sharing such data do exist.

Considering the great importance of injection moulding in plastic gear manufacturing, it is momentous to effectively control all the influential factors in the plastic injection moulding industry to improve the quality characteristics of the final gear part. As plastic materials exhibit extremely convoluted properties, the complexity of the injection moulding process makes it very challenging to attain the desired gear part properties. Since the intricate injection moulding process produces a wide range of parts with complex shapes within very narrow limits of tolerances, requires a great effort in order to keep the quality characteristics of moulded gears under control. In fact, the optimum properties of the plastic material cannot be achieved even with the most innovative part and mould design, and become meaningless without optimized processing parameters during the gear manufacturing. Therefore, the aim of this study is to propose the integration of Taguchi method/Grey relational analysis optimization approach in designing the gear part, setting up processing parameters, and selecting a suitable material for a helical gear via numerical simulation. The findings implied that an experimental design based on the integration of numerical simulation and Taguchi/Grey relational analysis is capable to enhance the multi-quality characteristic of the helical gear.

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The primary objective of this research was to experimentally investigate the robustness of a commercially available zirconium-based bulk metallic glass material (Zr-based BMG) for microinjection molding (μIM) tooling. The focused ion beam (FIB) direct milling process was utilized to fabricate microfeatures onto two BMG-based mold inserts. Uncoated and Ti-coated inserts were inspected through molding cycles utilizing SEM. Additionally, TPU molded samples were characterized to quantify the replication quality of the inserts through molding cycles. This is to understand the polymer melt effect of the tooling during molding conditions. The uncoated BMG insert was utilized for more than 1000 molding cycles regardless of the potential crystallization. No signs of any crack initiation were observed in any part of the BMG insert. Through molding process, the replication quality degraded due to the polymer adhesion to the microcavity base. In the case of the coated BMG insert, the coating could not withstand the high ejection force during demolding stage. The adhesion between the coating and the BMG surface was insufficient to survive molding conditions. This resulted in disintegrated coating that was bonded into molded samples.

A novel invention called Rheodrop technology is introduced for hot runner based injection molding. The technology allows control over melt rheology by applying desired shear rate values to the polymer melt during and/or in between injection molding cycles. The shear rate is applied by rotating the valve pin inside the hot drops and it is controlled by adjusting the rotational speed. The main goals are to optimize the process and to enhance the properties of molded parts. The focus on this study was incomplete filling defects which can be eliminated by the introduced technology. Numerical simulation and experimental analysis were performed to investigate the incomplete filling issue for hot runner systems. A four cavity hot runner mold was utilized in this research study and the processed material was Acrylonitrile Butadiene Styrene (ABS). Moldflow simulations was presented at three different temperature levels. The cavities were perfectly filled at the highest melt temperature level with incomplete filling resulting at the lower levels of melt temperature. Experimental results showed that implementing Rheodrop technology produces consistent ideal filling throughout the selected range of melt temperatures.