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    Automatic and Rapid Measurement in Artificial Intelligence-Aided Microstructure Analysis: A Deep Learning Approach Applied to AlSi9 Alloys
    (Springer Heidelberg, 2025) Kalkan, Mahmut Furkan; Kalkan, Ibrahim Halil; Yilmaz, Necip Fazil; Dispinar, Derya; Kahruman, Cem; Yavuz, Abdulcabbar
    This study showcases a proof-of-concept of an artificial intelligence-driven analytical technique that facilitates the automated extraction of significant quantitative data from microstructural images. Semantic segmentation and classification were conducted on eutectic Si particles and dendritic architectures utilizing microscopic images of AlSi9 alloys with varying Sr ratios. Following segmentation, characteristics including area, aspect ratio, maximum Feret diameter, circularity and SDAS were assessed automatically, and the resulting values were compared with both literature and manual measurements. The samples were effectively categorized based on their alteration levels using a CNN-based classification algorithm. This technology provides significant temporal and financial benefits for microstructural investigation by executing the entire procedure autonomously and expeditiously. The minimal error rates and elevated accuracy findings demonstrate the usefulness and dependability of the devised method for automated microstructural analysis. This paper exemplifies the application of artificial intelligence-driven microstructural analysis techniques in materials science, addressing a significant gap in the literature.
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    Effects of junction angle and gas pressure on polymer nanosphere preparation from microbubbles bursted in a combined microfluidic device with thin capillaries
    (Elsevier Science Bv, 2018) Küçük, İsrafil; Yilmaz, Necip Fazil; Sinan, Aussama
    This study describes polymethylsilsesquioxane (PMSQ) nanospheres preparation from microbubble bursting in a 100 mu m capillaries embedded combined microfluidic device based on effects of junction angle and flow rate of liquid solution. The effects of the junction angle (phi = 0 degrees-60 degrees) between the liquid and gas channels and the gas pressure ratios (50-400 kPa) are considered. The digital microscope results indicate that the microbubble size during the bubble generation process generally decreases with the increase of junction angle at the same flow rate and gas pressure. In addition, the nanosphere size in the combined microfluidic junction device with 100 gm capillaries decreases as junction angle increases with the same flow and gas pressure conditions. When junction angle is about 60 degrees, there always exists the smallest nanosphere formation in the device with thin capillaries used. The microbubble formation in the device used in this work depends significantly on the gas pressure, and the combined microfluidic junction device with thin capillaries becomes a microbubble generation when N-2 gas pressure is greater than 50 kPa at the same junction angle and liquid flow rate. Furthermore, the resulting microbubble and polymer nanosphere size in the device used decreases with an increase of N-2 gas pressure. To evaluate chemical structure of the polymers used before and after the microfluidic processing, PMSQ raw materials and the resultant Polymer nanospheres obtained were also characterised using an MR spectroscopy. The understanding of polymer nanosphere generation from microbubble bursting in the device with thin capillaries used could be very useful for many applications, such as cell transplantation in biomedical therapy, advanced therapeutic applications and food industry. (C) 2018 Elsevier B.V. All rights reserved.