Automatic and Rapid Measurement in Artificial Intelligence-Aided Microstructure Analysis: A Deep Learning Approach Applied to AlSi9 Alloys
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Aluminum silicon alloy, Deep learning, Semantic segmentation, CNN, Microstructure analysis
Kaynak
Integrating Materials and Manufacturing Innovation
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
14
Sayı
4
