Automatic and Rapid Measurement in Artificial Intelligence-Aided Microstructure Analysis: A Deep Learning Approach Applied to AlSi9 Alloys

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dc.authorid0000-0002-7216-0586
dc.authorid0000-0002-1903-5583
dc.contributor.authorKalkan, Mahmut Furkan
dc.contributor.authorKalkan, Ibrahim Halil
dc.contributor.authorYilmaz, Necip Fazil
dc.contributor.authorDispinar, Derya
dc.contributor.authorKahruman, Cem
dc.contributor.authorYavuz, Abdulcabbar
dc.date.accessioned2026-02-08T15:15:06Z
dc.date.available2026-02-08T15:15:06Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThis 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.
dc.description.sponsorshipThe authors thank the Scientific Research Project Unit at Gaziantep University (MF.DT.24.1).
dc.identifier.doi10.1007/s40192-025-00422-5
dc.identifier.endpage642
dc.identifier.issn2193-9764
dc.identifier.issn2193-9772
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105016741785
dc.identifier.scopusqualityQ2
dc.identifier.startpage631
dc.identifier.urihttps://doi.org/10.1007/s40192-025-00422-5
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5590
dc.identifier.volume14
dc.identifier.wosWOS:001573797000001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofIntegrating Materials and Manufacturing Innovation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectAluminum silicon alloy
dc.subjectDeep learning
dc.subjectSemantic segmentation
dc.subjectCNN
dc.subjectMicrostructure analysis
dc.titleAutomatic and Rapid Measurement in Artificial Intelligence-Aided Microstructure Analysis: A Deep Learning Approach Applied to AlSi9 Alloys
dc.typeArticle

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