Expanding the frontiers of additive manufacturing: Higher microstructure identification through probability modeling
Yükleniyor...
Dosyalar
Tarih
2025-06
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier BV
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Probabilistic models and machine learning methods create a step forward in making predictions for additive manufacturing (AM) microstructure. In this probabilistic framework, it became possible to express modifications in the properties of metal, polymer, ceramic, and composite microstructures. Process parameters and material consistency reached maximum levels through the use of statistical modeling along with finite element analysis (FEA) and Gaussian process regression (GPR). Experimental validation through AM process parameters, microstructural values, and material characteristics led to 40 % fewer metal and polymer microstructure variations with simultaneous strength increases. The computational system demonstrated its resistance to process modifications through a validated sensitivity analysis. Additionally covered were scalability issues, computing needs, and possible real-time adaption. These results help AM approaches in aerospace and biomedical engineering to be scalable and performable.
Açıklama
Anahtar Kelimeler
Additive manufacturing, Finite element analysis, Gaussian process regression, Microstructure prediction, Probabilistic modeling, Process optimization
Kaynak
Results in Engineering
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
26
Sayı
Künye
Shoaib, M., Idrees, M., Ullah, H., Jan, A. U., Ahmad, T., Akgül, A., ... & Al-Mekhlafi, S. M. (2025). Expanding the Frontiers of Additive Manufacturing: Higher Microstructure Identification through Probability Modeling. Results in Engineering, 104707.
