Expanding the frontiers of additive manufacturing: Higher microstructure identification through probability modeling

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dc.contributor.authorMuhammad Shoaib
dc.contributor.authorMuhammad Idrees
dc.contributor.authorHakeem Ullah
dc.contributor.authorAasim Ullah Jan
dc.contributor.authorTouqeer Ahmad
dc.contributor.authorAli Akgül
dc.contributor.authorMagda Abd El-Rahman
dc.contributor.authorSeham M. Al-Mekhlafi
dc.date.accessioned2025-04-29T05:11:36Z
dc.date.available2025-04-29T05:11:36Z
dc.date.issued2025-06
dc.departmentFakülteler, Fen-Edebiyat Fakültesi, Matematik Bölümü
dc.description.abstractProbabilistic 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.
dc.identifier.citationShoaib, 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.
dc.identifier.doi10.1016/j.rineng.2025.104707
dc.identifier.issn2590-1230
dc.identifier.scopus2-s2.0-105002492736
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1016/j.rineng.2025.104707
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8629
dc.identifier.volume26
dc.indekslendigikaynakScopus
dc.institutionauthorAkgül, Ali
dc.institutionauthorid0000-0001-9832-1424
dc.publisherElsevier BV
dc.relation.ispartofResults in Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAdditive manufacturing
dc.subjectFinite element analysis
dc.subjectGaussian process regression
dc.subjectMicrostructure prediction
dc.subjectProbabilistic modeling
dc.subjectProcess optimization
dc.titleExpanding the frontiers of additive manufacturing: Higher microstructure identification through probability modeling
dc.typejournal-article
oaire.citation.volume26

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