Toward a Digital Twin: Micro-CT-Resolved Fatigue-Life Prediction for SLM AlSi10Mg


Bal H., Butt M. M., Tanabi H., Kızıl H., SALAMCİ E.

Fatigue and Fracture of Engineering Materials and Structures, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1111/ffe.70438
  • Dergi Adı: Fatigue and Fracture of Engineering Materials and Structures
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
  • Anahtar Kelimeler: additive manufacturing, AlSi10Mg, digital twin, fatigue-life prediction, finite element analysis (FEA), laser powder bed fusion (LPBF), microcomputed tomography (micro-CT), porosity, selective laser melting (SLM)
  • Gazi Üniversitesi Adresli: Evet

Özet

A microcomputed tomography (micro-CT)-based framework was developed to predict the fatigue life of selective laser melting (SLM) AlSi10Mg specimens with different porosity levels and defect morphologies. Because full-specimen pore-resolved finite element analysis (FEA) is computationally impractical, specimen-specific regions of interest (ROIs) were extracted from experimentally identified fracture-critical locations for fatigue simulations. Predicted fatigue lives were consistently conservative, with an average log-scale deviation of 13.64%, while preserving the experimental fatigue-life trend. Additional simulations between 140 and 9 MPa enabled the extraction of ROI-specific FEA-derived fatigue strength. Void volume fraction (VVF) exhibited inverse relationships with both predicted fatigue life and fatigue strength. Comparisons of ROIs with similar VVF showed that volume-weighted average sphericity also affected fatigue performance, indicating that defect morphology contributes beyond porosity alone. The proposed framework provides a defect-resolved and morphology-sensitive basis for fatigue assessment and supports future digital-twin–oriented prediction of additively manufactured AlSi10Mg components.