Effect of microstructural heterogeneity on fatigue strength predicted by reinforcement machine learning

M Awd, S Münstermann… - Fatigue & Fracture of …, 2022 - Wiley Online Library
The posterior statistical distributions of fatigue strength are determined using Bayesian
inferential statistics and the Metropolis Monte Carlo method. This study explores how …

Towards deterministic computation of internal stresses in additively manufactured materials under fatigue loading: Part I

M Awd, MF Labanie, K Moehring, A Fatemi, F Walther - Materials, 2020 - mdpi.com
The ongoing studies of the influence of internal defects on fatigue strength of additively
manufactured metals adopted an internal crack or notch-like model at which the threshold …

[图书][B] Machine learning algorithm for fatigue fields in additive manufacturing

MMM Awd - 2023 - Springer
This dissertation is the product of my work as a head of the Workgroup Modeling and
Simulation at the Chair of Materials Test Engineering (WPT) of TU Dortmund University. At …

Numerical Investigation of the Influence of Fatigue Testing Frequency on the Fracture and Crack Propagation Rate of Additive-Manufactured AlSi10Mg and Ti-6Al-4V …

M Awd, F Walther - Solids, 2022 - mdpi.com
Advances in machine systems and scanning technologies have increased the use of
selective laser melted materials in industrial applications, resulting in almost full-density …

Quantum Mechanical-Based Fracture Behavior of L-PBF/SLM Ti-6Al-4V in the Very High Cycle Fatigue Regime

M Awd, L Saeed, F Walther - Materials …, 2023 - asmedigitalcollection.asme.org
This work predicts bulk elastic properties and solves the wave function probabilistically
using the density functional theory and by fixing outcomes with instrumented indentation …