Additive manufacturing features rapid production of complicated shapes and has been
widely employed in biomedical, aeronautical and aerospace applications. However, additive …

The development of machine learning (ML) provides a promising solution to guarantee the
structural integrity of critical components during service period. However, considering the …

Additive manufacturing (AM) has attracted many attentions because of its design freedom
and rapid manufacturing; however, it is still limited in actual application due to the existing …

Fatigue life assessment of metal additive manufacturing (AM) products has remained
challenging due to the uncertainty of as–built defects, heterogeneity of the microstructure …

Additive manufacturing (AM) has emerged as a very promising technology for producing
complex metallic components with enhanced design flexibility. However, the mechanical …

Accurate fatigue assessment of material plagued by defects is of utmost importance to
guarantee safety and service continuity in engineering components. This study shows how …

Fish-scale-like melt pool structures and internal defects are characteristic features in
additively manufactured (AM) metals. These play a critical role in the damage and fracture …

Due to the comprehensive influencing factors, accurate fatigue life prediction of materials is
still a challenging task. In the present study, a novel deep learning approach named Multi …

Substantial advances in fatigue estimation of defective materials can be attained through the
employment of a Physics-Informed Neural Network (PINN). The fundamental strength of …

Microstructural inhomogeneity in additively manufactured (AM) components leads to
uncertainty in their fatigue performance. While purely data-driven methods can only provide …