Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
H Wang, B Li, J Gong, FZ Xuan - Engineering Fracture Mechanics, 2023 - Elsevier
Fatigue life prediction is critical for ensuring the safe service and the structural integrity of
mechanical structures. Although data-driven approaches have been proven effective in …
mechanical structures. Although data-driven approaches have been proven effective in …
[HTML][HTML] Holistic computational design within additive manufacturing through topology optimization combined with multiphysics multi-scale materials and process …
Additive manufacturing (AM) processes have proven to be a perfect match for topology
optimization (TO), as they are able to realize sophisticated geometries in a unique layer-by …
optimization (TO), as they are able to realize sophisticated geometries in a unique layer-by …
A deep learning approach for inverse design of gradient mechanical metamaterials
Mechanical metamaterials with unique micro-architectures possess excellent physical
properties in terms of stiffness, toughness, vibration isolation, and thermal expansion …
properties in terms of stiffness, toughness, vibration isolation, and thermal expansion …
[HTML][HTML] Machine learning aided nanoindentation: A review of the current state and future perspectives
The solution of instrumented indentation inverse problems by physically-based models still
represents a complex challenge yet to be solved in metallurgy and materials science. In …
represents a complex challenge yet to be solved in metallurgy and materials science. In …
Characterisation of in-situ alloyed titanium-tantalum lattice structures by laser powder bed fusion using finite element analysis
Lattice structures are widely used in the industry for aerospace, automotive and biomedical
applications as they are strong yet lightweight. Due to the complex geometry, lattice …
applications as they are strong yet lightweight. Due to the complex geometry, lattice …
[HTML][HTML] Inverse design of 3D cellular materials with physics-guided machine learning
M Abu-Mualla, J Huang - Materials & Design, 2023 - Elsevier
This paper investigates the feasibility of data-driven methods in automating the engineering
design process, specifically studying inverse design of cellular mechanical metamaterials …
design process, specifically studying inverse design of cellular mechanical metamaterials …
Transfer learning-based crashworthiness prediction for the composite structure of a subway vehicle
Due to the lack of load/displacement sensors in a complex and uncertain crash test/accident
of rail vehicles (eg, vehicle-to-vehicle or train-to-train collision), only structural deformation …
of rail vehicles (eg, vehicle-to-vehicle or train-to-train collision), only structural deformation …
Tunable mechanical performance of additively manufactured plate lattice metamaterials with half-open-cell topology
Plate lattices are an emerging class of lightweight mechanical metamaterials that exhibit
superior mechanical properties. The unique architectures of plate lattice metamaterials are …
superior mechanical properties. The unique architectures of plate lattice metamaterials are …
Progress and opportunities for machine learning in materials and processes of additive manufacturing
In recent years, there has been widespread adoption of machine learning (ML) technologies
to unravel intricate relationships among diverse parameters in various additive …
to unravel intricate relationships among diverse parameters in various additive …
Structure genome based machine learning method for woven lattice structures
C Zhang, B Wang, H Zhu, H Fan - International Journal of Mechanical …, 2023 - Elsevier
As a type of lightweight composite material, three-dimensional (3D) woven lattice structure
(WLS) has been extensively applied in various fields. It is extremely significant to investigate …
(WLS) has been extensively applied in various fields. It is extremely significant to investigate …