[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 …
Recent advances and applications of machine learning in experimental solid mechanics: A review
For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …
and understanding the mechanical properties of natural and novel artificial materials …
Review of transfer learning in modeling additive manufacturing processes
Modeling plays an important role in the additive manufacturing (AM) process and quality
control. In practice, however, only limited data are available for each product due to the …
control. In practice, however, only limited data are available for each product due to the …
[HTML][HTML] Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions
Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI)
has seen accelerated growth since the beginning of the 21st century. Successful AI …
has seen accelerated growth since the beginning of the 21st century. Successful AI …
Vision on metal additive manufacturing: Developments, challenges and future trends
Additive Manufacturing (AM) is one of the innovative technologies to fabricate components,
parts, assemblies or tools in various fields of application due to its main characteristics such …
parts, assemblies or tools in various fields of application due to its main characteristics such …
Error homogenization in physics-informed neural networks for modeling in manufacturing
Physics-informed neural networks (PINNs) have demonstrated effectiveness in solving
partial differential equations (PDEs) associated with manufacturing scenarios, due to their …
partial differential equations (PDEs) associated with manufacturing scenarios, due to their …
Enhanced framework for solving general energy equations based on metropolis-hasting Markov chain Monte Carlo
ZY Zhu, BH Gao, ZT Niu, YT Ren, MJ He… - International Journal of …, 2024 - Elsevier
Due to the widespread presence of heat and mass transfer phenomena in industrial
applications, numerous studies have been devoted to the accurate solution of energy …
applications, numerous studies have been devoted to the accurate solution of energy …
Thermal prediction of additive friction stir deposition through Bayesian learning-enabled explainable artificial intelligence
Additive friction stir deposition is an emerging solid-state metal additive manufacturing
technology that can conveniently and economically produce fully-dense, high-end …
technology that can conveniently and economically produce fully-dense, high-end …
[HTML][HTML] Physics-informed neural networks for data-free surrogate modelling and engineering optimization–an example from composite manufacturing
Engineering components require an optimization of design and manufacturing parameters
to achieve maximum performance–usually involving numerous physics-based simulations …
to achieve maximum performance–usually involving numerous physics-based simulations …
Experimental, computational, and machine learning methods for prediction of residual stresses in laser additive manufacturing: a critical review
In recent decades, laser additive manufacturing has seen rapid development and has been
applied to various fields, including the aerospace, automotive, and biomedical industries …
applied to various fields, including the aerospace, automotive, and biomedical industries …