Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives
Today's manufacturing processes are pushed to their limits to generate products with ever-
increasing quality at low costs. A prominent hurdle on this path arises from the multiscale …
increasing quality at low costs. A prominent hurdle on this path arises from the multiscale …
Modeling of hierarchical solidification microstructures in metal additive manufacturing: Challenges and opportunities
Metal-based additive manufacturing (AM) processes often produce parts with improved
properties compared to conventional manufacturing and metal working routes. However …
properties compared to conventional manufacturing and metal working routes. However …
Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability …
T Yasuda, S Ookawara, S Yoshikawa… - Chemical Engineering …, 2023 - Elsevier
This study proposes a material discovery framework for porous materials to identify design
variable recipes and the corresponding material structures that can be utilized to improve …
variable recipes and the corresponding material structures that can be utilized to improve …
Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys
We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML)
framework leveraging Gaussian processes (GP) to fuse atomistic computational model …
framework leveraging Gaussian processes (GP) to fuse atomistic computational model …
Deep learning-based discriminative refocusing of scanning electron microscopy images for materials science
Scanning electron microscopy (SEM) has contributed significantly to the development of
microstructural characteristics analysis in modern-day materials science. Although it is …
microstructural characteristics analysis in modern-day materials science. Although it is …
Improved irradiation resistance of accident-tolerant high-strength FeCrAl alloys with heterogeneous structures
Post–neutron irradiation examination is performed on advanced accident-tolerant fuel (ATF)
cladding iron-chromium-aluminum (FeCrAl) alloys with∼ 10–13at.% Cr,∼ 10–12 at.% Al,∼ …
cladding iron-chromium-aluminum (FeCrAl) alloys with∼ 10–13at.% Cr,∼ 10–12 at.% Al,∼ …
[HTML][HTML] Characterization of porous membranes using artificial neural networks
Y Zhao, P Altschuh, J Santoki, L Griem, G Tosato… - Acta Materialia, 2023 - Elsevier
Porous membranes have been utilized intensively in a wide range of fields due to their
special characteristics and a rigorous characterization of their microstructures is crucial for …
special characteristics and a rigorous characterization of their microstructures is crucial for …
Creep anisotropy modeling and uncertainty quantification of an additively manufactured Ni-based superalloy
The advantages offered by additive manufacturing over traditional processes has driven a
great deal of industrial and academic interest in recent years. However, the process is …
great deal of industrial and academic interest in recent years. However, the process is …
Solving stochastic inverse problems for property–structure linkages using data-consistent inversion and machine learning
Determining process–structure–property linkages is one of the key objectives in material
science, and uncertainty quantification plays a critical role in understanding both process …
science, and uncertainty quantification plays a critical role in understanding both process …
[HTML][HTML] Neural integration for constitutive equations using small data
Data-driven models based on deep learning algorithms intend to overcome the limitations of
traditional constitutive modelling by directly learning from data. However, the need for …
traditional constitutive modelling by directly learning from data. However, the need for …