Alloy design for laser powder bed fusion additive manufacturing: a critical review
Metal additive manufacturing (AM) has been extensively studied in recent decades. Despite
the significant progress achieved in manufacturing complex shapes and structures …
the significant progress achieved in manufacturing complex shapes and structures …
Materials informatics for mechanical deformation: A review of applications and challenges
In the design and development of novel materials that have excellent mechanical properties,
classification and regression methods have been diversely used across mechanical …
classification and regression methods have been diversely used across mechanical …
Benchmarking AutoML for regression tasks on small tabular data in materials design
F Conrad, M Mälzer, M Schwarzenberger, H Wiemer… - Scientific Reports, 2022 - nature.com
Abstract Machine Learning has become more important for materials engineering in the last
decade. Globally, automated machine learning (AutoML) is growing in popularity with the …
decade. Globally, automated machine learning (AutoML) is growing in popularity with the …
[HTML][HTML] Machine learning-based forward and inverse designs for prediction and optimization of fracture toughness of aluminum alloy
Utilization of machine learning framework to design aluminum alloy with high fracture
toughness is increasing. Nonetheless, before such model can be applied, the …
toughness is increasing. Nonetheless, before such model can be applied, the …
Predicting the hardness of high-entropy alloys based on compositions
Q Guo, Y Pan, H Hou, Y Zhao - … Journal of Refractory Metals and Hard …, 2023 - Elsevier
Features calculation and combinatorial screening are necessary and tedious in predicting
the hardness of high-entropy alloys by empirical parameters. To simplify the prediction …
the hardness of high-entropy alloys by empirical parameters. To simplify the prediction …
Investigation of the effect of ECAP parameters on hardness, tensile properties, impact toughness, and electrical conductivity of pure Cu through machine learning …
Copper and its related alloys are frequently adopted in contemporary industry due to their
outstanding properties, which include mechanical, electrical, and electronic applications …
outstanding properties, which include mechanical, electrical, and electronic applications …
[HTML][HTML] Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning
Data-driven modelling, such as machine learning (ML), has great potential to streamline the
complexity involved in designing new alloys. However, such powerful predictive models …
complexity involved in designing new alloys. However, such powerful predictive models …
Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy
The majority of computational catalyst design focuses on the screening of material
components and alloy composition to optimize selectivity and activity for a given reaction …
components and alloy composition to optimize selectivity and activity for a given reaction …
Inverse design of aluminium alloys using genetic algorithm: a class-based workflow
The design of aluminium alloys often encounters a trade-off between strength and ductility,
making it challenging to achieve desired properties. Adding to this challenge is the broad …
making it challenging to achieve desired properties. Adding to this challenge is the broad …
A feasibility study of machine learning-assisted alloy design using wrought aluminum alloys as an example
Abstract Machine learning (ML) often requires large datasets for reliable predictions, which
may not be feasible for most commercial alloy systems. Also, the alloy development requires …
may not be feasible for most commercial alloy systems. Also, the alloy development requires …