Machine learning for high-entropy alloys: Progress, challenges and opportunities
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …
mechanical properties and the vast compositional space for new HEAs. However …
Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions
In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness
Trapped by time-consuming traditional trial-and-error methods and vast untapped
composition space, efficiently discovering novel high entropy alloys (HEAs) with exceptional …
composition space, efficiently discovering novel high entropy alloys (HEAs) with exceptional …
The potency of defects on fatigue of additively manufactured metals
Given their preponderance and propensity to initiate fatigue cracks, understanding the effect
of processing defects on fatigue life is a significant step towards the wider application of …
of processing defects on fatigue life is a significant step towards the wider application of …
[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 …
Physics-guided machine learning frameworks for fatigue life prediction of AM materials
Introducing random defects is a type of the dominant causes of fatigue scatter of additive
manufacturing (AM) materials. The fracture mechanics-based models oversimplify the …
manufacturing (AM) materials. The fracture mechanics-based models oversimplify the …
Artificial intelligence and advanced materials
C López - Advanced Materials, 2023 - Wiley Online Library
Artificial intelligence (AI) is gaining strength, and materials science can both contribute to
and profit from it. In a simultaneous progress race, new materials, systems, and processes …
and profit from it. In a simultaneous progress race, new materials, systems, and processes …
[HTML][HTML] Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems
Reinforced composite is a preferred choice of material for the design of industrial lightweight
structures. As of late, composite materials analysis and development utilizing machine …
structures. As of late, composite materials analysis and development utilizing machine …
Intelligent prediction model of mechanical properties of ultrathin niobium strips based on XGBoost ensemble learning algorithm
ZH Wang, YF Liu, T Wang, JG Wang, YM Liu… - Computational Materials …, 2024 - Elsevier
Ultrathin niobium strips with different thicknesses are prepared by an accumulative rolling
process. The tensile test of the ultrathin niobium strips is carried out, and the microstructure …
process. The tensile test of the ultrathin niobium strips is carried out, and the microstructure …
Estimation of tool–chip contact length using optimized machine learning in orthogonal cutting
Tool–chip contact length has a significant effect on the various characteristics of metal
cutting, including cutting pressures, chip formation, tool wear, tool life, and cutting …
cutting, including cutting pressures, chip formation, tool wear, tool life, and cutting …