A5055869768- Microstructure and Mechanical Properties of Steels
- Hydrogen embrittlement and corrosion behaviors in metals
- Metal Alloys Wear and Properties
- Metallurgy and Material Forming
- Magnetic Properties and Applications
- High Temperature Alloys and Creep
- Laser Material Processing Techniques
- Welding Techniques and Residual Stresses
- Fatigue and fracture mechanics
- Microstructure and mechanical properties
- Corrosion Behavior and Inhibition
- Non-Destructive Testing Techniques
- High-Temperature Coating Behaviors
- Advanced machining processes and optimization
- Fusion materials and technologies
- Machine Learning in Materials Science
- Aluminum Alloy Microstructure Properties
- Additive Manufacturing Materials and Processes
- Advanced Surface Polishing Techniques
- High Entropy Alloys Studies
- Metal and Thin Film Mechanics
- Aluminum Alloys Composites Properties
- Advanced Materials Characterization Techniques
- Laser-induced spectroscopy and plasma
- Advanced materials and composites
Northeastern University
2017-2025
Universidad del Noreste
2019-2023
Tsinghua University
2014-2017
High-Co–Ni secondary hardening steels are valuable in the field of aviation materials. With increasing demand lightweight, further enhancing its strength has received widespread attention. In this study, instead using only M2C carbides as reinforcements traditional high Co–Ni steels, nano-size NiAl phases were introduced to provide additional strength. To obtain collaborative precipitation these two reinforcements, systematically thermodynamic calculation, including driving force, coarsening...
Creep-oriented alloy design is a long-standing interesting topic in the field of metal structural materials. However, high cost for creep testing limits development efficiency new alloys using traditional trial-and-error methods. Additionally, complex mechanism and influencing factors significantly increase difficulty physical modeling simulation-guided design. In this study, an framework life improvement established, including two modules: prediction high-throughput For first module, based...
Abstract In this work, a hybrid modeling approach, combining machine learning (ML) and computational thermodynamics, has been applied to predict deformation-induced martensitic transformation (DIMT) explore the generic alloy-specific parameters governing DIMT in austenitic steels. The model was established based on ensemble ML algorithms comprehensive set of physical variables. developed is highly generalizable as validated unseen alloys. are good agreement with previous studies literature....
Stacking fault energy (SFE) significantly influences plastic deformation, strength, and processing performance, making accurate assessment prediction of SFE essential for material design optimization. Traditional calculations mainly rely on experimental measurements thermodynamic theories, with the former usually being time-consuming latter limited in applicability at different compositions. To overcome these limitations, this study proposes a machine learning (ML) strategy introducing...
Abstract: Traditional creep life prediction methods are generally difficult for researchers to fully consider the key factors affecting performance, which limits their application in research and development of new alloys. The artificial intelligence method can skip complex mechanism directly establish mathematical correlation between composition/process target performance. accuracy, universality, efficiency model better than traditional material strategy. In this study, we collected 216...