Machine Learning-Guided Optimization of Additively Manufactured TiB <sub>2</sub> -Modified Aluminum Alloys
nanoparticles promoted heterogeneous nucleation, resulting in ultrafine equiaxed grains. The strengthening mechanisms were attributed to grain boundary strengthening from the refined microstructure and dislocation strengthening arising from the high density of geometrically necessary dislocations (GNDs) generated by thermal mismatch under rapid solidification conditions, as well as dislocation bypassing of second-phase particles. This work demonstrates that ML-driven optimization can effectively navigate the high-dimensional parameter space of AM, offering a process-property modeling workflow for accelerating the design of high-performance, customized Al alloys.
Authors
- Chunnian He (ORCID: https://orcid.org/0000-0002-5768-6598)
- Xudong Rong (ORCID: https://orcid.org/0000-0002-7603-5547)
- Dongdong Zhao (ORCID: https://orcid.org/0000-0001-9268-1453)
- Xi Zhang (ORCID: https://orcid.org/0000-0002-4638-3997)
- Naiqin Zhao
- Xiaoxu Wang
Institutions
- Tianjin University (CN)
- National University of Singapore (SG)
- Composite Components (Czechia) (CZ)
- Division of Materials Science and Engineering (AU)
- Kunming Institute of Precious Metals (CN)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2026-04-25
- DOI
- https://doi.org/10.1021/acsami.6c02578
- Primary Topic
- Additive Manufacturing Materials and Processes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Tianjin City
- National Key Research and Development Program of China