Breaking the strength–ductility trade-off through integrated computational and data-driven design of hierarchical gradient metals
HIGMAM develops computational and experimental methods for designing stronger, tougher metallic materials made by additive manufacturing.
The project focuses on hierarchical gradient microstructures: internal material structures whose grains, phases, substructures, and defects vary deliberately across space.
The goal is direct material design:
process → microstructure → properties
HIGMAM combines experimental research, computational mechanics, and data-assisted analysis to study metallic materials across different length scales.
The work brings together:
- advanced material characterization,
- mechanical testing,
- finite-element modelling,
- micromechanics and crystal plasticity,
- damage and fracture analysis,
- machine-learning-assisted interpretation and design.
The public repositories in this organization provide selected datasets, scripts, and computational workflows that support open and reproducible research.
Publications related to HIGMAM project are listed below.
| Authors | Year | Title | Journal | Status |
|---|---|---|---|---|
| Yuan et al. | 2026 | A multi-task and multiscale spatial-temporal-coupled machine learning framework for high-throughput and high-fidelity crystal plasticity modeling | International Journal of Plasticity | In preparation |
| Nguyen et al. | 2026 | Strain gradient crystal plasticity modeling of indentation size effect to Hall–Petch strengthening for stainless steel AISI 439 | Acta Materialia | In preparation |
| Liu et al. | 2026 | Hierarchical and periodic gradient steels by additive manufacturing | Journal to be added | Submitted |
| Yang et al. | 2026 | Towards sustainable hydrogen infrastructures: Additive manufacturing of metallic materials for enhanced resistance to hydrogen embrittlement | Materials Today Sustainability, 34, 101339 | Published |
| Li et al. | 2026 | An evolving non-associated Hill–Barlat yield criterion for anisotropic hardening with closed-form analytical calibration and guaranteed convexity | International Journal of Plasticity, 204, 104762 | Published |
additive manufacturing · LPBF · gradient microstructure · crystal plasticity · micromechanics · damage · fracture · ICME · machine learning
Principal investigator: Prof. Junhe Lian
Email: junhe.lian@ibf.rwth-aachen.de
Members:
-
Binh Xuan Nguyen — Main GitHub maintainer
Email: binh.nguyen@ibf.rwth-aachen.de -
Yanglang Yuan — Main GitHub maintainer
Email: yanglang.yuan@ibf.rwth-aachen.de
