Doctoral theses of the School of Engineering are available in the open access repository maintained by Aalto, Aaltodoc.
Public defence in Mechanical Engineering, MSc Juan Rongfei
Linking experiments and crystal plasticity to reveal microstructure-governed, path-dependent hardening and fracture in metallic alloys. Public defence from the Aalto University School of Engineering, Department of Mechanical Engineering.
Title of the thesis: Microstructure based crystal plasticity modelling: from single phase to multiphase alloys
Thesis defender: Juan Rongfei
Opponent: Prof. Semih Perdahcioglu, University of Twente, Holland
Custos: Prof. Luc St-Pierre, Aalto University School of Engineering
Microstructure based crystal plasticity modelling: from single phase to multiphase alloys
Modern lightweight structures, such as cars and energy systems, rely on advanced metals that must be both strong and reliable under complex loading conditions. In real applications, materials rarely experience simple, one-directional deformation. Instead, they are subjected to changing load paths and stress states that strongly influence how they deform and ultimately fail. Accurately predicting this behaviour remains a major challenge in materials engineering.
This doctoral thesis investigates how the internal microstructure of metals governs their macroscopic mechanical behaviour and fracture. The study focuses on two technologically important materials: the aluminium alloy AA5754, widely used in automotive body structures, and quenching and partitioning (Q&P) 1000 steel, a new generation high-strength steel designed for improved crash performance.
The research combines advanced experimental characterization with microstructure-based crystal plasticity modelling. For AA5754, the work demonstrates how complex loading paths affect yielding and hardening, and shows that machine-learning-assisted calibration can significantly improve the accuracy and efficiency of material models. For Q&P1000 steel, the thesis reveals how fracture mechanisms depend on stress state and microstructural heterogeneity, identifying key damage initiation modes at the phase level.
The main result of the thesis is a robust modelling framework that links real microstructures to reliable predictions of deformation and damage. The research provides new insight into how microstructural features control mechanical response and failure, and offers practical tools for predicting material performance.
These results can be applied in the design and simulation of forming processes and structural components, helping engineers develop safer, lighter, and more efficient metallic structures.
Keywords: Crystal plasticity modelling, Representative volume element (RVE), Machine learning calibration, Fracture mechanisms
Thesis available for public display 7 days prior to the defence at .
Contact information: rongfei.juan@aalto.fi
Research group: Materials to Products, Department of Energy and Mechanical Engineering, Aalto University
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Doctoral theses of the School of Engineering
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