The overarching project: 'Data Enhanced Physical models to reduce MATerials use' (DEPMAT)
Society calls for an increased recycling of material in steel production processes to reduce the huge CO2 footprint. Current physics-based material models relating composition and thermo-mechanical history are too simple or too computationally expensive for use in industry. DEPMAT aims to develop physics-informed, data-based (machine learning and artificial intelligence) methods for superior accuracy and speed, to enable predictive modelling in industry and to increase recyclability in steel making. We are building a team of talented, enthusiastic researchers to achieve this exciting goal.PhD vacancy with a focus on in-situ micro-mechanical testing and micromechanical modeling
The goal of this experimental-numerical PhD project is to set up guidelines for providing optimal experimental input for constructing the microstructural part of a physics-informed machine learning approach, with the aim to maximize mechanical accuracy and prediction robustness in light of recycling-induced compositional variations. How to build microstructural models with a minimal level of microstructural detail that finds the optimum between accuracy and computational cost? How to validate accuracy and associated statistical spread of such models, e.g., as function of recycling-induced compositional variations? To this end, you will carry out in-depth microstructural characterization and state-of-the-art in-situ
SEM micro-mechanical tests on a few, carefully selected steel grades, to deeply understand their micromechanical behavior. Then you will analyze the mechanical predictions of the physics-informed machine learning approach when trained on different parts of the experimental data, to shed light on the questions above.Section Mechanics of Materials and the Multiscale Mechanics Laboratory
You will work in the Section of Mechanics of Materials (www.tue.nl/mechmat
), Department of Mechanical Engineering, which is globally recognized for its research on experimental analysis, theoretical understanding and predictive modelling of complex mechanical behavior in engineering materials at different length scales (e.g, plasticity, damage, fracture,…), which emerges from the physics and mechanics of the underlying multi-phase microstructure. An integrated numerical-experimental approach is generally adopted for this goal.
You will carry out the state-of-the-art high-resolution in-situ SEM micro-mechanical experiments at the Multiscale Mechanics Laboratory (www.tue.nl/multiscale-lab
), led by dr. Johan Hoefnagels (www.tue.nl/hoefnagels-group
), which bridges the gap between traditional materials science and mechanical characterization labs, by integrating micro-mechanical testing with real-time and in-situ microscopic observation.
You will closely interact with a numerical PhD student, who aims to establish a multiscale data-driven solution procedure exploiting constitutive equations. Part of your work is a statistical confrontation of simulation results against experimental data.