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Cellular Automata for Modelling Microstructure Development during Metal Additive Manufacturing
Cellular Automata is an effective approach for prediction of the microstructure and texture evolution during additive manufacturing. The aim of this student project is to numerically and experimentally analyse the microstructure evolution of a superalloy during SLM process.
The advantages of metal additive manufacturing (MAM) for the fabrication of complex structures and geometries have been extolled by many researchers. However, the capability of MAM to design alloys' microstructure and generate materials with site-specific properties is less explored. By changing the material microstructure and properties with position one can produce a more efficient engineering structure.
Effective exploitation of the MAM capability for the manufacture of functionally graded microstructure requires a deep understanding of microstructure development during the manufacturing process. Employment of microstructure modelling approaches provides an insight into the complex process of microstructure formation during additive manufacturing and can determine the sensitivity of developed microstructure to the AM process parameters.
A number of microstructure modelling approaches have been employed for AM microstructure prediction (kinetic Monte Carlo, cellular automata, phase-field), each provides a different level of microstructure details and demands a different amount of computational power.
Among these, Cellular automata (CA) becomes a promising tool for SLM microstructure prediction since it can consider crystallography orientation evolution with less computational cost. The main concept of CA is simulating complex morphology evolution based on simple local/global deterministic/probabilistic rules. For CA microstructure modelling, the simulation domain is represented by a set of (uniform) cells. Cells can be in different states (molten, solidifying, solidified) and have data such as crystallographic orientation. Taking temperature evolution data from Finite element simulation or analytical models, the cell state can change in discrete time step based on probabilistic nucleation and deterministic grain growth model and then form resultant microstructure. A CA code for SLM microstructure prediction has been developed in our group. And a running PhD project focuses on this topic and therefore assistance will be available for the MSc student.
The aim of the proposed project is to further develop this CA code and combine it with experimental tools such as EBSD and SEM to study the relationship between SLM process parameters and formed microstructure.
This project is suitable for **ETH** MSc thesis and for students who are passionate about computational material science and have programming skills in C++/MATLAB.
The advantages of metal additive manufacturing (MAM) for the fabrication of complex structures and geometries have been extolled by many researchers. However, the capability of MAM to design alloys' microstructure and generate materials with site-specific properties is less explored. By changing the material microstructure and properties with position one can produce a more efficient engineering structure.
Effective exploitation of the MAM capability for the manufacture of functionally graded microstructure requires a deep understanding of microstructure development during the manufacturing process. Employment of microstructure modelling approaches provides an insight into the complex process of microstructure formation during additive manufacturing and can determine the sensitivity of developed microstructure to the AM process parameters.
A number of microstructure modelling approaches have been employed for AM microstructure prediction (kinetic Monte Carlo, cellular automata, phase-field), each provides a different level of microstructure details and demands a different amount of computational power. Among these, Cellular automata (CA) becomes a promising tool for SLM microstructure prediction since it can consider crystallography orientation evolution with less computational cost. The main concept of CA is simulating complex morphology evolution based on simple local/global deterministic/probabilistic rules. For CA microstructure modelling, the simulation domain is represented by a set of (uniform) cells. Cells can be in different states (molten, solidifying, solidified) and have data such as crystallographic orientation. Taking temperature evolution data from Finite element simulation or analytical models, the cell state can change in discrete time step based on probabilistic nucleation and deterministic grain growth model and then form resultant microstructure. A CA code for SLM microstructure prediction has been developed in our group. And a running PhD project focuses on this topic and therefore assistance will be available for the MSc student.
The aim of the proposed project is to further develop this CA code and combine it with experimental tools such as EBSD and SEM to study the relationship between SLM process parameters and formed microstructure.
This project is suitable for **ETH** MSc thesis and for students who are passionate about computational material science and have programming skills in C++/MATLAB.
The goal of the project is to study the effect of the SLM process parameters on the formed microstructure based on developed CA model and experiments. The candidate should have a background or interest in computational material science. S/he will be working in close contact with our PhD student who is working on the CA simulation for the SLM process.
The goal of the project is to study the effect of the SLM process parameters on the formed microstructure based on developed CA model and experiments. The candidate should have a background or interest in computational material science. S/he will be working in close contact with our PhD student who is working on the CA simulation for the SLM process.
Contact: Dr. Ehsan Hosseini ehsan.hosseini@mavt.ethz.ch Mr. Jian Tang
jian.tang@empa.ch
Contact: Dr. Ehsan Hosseini ehsan.hosseini@mavt.ethz.ch Mr. Jian Tang jian.tang@empa.ch