Abstract
Advancing our understanding of the mechanisms of phase transformations is an efficient pathway to designing new steels with enhanced, tailor-made mechanical properties at minimal experimental cost. This dissertation investigates the bainitic transformation in steels. The mechanism of bainite formation is still not well understood, as there is no consensus on the role of carbon diffusion during the growth of bainite. Thus, it is difficult to understand and predict chemical and microstructural effects on the kinetics of bainite formation, such as the effect of prior austenite grain size and the presence of prior martensite. A better understanding of such effects may allow the design of new high performance bainitic steels with lean chemical composition and energy efficient heat treatments. In this dissertation, a new analytical model of the kinetics of bainite formation based on the displacive-diffusionless theory is proposed. This new model can reproduce and offer insights into the effect of prior austenite grain size and prior martensite on the kinetics of bainite formation. Following the understanding achieved with the model, the accelerating effect that martensite has on bainite formation kinetics is used to develop novel advanced high strength steels for the automotive industry that can be manufactured in existing continuous annealing lines thanks to fast bainite formation. Finally, to better understand the nucleation and growth of bainite, a real time observation of bainite formation using in situ transmission electron microscopy (TEM) is shown and discussed.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 3 Feb 2025 |
Print ISBNs | 978-94-6506-909-8 |
DOIs | |
Publication status | Published - 2025 |
Keywords
- bainite
- martensite
- phase transformation
- steel
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Dataset accompanying the dissertation "Bainite formation in the presence of martensite"
dos Santos Avila, D. (Creator), TU Delft - 4TU.ResearchData, 21 Oct 2024
DOI: 10.4121/6f40147a-d36f-4ef2-9b5e-950f35fba57a
Dataset/Software: Dataset