Microstructure dynamics in advanced steels studied with high-throughput X-ray diffraction and machine learning

Microstructure dynamics in advanced steels studied with high-throughput X-ray diffraction and machine learning

Designing new steels and their processing is a challenging task, which relies on a many-dimension design space, far too vast to be explored in totality. The steel industry is in urgent need of a methodology to determine more effectively how the microstructure depends on the chemical composition and on the processing parameters, to feed materials models, responding to societal needs and improving process control with an industry 4.0 approach.

The project shall develop such a methodology, thereby obtaining dynamic time-resolved microstructure maps in compositional space.
This goal will be achieved by fabricating compositionally graded steels with careful control of the solute spatial distribution, followed by time and space-resolved high energy X-ray diffraction during in-situ heat treatments. The sample will be continuously moved with respect to the X-ray beam, yielding for each ~1h experiment of the order of 100 phase transformation kinetics (100 data points each), thanks to the properties of the EBS source. Strategic scientific cases defined with the industrial partner include: ferrite-to-austenite transformation and cementite dissolution during annealing, bainitic transformation in third-generation advanced high strength steels, and precipitation of carbides in bainitic and martensitic steels.

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PROJECT PARTNERS:

        ArcelorMittal          UGA

 

 

Vuk Manojlovic is working on microstructure dynamics in advanced steels studied with high-throughput X-ray diffraction and machine learning project. This project involves collaboration between the ESRF, ArcelorMittal and UGA.