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X-WR-CALNAME:EIT RawMaterials
X-ORIGINAL-URL:https://old.eitrawmaterials.eu
X-WR-CALDESC:Events for EIT RawMaterials
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TZID:Europe/Helsinki
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DTSTART:20200329T010000
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DTSTART;VALUE=DATE:20201210
DTEND;VALUE=DATE:20201211
DTSTAMP:20260824T150428
CREATED:20201102T152706Z
LAST-MODIFIED:20201124T162645Z
UID:25309-1607558400-1607644799@old.eitrawmaterials.eu
SUMMARY:Webinar: Machine Learning Steel Properties Simulation
DESCRIPTION:EIT RawMaterials consortiums EndureIT and CorTools have the common goal to develop machine learning based software for the purpose of steel characteristics simulation\, focusing on embrittlement and corrosion respectively. Extended simulation capabilities would support a larger degree of steel optimization and have incalculable effects on the steel industry and the society. \nOn 10 December 2020\, EndureIT and CorTools project partner Ferritico will host a public webinar together with EIT RawMaterials and the project consortiums. The webinar will focus on machine learning steel properties simulation and present the solutions being developed in the projects. \nIndustrial steel development\, manufacturing and implementation are time-consuming\, inefficient and manual processes\, but digitalization has the potential to change this. These processes are characterized by significant trial-and-error and physical testing as conventional physical modelling-based simulation software cannot simulate with satisfying accuracy for all steels and processes.  \nResearch shows that a data-driven machine learning approach can outperform physical modelling\, in terms of simulation accuracy and effectiveness\, for many steel characteristics. Hence\, machine learning-based steel simulation is becoming an important complement to physical-based modelling.  \nOne of the key success factors for developing machine learning software for steel simulation is data availability. Therefore\, collaboration and setting up consortiums to pool data where each partner e.g. has data for a certain steel composition range and property is of great importance.  \nFerritico\, a Spin-off from steel research made at KTH Royal Institute of Technology\, is developing simulation software for the steel and manufacturing industry\, combining metallurgical know-how and physical modelling with development of large databases and machine learning models.  \nEIT RawMaterials CorTools project is developing a corrosion resistance prediction model. Consortium partners are VTT\, Outotec\, Boliden\, Outokumpu\, Tecnalia\, ZAG and DMS.  \nEIT RawMaterials EndureIT project is developing an embrittlement prediction model. Consortium partners are KTH\, CEA and Outokumpu. 
URL:https://old.eitrawmaterials.eu/events/machine-learning-steel-properties-simulation-webinar/
LOCATION:Online\, Online
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