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Applicability of Quantum Machine Learning to Predict Mechanical Properties of Aluminium Alloys in 3D-Printing

March 7 at 3:55 pm
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  • Case Studies
  • Track 3
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Machine Learning can be used to model the process-property relation in 3D printing to predict the resulting materials properties. However, limited amounts of training data pose a challenge for Machine Learning for proper generalization and prediction. Quantum machine learning could help to generalize faster with small data sets and allows for more robust predictions, which is what’s explored in this use case.
Florian Funcke, Materials Engineer, BMW Group
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Featured Speaker(s):
Florian Funcke

Florian Funcke

Materials Engineer BMW Group
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Applicability of Quantum Machine Learning to Predict Mechanical Properties of Aluminium Alloys in 3D-Printing

March 7 @ 3:55 pm - 4:15 pm

Event Series Event Series (See All)
Featured Speaker(s):
Florian Funcke

Florian Funcke

Materials Engineer BMW Group

Machine Learning can be used to model the process-property relation in 3D printing to predict the resulting materials properties. However, limited amounts of training data pose a challenge for Machine Learning for proper generalization and prediction. Quantum machine learning could help to generalize faster with small data sets and allows for more robust predictions, which is what’s explored in this use case.

Florian Funcke, Materials Engineer, BMW Group

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Date:
March 7
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3:55 pm - 4:15 pm
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