Can unsupervised machine learning boost the on-site analysis of in situ synchrotron diffraction data?

Author:

Strohmann T.,Barriobero-Vila P.ORCID,Gussone J.,Melching D.ORCID,Stark A.ORCID,Schell N.,Requena G.

Publisher

Elsevier BV

Subject

Condensed Matter Physics,General Materials Science,Mechanics of Materials,Metals and Alloys,Mechanical Engineering

Reference28 articles.

1. Neutrons and synchrotron radiation – unique tools for the characterization of materials;Lu;Adv. Eng. Mater.,2021

2. Inducing stable α + β microstructures during selective laser melting of Ti-6Al-4V using intensified intrinsic heat treatments;Barriobero-Vila;Materials (Basel, Switzerland),2017

3. Phase transformation kinetics during continuous heating of a β-quenched Ti–10V–2Fe–3Al alloy;Barriobero-Vila;J. Mater. Sci.,2015

4. W.B. Park, J. Chung, J. Jung, K. Sohn, S.P. Singh, M. Pyo, N. Shin, K.S. Sohn, Classification of crystal structure using a convolutional neural network, IUCrJ. 4 (2017) 486–494. 10.1107/S205225251700714X.

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