Reconstruction of Incomplete X-Ray Diffraction Pole Figures Using Deep Learning
| dc.contributor | {"last":"Degener","first":"Sebastian","role":"Researcher","affiliation":"University of Kassel, Institute of Materials Engineering, Metallic Materials"} | |
| dc.contributor | {"last":"Meier","first":"David","role":"Researcher","affiliation":"Optics and Beamlines, Helmholtz-Zentrum Berlin"} | |
| dc.contributor | {"last":"Ragunathan","first":"Rishan","role":"Researcher","affiliation":"University of Kassel, Intelligent Embedded Systems"} | |
| dc.contributor | {"last":"Liehr","first":"Alexander","role":"Researcher","affiliation":"University of Kassel, Institute of Materials Engineering, Metallic Materials"} | |
| dc.contributor | {"last":"Sick","first":"Bernhard","role":"ProjectLeader","affiliation":"University of Kassel, Intelligent Embedded Systems","id":"orcid","id_value":"0000-0001-9467-656X"} | |
| dc.contributor | {"last":"Niendorf","first":"Thomas","role":"ProjectLeader","affiliation":"University of Kassel, Institute of Materials Engineering, Metallic Materials","id":"orcid","id_value":"0000-0003-2622-5817"} | |
| dc.contributor.author | {"last":"Vollmer","first":"Malte","affiliation":"University of Kassel, Institute of Materials Engineering, Metallic Materials"} | |
| dc.date.accessioned | 2023-07-11T13:22:00Z | |
| dc.date.available | 2022-11-24T12:20:53Z | |
| dc.date.available | 2023-07-11T13:22:00Z | |
| dc.date.Issued | 2019-05-28 | |
| dc.identifier.uri | https://daks.uni-kassel.de/handle/123456789/48.4 | |
| dc.identifier.uri | https://doi.org/10.48662/daks-14.4 | |
| dc.description | Iron-based shape memory alloys are promising candidates for large-scale structural applications due to their cost efficiency and the possibility of using conventional processing routes from the steel industry. However, recently developed alloy systems like Fe–Mn–Al–Ni suffer from low recoverability if the grains do not completely cover the sample cross-section. To overcome this issue, small amounts of titanium can be added to Fe–Mn–Al–Ni. This significantly enhance abnormal grain growth due to a considerable refinement of the subgrain sizes, whereas small amounts of chromium lead to a strong inhibition of abnormal grain growth. By tailoring and promoting abnormal grain growth it is possible to obtain very large single crystalline bars. | de_DE |
| dc.description | This dataset provides pole figure measurements performed on a 300 mm long Fe-Mn-Al-Ni-Ti bar with a diameter of 6.3 mm consisting of two abnormally grown grains. The investigated lattice plane 211 of the present body centered cubic (BCC) phase was measured using a cobalt anode at theta = 98° on the diffractometer Seifert XRD 3003 Micro operated at 40 kV and 35 mA, equipped with a monochromator in the secondary beam paths and a polycapillary with a beam size of 2 mm in diameter in the primary beam path. | de_DE |
| dc.description | IMPORTANT: In case you use the data please cite our corresponding article https://doi.org/10.1038/s41598-023-31580-1. | de_DE |
| dc.description.sponsorship | {"funderName":"Universität Kassel - Programmlinie Zukunft","awardTitle":"Digitalisierung in der Werkstofftechnik (DigiWerk)"} | |
| dc.language.iso | eng | de_DE |
| dc.relation | {"relationType":"Cites","relatedIdentifierType":"DOI","id_value":"https://doi.org/10.1038/s41467-019-10308-8"} | |
| dc.relation | {"relationType":"IsSupplementTo","relatedIdentifierType":"DOI","id_value":"https://doi.org/10.1038/s41598-023-31580-1"} | |
| dc.relation | {"relationType":"IsSupplementTo","relatedIdentifierType":"DOI","id_value":"https://doi.org/10.21203/rs.3.rs-1968155/v1"} | |
| dc.rights | Creative Commons Attribution 4.0 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | X-Ray Diffraction | de_DE |
| dc.subject | Deep Learning | |
| dc.subject | Pole Figure Reconstruction | |
| dc.subject | Coarse-grained Microstructures | |
| dc.subject | Pole Figures | |
| dc.subject | Shape Memory Alloy | |
| dc.subject | FeMnAlNiTi | |
| dc.subject.classification | 405-01 Metallurgische, thermische und thermomechanische Behandlung von Werkstoffen | de_DE |
| dc.subject.ddc | 620 | |
| dc.title | Reconstruction of Incomplete X-Ray Diffraction Pole Figures Using Deep Learning | de_DE |
| dc.type | Dataset | de_DE |
| local.ka.faculty | FB15:Maschinenbau | de_DE |
| local.ka.department | Institute of Materials Engineering, Metallic Materials | de_DE |
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