In:
JOM, Springer Science and Business Media LLC, Vol. 73, No. 11 ( 2021-11), p. 3347-3355
Abstract:
Data-driven methods for developing new structural materials require large databases to identify new materials from known process routes, the resulting microstructures, and their properties. Due to the high number of parameters for such process chains, this can only be achieved with methods that allow high sample throughputs. This paper presents the experimental approach of the "Farbige Zustände" method through a case study. Our approach features a high-temperature drop-on-demand droplet generator to produce spherical micro-samples, which are then heat-treated and subjected to various short-time characterizations, which yield a large number of physical, mechanical, technological, and electrochemical descriptors. In this work, we evaluate achievable throughput rates of this method resulting in material property descriptions per time unit. More than 6000 individual samples could be generated from different steels, heat-treated and characterized within 1 week. More than 90,000 descriptors were determined to specify the material profiles of the different alloys during this time. These descriptors are used to determine the material properties at macro-scale.
Type of Medium:
Online Resource
ISSN:
1047-4838
,
1543-1851
DOI:
10.1007/s11837-021-04901-w
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2021
detail.hit.zdb_id:
2002726-6
SSG:
19,1
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