5 Werkstofftechnik
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Creep and creep damage behavior of stainless steel 316L manufactured by laser powder bed fusion
(2022)
This study presents a thorough characterization of the creep properties of austenitic stainless steel 316L produced by laser powder bed fusion (LPBF 316L) contributing to the sparse available data to date. Experimental results (mechanical tests, microscopy, X-ray computed tomography) concerning the creep deformation and damage mechanisms are presented and discussed. The tested LPBF material exhibits a low defect population, which allows for the isolation and improved understanding of the effect of other typical aspects of an LPBF microstructure on the creep behavior. As a benchmark to assess the material properties of the LPBF 316L, a conventionally manufactured variant of 316L was also tested. To characterize the creep properties, hot tensile tests and constant force creep tests at 600 °C and 650 °C are performed. The creep stress exponents of the LPBF material are smaller than that of the conventional variant. The primary and secondary creep stages and the times to rupture of the LPBF material are shorter than the hot rolled 316L. Overall the creep damage is more extensive in the LPBF material. The creep damage of the LPBF material is overall mainly intergranular. It is presumably caused and accelerated by both the appearance of precipitates at the grain boundaries and the unfavorable orientation of the grain boundaries. Neither the melt pool boundaries nor entrapped gas pores show a significant influence on the creep damage mechanism.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.