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Different imaging techniques were employed to monitor Full Notch Creep Test (FNCT) experiments addressing environmental stress cracking in more detail. The FNCT is a well-established test method to assess slow crack growth and environmental stress cracking of polymer materials, especially polyethylene. The standard test procedure, as specified in ISO 16770, provides a simple comparative measure of the resistance to crack growth of a certain material based on the overall time to failure when loaded with a well-defined mechanical stress and immersed in a liquid medium promoting crack propagation.
Destructive techniques which require a direct view on the free fracture surface, such as light microscopy and laser scanning microscopy, are compared to non-destructive techniques, i.e. scanning acoustic microscopy and xray micro computed tomography. All methods allow the determination of an effective crack length. Based on a series of FNCT specimens progressively damaged for varied Durations under standard test conditions, the estimation of crack propagation rates is also enabled. Despite systematic deviations related to the respective Imaging techniques, this nevertheless provides a valuable tool for the detailed evaluation of the FNCT and its further development.
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.