Filtern
Dokumenttyp
- Zeitschriftenartikel (2)
- Sonstiges (1)
Sprache
- Englisch (3)
Schlagworte
- MOUSE (2)
- Nanostructure quantification (2)
- Round Robin (2)
- Data analysis (1)
- Geteilte Infrastruktur (1)
- Intelligent tribological material characterization (1)
- Intelligente tribologische Werkstoffcharakterisierung (1)
- Interlaboratory comparability (1)
- Lab-to-field up-scaling (1)
- Lab-to-field upscaling (1)
Organisationseinheit der BAM
A round-robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions and two from powders. Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5% and half of the population width entries within 40%. Due to the added complexity of the structure factor, far fewer people submitted answers on the powder datasets. For those that did, half of the entries for the means and widths were within 44 and 86%, respectively. This round-robin experiment highlights several causes for the discrepancies, for which solutions are proposed.
A Round Robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions, and two from powders.
Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5 % and half of the population width entries within 40 %, respectively. Due to the added complexity of the structure factor, much fewer people submitted answers on the powder datasets.
For those that did, half of the entries for the means and widths were within 44 % and 86 % respectively. This Round Robin experiment highlights several causes for the discrepancies, for which solutions are proposed.
The prediction of the properties of the materials used in the operation of components is of enormous importance, in order to accelerate the development process of new components. To evaluate new materials in terms of their performance (in a component), the development of new innovative methods is necessary. These methods can also be summarized under the term lab-to-field or materials – upscaling, meaning materials being characterised in a laboratory and their properties being upscaled to the component performance by means of e.g. simulation. i-TRIBOMAT is a EU funded project (H2020, GA Nr. 814494) aiming at building an Open Innovation Test Bed for tribological material characterization and offering corresponding services from tribological characterization of new materials to simulation models for predicting the performance of industrial components. By bundling the infrastructure, know-how for characterization and building a digital platform, i-TRIBOMAT becomes the world’s largest open innovation test bed for tribological material characterization.