The new platform allows users to upload, analyze and share measurements of surface roughness. Image: Rick Henkel, Lucas Frérot.
The new platform allows users to upload, analyze and share measurements of surface roughness. Image: Rick Henkel, Lucas Frérot.

Scientists from the University of Pittsburgh and the University of Freiburg in Germany have developed a software platform that facilitates and standardizes the analysis of surfaces. By allowing users to create a digital twin of a surface, the ‘’ platform can help predict, for example, how quickly a surface wears out, how well it conducts heat and how well it adheres to other materials. The scientists report the new software platform in a paper in Surface Topography: Metrology and Properties.

All engineered materials have surface roughness, even if they appear smooth when seen with the naked eye; when viewed with a microscope, they can resemble the surface of a mountain landscape. “It is of particular interest, in both industrial applications and scientific research, to have precise knowledge of a surface’s topography, as this influences properties like the adhesion, friction, wettability and durability of the material,” says Lars Pastewka from the Department of Microsystems Engineering at the University of Freiburg.

Manufacturers must carefully control the surface finish of products such as automobiles and medical devices to ensure their proper performance. At present, the optimal surface finish is found primarily by trial-and-error, by making a series of components with different machining practices and then testing their properties to determine which is best. This is a slow and costly process.

“It would be far more efficient to use scientific models to design the optimal topography for a given application, but this is not possible at present,” says Tevis Jacobs from the Department of Mechanical Engineering and Materials Science at the University of Pittsburgh. “It would require scientific advancements in linking topography to properties, and technical advancements in measuring and describing a surface.”

The platform facilitates both of these advances and standardizes the procedure, by automatically integrating the data from different analysis tools, correcting measurement errors and using the data to create a digital twin of the surface. The platform calculates statistical metrics and applies mechanical models to the surfaces, helping to predict behavior.

“The users can thus identify which topographical features influence which properties,” says Pastewka. “This allows a systematic optimization of finishing processes.”

The software platform also serves as a database on which users can share their measurements with colleagues or collaborators. Users can also choose to make their surface measurements available to the public. When they publish the data, a digital object identifier (DOI) is generated that can be referenced in scientific publications.

“We are continually developing and would like to add even more analysis tools, for example for the chemical composition of surfaces,” says Pastewka. “The goal is to provide users with a digital twin that is as comprehensive as possible. That’s why we also welcome suggestions for improvements to the software platform from users in industry and research.”

This story is adapted from material from the University of Pittsburgh, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.