Development of an automated 3D metallography system and some first application examples in microstructural analysis
A. Lemiasheuski, E. Bajer, G. Oder, Alexander Göbel, René Hesse, Anja Pfennig, Dirk Bettge
- Year
- 2023
- Citations
- 4
Abstract
Abstract Traditional metallography relies on the imaging of individual section planes. However, conclusions as to spatial shapes and microstructural arrangements can only be drawn to a limited extent. The idea to reconstruct three-dimensional microstructures from metallographic serial sections is therefore obvious and not at all new. However, the manual process of preparing a great number of individual sections and assembling them into image stacks is time-consuming and laborious and therefore constitutes an obstacle to frequent use. This is why the Federal Institute for Materials Research and Testing, or BAM for short ( Bundesanstalt für Materialforschung und -prüfung ), is developing a robot-assisted 3D metallography system performing the tasks of preparation and image acquisition on a metallographic section fully automatically and repeatedly. Preparation includes grinding, polishing and optional etching of the section surface. Image acquisition is performed using a light optical microscope with autofocus at several magnification levels. The obtained image stack is then pre-processed, segmented and converted to a 3D model resembling a microtomographic image, but with a higher lateral resolution at large volumes. As opposed to tomographic techniques, it is possible to perform traditional chemical etching for contrasting. The integration of a scanning electron microscope is in the planning stages. Studies conducted so far have demonstrated the possibility of visualizing hot gas corrosion layers, gray cast irons and ceramic-based microelectronic structures (vias).
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992