journal article·2026-01-21Open access

Robot‐Assisted Automated Serial‐Sectioning and Imaging for 3D Microstructural Investigations

Michael Moschetti, Anton Lemiasheuski, Evgenia Bajer, Ilja Porohovoj, Artur Göbel, Anja Pfennig, Dirk Bettge, Robert Maaß

Advanced Engineering Materials· Volume 28· Issue 2

Abstract

Comprehensive materials characterization requires precise structural knowledge beyond traditional methods. The robot‐assisted automated serial‐sectioning and imaging (RASI) platform, developed at BAM, provides automated 3D metallographic reconstructions, enabling detailed microstructural analysis of technical materials. This article showcases RASI's capabilities through several case studies, including characterization of lamellar graphite in gray cast iron, porosity in sintered steel, melt pool morphology in additively manufactured 316L stainless steel, defects in metal‐ceramic packages, and oxidation behavior in an Fe‐12Cr‐2Co alloy. By automating sample handling, mechanical serial‐sectioning, etching, and optical imaging, RASI captures complex 3D microstructures with high precision and at high speed. This approach reveals microstructural features missed by 2D analysis, even using stereological assumptions. Specifically, statistically rare and large microstructural features, such as secondary phases or interconnected pores, become apparent, which 2D methods cannot reveal. The generated volumetric data can furthermore serve as quantitative reference datasets (i.e., the ‘ground truth’) essential for validating other 3D characterization techniques and computational models, helping to bridge the gap between predictive simulations and real‐world material behavior. RASI's modular design makes it a flexible tool that provides realistic 3D insights into materials, which can be used for advanced materials research, process optimization, and quality control.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Machine Learning in Materials Science · Advanced Materials Characterization Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Materials Engineering Microstructural Design and Degradation Division Federal Institute of Materials Research and Testing (BAM) Unter der Eichen 87 12205 Berlin Germany

Department of Engineering Sciences Technology and Life Division University of Applied Sciences (HTW) Berlin Wilhelminenhofstraße 75A 12459 Berlin Germany

Department of Materials Engineering Technical University of Munich 85748 Garchingen Germany

Department of Materials Science and Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA

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