Acquisition of Real-Time Operation Analytics for an Automated Serial Sectioning System

Abstract

Mechanical serial sectioning is a highly repetitive technique employed in metallography for the rendering of 3D reconstructions of microstructure. While alternate techniques such as ultrasonic detection, micro-computed tomography, and focused ion beam milling have progressed much in recent years, few alternatives provide equivalent opportunities for comparatively high resolutions over significantly sized cross-sectional areas and volumes. To that end, the introduction of automated serial sectioning systems has greatly heightened repeatability and increased data collection rates while diminishing opportunity for mishandling and other userintroduced errors. Unfortunately, even among current, stateof-the-art automated serial sectioning systems, challenges in data collection have not been fully eradicated. Therefore, this paper highlights two specific advances to assist in this area; a non-contact laser triangulation method for assessment of material removal rates and a newly developed graphical user interface providing real-time monitoring of experimental progress. Both are shown to be helpful in the rapid identification of anomalies and interruptions, while also providing comparable and less error-prone measures of removal rate over the course of these long-term, challenging, and innately destructive characterization experiments.

AFRL Additive Manufacturing Modeling Series: Challenge 4, 3D Reconstruction of an IN625 High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning

“…Prior to SS, the outer rim of the thermoset mount was machined using a lathe to create a ledge surrounding the SS surface. The purpose of this offset ledge is to provide a convenient reference surface that is not altered during the SS experiment, which can be used to perform differential depth measurements using a variety of methods (focus values from optical microscopes, mechanical or laser-based profilometers, or a dial indicator, as discussed in [9]). Lastly, the sample was permanently affixed to a RoboMet.3D™ stainless steel sample fixture using a SEM-compatible epoxy (Epo-Tek Silver Conductive Epoxy, Ted Pella).…”

“…To avoid this problem, the use of materialographic mount material that contained carbon filler particles, combined with the use of large-area montage image data collection that created images with thousands of randomly oriented particles, enabled the use of the SIFT algorithm [14] to calculate the optimal registration. Second, a common method to measure the depth of the SS surface is by recording the value of the optical microscope focus motor position [9], which can be effective when both high numerical aperture objective lenses (which have a narrow depth of field) are used in conjunction with automated focusing algorithms.…”