This folder contains image data sets from 14 separate serial sectioning sessions. The entire data folder consists of 1379 8 bit tif images and is 47.3 GB in size. Serial sectioning was performed using FEI Helios 660 NanoLab focused ion beam scanning electron microscope (FIB SEM) and Auto Slice and View G3 software. The sample was a heavy metal stained and resin embedded Caenorhabditis elegans (C. elegans) that were exposed to 60 nm Au nanoparticles. Detailed descriptions of the worm preparation and resin block processing are described in Johnson, M.E. et al. (ACS Nano, 2016).

This dataset contains thermographic measurements acquired during single and multiple track scans on bare substrates and on single layers of powder. The substrates and powder are nickel alloy 625 and the experiments are performed inside a commercial laser powder bed fusion machine. There are four experiment cases: 1) a single scan track on a bare substrate, 2) a single scan track on a single hand-spread layer of powder, 3) multiple (39) scan tracks covering an area on a bare substrate, and 4) multiple (39) scan tracks solidifying a single hand-spread layer of powder.

NIST is developing metrics and test methods to benchmark the performance of robotic systems when performing manufacturing tasks. The ability to perform simple insertions is critical for robotic systems in manufacturing. A simple peg-in-hole test was designed to measure a robotic system's capability for performing these simple insertions. The dataset captures the performance metrics of a robotic system outfitted with a robotic hand and a robotic gripper to study the effect of next-generation robotic hand technology versus conventional parallel gripper technologies.

This software provides a framework to generate events with both application payload identification and timestamps. Events information is logged at each producer and consumer. The logs can be used to derive latency and reliability metrics for cyber-physical systems experiments in which wireless communication is used for messaging.

The Additive Manufacturing Benchmark Test Series (AM-Bench) is developing a continuing series of controlled benchmark tests, in conjunction with a conference series, with two initial goals: 1) to allow modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark test data, and 2) to encourage additive manufacturing practitioners to develop novel mitigation strategies for challenging build scenarios.

A method was developed to reduce the point-based registration error by restoring the rigid body condition (RRBC method). Registration is the process of transforming one coordinate frame to another coordinate frame. The coordinate frame from which points are transformed is called the working frame and the coordinate frame to which points are transformed is called the destination frame. The RRBC method can be used to reduce the uncertainty of a hole location and thus, improve the success rate for insertion tasks.

These measurements were performed as part of the 2018 Additive Manufacturing Benchmark Test Series (AM-Bench), specifically supporting the melt pool geometry challenge (CHAL-AMB2018-02-MP) and cooling rate challenge (CHAL-AMB2018-02-CR).

In this study, a rail of a linear axis testbed was intentionally degraded to various levels, affecting the carriage motion, which can be deduced with either an inertial measurement unit (IMU) or a laser-based reference system. The rail was degraded over a 10-cm-long region. Thus, the two trucks that move on the rail interact with this region of the rail, changing the translational and angular error motions of the carriage. To mechanically simulate spalling, a handheld grinder was used to wear the surface of the raceway groove of the rail.

These measurements were performed as part of the 2018 Additive Manufacturing Benchmark Test Series (AM-Bench). This dataset and the associated experiments are part of a continuing series of controlled benchmark tests, in conjunction with a conference series, with two initial goals, 1) to allow modelers of Additive Manufacturing processes to test their simulations against rigorous, highly controlled additive manufacturing benchmark test data, and 2) to encourage additive manufacturing practitioners to develop novel mitigation strategies for challenging build scenarios.

Wireless technology is a key enabler of the vision of the future factory work-cell. Such work-cell will operate autonomously with a high degree of mobility enabled by wireless technology. This model describes the work-cell using the Systems Modeling Language (SysML). Using SysML the structural and parametric characteristics of the work-cell are described. Our model provides the architectural components and performance constraints of the work-cell in which wireless is used for a significant portion of connectivity. It identifies the structural components, interfaces, and data flows.