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.

The dataset contains both the robot's high-level tool center position (TCP) health data and controller-level components' information (i.e., joint positions, velocities, currents, temperatures, currents). The datasets can be used by users (e.g., software developers, data scientists) who work on robot health management (including accuracy) but have limited or no access to robots that can capture real data. The datasets can support the:

This dataset provides surface height data from nickel super alloy 625 experiment samples built through laser powder bed fusion additive manufacturing. The experiment methodically varied part position and orientation relative to the build plate and recoater blade and details of the experiment and dataset are available in a Journal of Research at NIST article.

This tool is used to implement Monte Carlo analysis, which uses probabilistic sensitivity analysis to account for uncertainty. This tool is developed to follow the simulation segment of ASTM E1369. This technique involves a method of model sampling. Specification involves defining which variables are to be simulated, the distribution of each of these variables, and the number of iterations performed. The software then randomly samples from the probabilities for each input variable of interest. Three common distributions that are used include triangular, normal, and uniform.