A recently released NIST Advanced Manufacturing Series publication AMS 300-12 “Research Results and Recommendations for Universally Unique Identifiers in Product Data Standards” proposes recommendations for employing universally unique identifiers (UUIDs) to address findability of data in multi-domain and multi-life-cycle engineering and manufacturing contexts. In the model-based enterprise (MBE) paradigm, enterprises are fueled by the digital thread, an authoritative, integrated information flow that connects all phases of the product life cycle. The digital thread enables use of data-driven processes to build knowledge, make decisions, manage requirements, and control manufacturing execution. As information about a product moves through the supply chain, different systems consume or modify that information for a variety of reasons. Associating persistent and universally unique identifiers, in combination with human-readable identifiers, to key engineering requirements (also referred to as features or characteristics) enables the enterprise to track all information related to that requirement over the life of the product. Neither widely used product data standards nor commercial engineering software adequately support universally unique identifiers (UUIDs). This is a major roadblock to realizing the promises of the digital thread and model-based enterprise.
This research examines the use of UUIDs in product data standards primarily during the design to manufacturing and inspection workflow. The included product data standards are: ISO 10303, or STandard for the Exchange of Product model data (STEP), a model-based standard for communicating engineering design information developed and maintained by the ISO Technical Committee 184’s Subcommittee 4 on Industrial Data; MTConnect, a model-based standard for process execution data collection and classification from manufacturing equipment developed by the MTConnect Institute, a subsidiary of the Association for Manufacturing Technology; and Quality Information Framework (QIF), an integrated model for manufacturing quality information developed and maintained by the Digital Metrology Standards Consortium (DMSC).
The NIST publication presents current support for UUIDs in digital thread and digital twin standards, use cases and requirements for UUIDs in the product life cycle; research results; and recommendations for use of universal identifiers in commonly used product data standards.
NIST researcher Jing Geng of the Operational Technology (OT) Wireless Team, Smart Connected Systems Division, presented the paper, “An Industrial Private 5G Testbed for Networked Automation Systems” at the International Conference on Advance Intelligent Mechatronics in Boston on July 18, 2024. This paper introduces a novel 5G private networking testbed designed to support research across various industrial use cases. The NIST testbed offers precise control over the network and RF channel, enabling detailed measurement and new research capabilities. The motivation for this work is the growing need for industrial wireless solutions and the potential benefits of adopting 5G technology across different industrial sectors. The network architecture and main elements of the testbed include a software-based 5G system, network devices for radio channel control, precision measurement tools, 5G user equipment (UEs), and computing devices to support 5G-capable industrial scenarios. The primary contribution of this testbed is the creation of a 5G research platform that incorporates real industrial equipment. This platform can simulate the impact of aggressors in industrial environments and adapt to various use case scenarios. Moving forward, the OT Wireless Team will focus on research in 5G resource scheduling, Quality of Service (QoS) implementations, adaptive RAN optimization with intelligent control, RF aggressor impacts, and a gap analysis of 5G for OT applications.
Rick Candell of the NIST Communications Technology Laboratory (CTL) presented research findings at the International Conference on Advance Intelligent Mechatronics in Boston on July 17, 2024, on the current efficacy of IEEE 802.11ax Orthogonal Frequency Division Multiple Access (OFDMA) technology for use in time-critical applications. In the paper, “Latency-Sensitive Networked Control Using 802.11ax OFDMA Triggering,” Dr. Candell and his team reported that, while OFDMA has promise to support real-time deterministic communications capability for closed-loop robotic control, currently available devices must place more emphasis on supporting time-critical applications by providing users with parameter adjustment of the OFDMA frame triggering algorithm. Such control would allow for reduced latency and improved assurance that transmission deadlines are met. Currently, device manufacturers successfully design OFDMA implementations to support downlink traffic applications such as video download for multiple users in home and office environments. Typical 802.11 implementations focus more on data throughput rather than meeting transmission deadlines. Robotic control, safety, and other industrial applications are normally very uplink intensive, requiring low latencies rather than high throughput. The NIST team plans to focus efforts to better understand the performance gaps in the ubiquitous 802.11 technology to support the growing demand of wireless communications for controls.
A co-simulation integrates multiple software processes and physical hardware into a cooperative simulation to create high-fidelity system models. While co-simulations can produce more accurate models of complex systems than standalone simulators, they tend to have poor performance and major scalability challenges when many processes are integrated into the cooperative simulation. This is a significant problem for Internet of Things (IoT) systems which contain hundreds to thousands of independent devices that communicate with each other to control the system.
This paper introduces the concept of an aggregator that represents multiple IoT devices and integrates the devices as a single process into the co-simulation. The aggregation method was implemented to maintain unique identifiers for each device allowing them to directly interact with each other and other processes in the co-simulation, unaware that their messages are being aggregated. A co-simulation of connected and automated vehicles (CAVs), where road vehicles communicate with each other to influence their driving behavior using vehicle-to-vehicle (V2V) communications, was implemented to demonstrate the performance benefits of the aggregation method. A simple road network containing up to 36 communicating vehicles was simulated both with and without using the new aggregation method. The use of aggregation resulted in, on average, a 15 % reduction to execution time and up to a 95 % reduction in the memory requirements to run the co-simulation.
Every summer, NIST hosts summer students in its Summer Undergraduate Research Fellowships (SURF) program and Summer High School Intern Program (SHIP) to work in areas that support its mission. NIST seeks to inspire students to serve in these fields. During their summer internships, students are exposed to cutting edge research areas and NIST researchers benefit from their fresh perspectives and thinking. This year, the following SURF and SHIP students researched and presented on projects in NIST’s Smart Connected Systems Division: Andrew Oxenberg, Ben Winig, Nicolas Carbone, Kevin Song, Tyler Wong, and Nathan Wei (SURF) and Derek Hu (SHIP).
The Division’s Smart Connected Manufacturing Systems group hosted Andrew, Ben, Nico, and Kevin, who worked with their NIST mentors on several different activities. Andrew developed file isolation and dependency mapping to enhance the usability of navigation and visualization of dependencies using easyEXPRESS, a NIST-developed tool supporting use of STEP standards for the exchange of product data. Ben focused on creating data management foundations for a simulated manufacturing environment using SimPROCESD, a NIST-developed open-source tool for modeling and simulating discrete manufacturing systems. In addition, Kevin worked on developing a new graphical data analysis tool for SimPROCESD, and Nico focused on collecting and analyzing data from a collaborative robotic manufacturing testbed.
In the Division’s Internet of Things (IoT) Devices and Infrastructures group, Tyler focused on software development for IoT device interoperability analysis, including creating a tool to test data collected directly from IoT device communication. In the Division’s Networked Control Systems group, Nathan worked in the Industrial Wireless Systems project on supporting User Datagram Protocol (UDP) communications for a real-time control robotic application involving a leader and follower robot that communicate over wireless. Nathan also had the honor of being selected as one of the plenary speakers at the SURF Colloquium. As a SHIP student in the Division’s Transformational Networks and Services group, Derek learned concepts in machine learning (ML) and federated learning (FL) and the use of Python/Tensorflow for ML applications, and he experimented with server learning schedules for improving FL performance.
