NIST released its newest software version of SimPROCESD, or Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-Making. SimPROCESD models discrete manufacturing systems such as the production and assembly of finished products like vehicles, consumer electronics, and consumer goods. SimPROCESD is free, available for download, and has instructions for getting started. Also see SimPROCESD announcement on LinkedIn.
NIST developed SimPROCESD to help researchers rapidly evaluate alternative manufacturing configurations, policies, and performance indicators. Within a manufacturing system, SimPROCESD can simulate:
Parts to represent the goods being produced
Machines for processing parts
Buffers for holding intermediate work in process between machines
Sources for introducing parts to the system
Sinks for receiving and storing parts that exit the manufacturing stream
Maintainers for conducting maintenance on machines according to user-specified policies.
The latest SimPROCESD is an improvement over the 2022 release and also enables users to simulate:
Manufacturing configurations in a multistage facility
Multiple modeling tests that allow users to estimate manufacturing performance indicators and their measures of uncertainty
Machine degradation and the impact on part quality
Maintenance policies, ranging from reactive corrective repairs, to proactive preventative policies
Condition monitoring systems, a class of smart connected devices that use artificial intelligence to detect, diagnose, or prognosticate risks in manufacturing
Multiple performance indicators and managed resources that characterize the manufacturing processes and maintenance policies.
In a July 10, 2023 press release, the standards organization Object Management Group (OMG®) announced approval of the NIST-aided Systems Modeling Language version 2 (SysMLv2) for public comment. It is an upgrade to the previous version, widely used to design and test complex systems. OMG’s approval of OMG has garnered industry and media attention.
NIST’s Conrad Bock and Raphael Barbau contributed significantly to SysMLv2’s development. It uses a more precise and complete modeling technique developed by NIST, based on its Process Specification Language. SysML2 also supports spatial requirements without initially committing to specific shapes, and then later defines and refines them. This capability is based on a NIST-developed, four-dimensional framework that integrates models of time and the three spatial dimensions.
In manufacturing, a product’s information must move across various systems in the production enterprise, from design to product support. This information flow is enabled by a family of standards called STEP (STandard for the Exchange of Product model data), which is the purview of ISO, an international standards organization.
STEP standards are written using an information modeling language EXPRESS that describes information elements and their relationships. Product information can include 3D geometric models of part designs, assemblies, and information about how that part must be manufactured, for example. Engineers are continually adding new STEP capabilities to support changing industry requirements. The EXPRESS language has a formal grammar that computers can understand.
However, EXPRESS models are commonly written in simple text editors that do not support the language features. Developing an EXPRESS schema is a complex and error-prone process that relies on an author's understanding of EXPRESS constructs and specifications. Moreover, schema files can be lengthy, with many references to other schema, which are hard to read and navigate. Improved readability also depends on manually formatting the syntax.
Seeking to provide a better user experience, NIST researchers developed “easyEXPRESS” to help EXPRESS authoring. They previewed the new tool at a recent meeting of the ISO 10303’s Technical Committee 184, Sub Committee 4, which is pursuing development and implementation of STEP Application Protocols and methods.
Designed to write accurate EXPRESS schemas, easyEXPRESS provides modern development capabilities to the EXPRESS community such as:
Syntax validation and highlighting
Intelligent code completion
Semantic validation and error reporting
Automated code fixes
Quick file and symbol navigation
Reference management
easyEXPRESS allows authors to focus on information modeling rather than trivial file formatting or navigation. easyEXPRESS will is undergoing final release testing and will be available as an extension to Visual Studio Code.
Wireless communications can enable Cyber-Physical Systems (CPS) and Internet of Things (IoT) systems that give industry greater flexibility in setting up and reconfiguring manufacturing systems, compared to today’s wired systems. However, realizing that potential depends on measuring a wireless network’s reliability and latency to ensure that a CPS meets industrial requirements.
The NIST team used the framework to evaluate a wireless network’s performance in a tesbed in which a leader and follower robot wirelessly coordinate the lifting and movement of an object. The framework uses a “Generative Adversarial Network,” which consists of two “dueling” networks. Both were provided respective performance data and were used as follows:
The “generative” network modeled the normal behavior of the robots with no interference impairing the wireless connection between them.
The “discriminator” network measured the impairments to the robots’ wireless connection and thus the deviation from their normal behavior.
The framework’s results can provide insights into the state of wireless communications within a given use case. The framework can also help determine when a CPS is not following the statistical behavior of its normal operations.
NIST researchers view the deep learning framework as an initial prototype for studying the performance of an industrial wireless system. The team plans to identify and characterize industrial wireless systems by assessing various operational, network, and spectrum parameters.
In June 2023, NIST’s Industrial Wireless System Team, as part of the NIST-led Industrial Wireless Technical Interest Group, held an online workshop on “Deployment Architectures and Technologies for Enhancing the Performance of Industrial Wireless Systems.” Approximately 100 personnel attended. The workshop explored the latest developments that could enhance the performance of wireless systems for critical industrial applications; these included wireless technologies; protocols; and platforms like 5G, 6G, and beyond.
Wireless technologies can help improve operating conditions, performance, and efficiency in emerging smart manufacturing practices and other mission-critical, industrial scenarios. However, deploying wireless technologies for mission-critical industrial applications requires improving communications link performance and reliability in various operating environments to attain efficiency and safety.
The workshop included eleven presenters from government, industry, and academia. They addressed high-performance W-Fi and 5G private networks; open-source initiatives; wireless sensor networks; time-sensitive networking; industrial wireless disruptors; millimeter wave technology advancements; and wireless for safety applications. NIST’s Rick Candell closed out the workshop, describing the P3388 Standard for industrial wireless systems performance assessment.
Information presented will help in several ways. It will enable manufacturers, system integrators, and users to determine suitable wireless technologies for use cases, with expected levels of performance. The information also will help deploy, integrate, and engineer wireless systems. Additionally, it will help enhance wireless performance in challenging environments, with physical obstructions and sources of interference.
NIST thanks the following, in order of appearance, for their contributions: Kang Lee (NIST); Nada Golmie (NIST); Dave Cavalcanti (Intel); Claude Seyrat (Firecell); Ingrid Moerman (Ghent University); Matt Simons (NIST); Pablo Sanz-Fontaneda (IKERLAN); Ted Schnaare (Emerson Automation Solutions); Mahin Atiq (Silicon Austria Labs); Scott McNeil (GPA); Mohamed Kashef (Hany) (NIST); and Richard Candell (NIST).
In a June 2023 presentation, NIST Networked Control Systems Group Leader, Keith Stouffer, described resources that could aid the Cybersecurity Workgroup supporting the Manufacturing Extension Partnership (MEP), which serves small and medium-sized manufacturers in 50 states and Puerto Rico. The presentation is available online.
Manufacturers have had more cybersecurity incidents than other critical infrastructure sectors, said Stouffer. Thus, NIST provides cybersecurity resources for operational technologies, such as those in manufacturing. NIST makes these resources available online. Stouffer summarized key publications, as described below.
Security control baselines for low-, moderate-, and high-impact OT systems
Cybersecurity Framework Version 1.1 Manufacturing Profile: NISTIR 8183 Revision 1: This profile adapts the NIST Cybersecurity Framework to manufacturing. It offers cybersecurity practices which best fit manufacturers’ needs, while minimizing negative impacts to system performance. NIST’s cybersecurity for OT testbed evaluated the profile, measuring the impacts of cybersecurity practices, including those for 42 technical capabilities. The profile can be implemented using the following guides:
Song gave an overview of sensors and actuators and their uses in IoT applications. They sense and signal some physical, chemical, or biological measurement and activate system responses in IoT systems such as smart cities, smart grids, and smart manufacturing. These IoT systems need large numbers of smart sensors and actuators to perform common functions, such as sensing, actuation, time synchronization, identification, localization, processing, and communicating data – and thus the need for interoperability and standards.
Song described the efforts of IEEE P1451.0 Working Group, which defines interfaces and communication specifications for smart sensors and actuators. Notably, the working group is developing a core standard for IEEE 1451 family, which defines:
Common functions for smart sensors and actuators
Network services
Transducer services
Transducer electronic datasheet formats
Universal unique identification
Time synchronization across sensor networks
Security for sensors and actuators and data
Song described the benefits of these standards. They will aid interoperability across smart sensor and actuator networks, enable the performance of common functions needed on IoTs, and help improve and maintain sensor accuracy. These standards also will facilitate interoperability and plug-and-play at network and sensor levels, make installation, maintenance and upgrades easier, and reduce life-cycle costs. More information related to Song’s presentation can be found at Q&A: Chair of a smart sensors IEEE standard working group hits the basics.
Today’s Internet architecture was developed to be “host-centric” – enabling connectivity between devices. However, this architecture does not efficiently find and retrieve content, which users seek. To enable more information-centric networking, the Named Data Network protocol has emerged.
NIST researchers stated that the NDN-DPDK software implements the NDN protocol and has resulted in the first NDN router that allows Internet users to search and retrieve named data at speeds higher than 100 gigabit per second. Researchers also stated that the science community is using NDN-DPDK for data-intensive science applications, such as for distributing Large Hadron Collider datasets.
Additionally, NIST researchers reported that NDN-DPDK is benefiting from the recent deployment on “FABRIC" (FABRIC is an Adaptive Programmable Research Infrastructure for Computer Science and Science Applications), which is funded by the National Science Foundation. FABRIC is a larger and faster network than most other experimental testbeds and thus allows the following for the NDN-DPDK router:
Benchmarking performance at a greater scale
Measuring latency across wide-area locations
Reduced complexity due to using the same hardware at different locations
NIST researchers plan to conduct a scenario at three node locations on the FABRIC testbed, in which files are downloaded from two NDN-DPDK file servers. Statistics on download times and transfer throughputs will be collected using a web-based application (“webapp”) they developed for this purpose.
NIST researchers view NDN-DPDK as accelerating NDN technology adoption for performance applications and enabling an Internet which is based on an Information-Centric Networking architecture.
In June 2023, NIST’s Automated Vehicle (AV) Research Lead, Ed Griffor, provided an overview of NIST’s AV Strategic and Emerging Research Initiatives to the Alliance for Automotive Innovation, which represents the major automotive industry and industry partners, globally.
NIST’s AV research program was informed by its 2022 AV workshop with over 800 AV stakeholders, stated Griffor. These stakeholders include the U.S. Department of Transportation, Michigan Tech, SAE International, and Virginia Tech Transportation Institute. Based on stakeholder input, NIST’s AV research program has focused on:
Developing measurement science for AV sensors and AV communications
Minimizing risk in artificial intelligence (AI) for AVs
Adapting the NIST Cybersecurity Framework to AVs
Developing testing capabilities to measure how AV sub-systems work together
For the new testing capabilities, NIST is developing a reproducible, systems interaction testbed, which uses measurement science and standards, for assessing AVs. The testbed contains both virtual (simulation) and physical components. NIST’s testing approach begins with simulations, followed by laboratory implementation and then track testing with partner institutes.
On September 5-8, 2023, noted Griffor, NIST will conduct its 2023 Standards and Performance Metrics for On-Road Automated Vehicles Workshop. The workshop will provide industry keynote presentations an update on NIST research including measurement science for roadway infrastructure that supports AVs, and a discussion forum for stakeholder input.
Today, driving intelligence resides on automated vehicles (AV). However, NIST’s Transformational Networks and Services Group Leader, Tao Zhang, offered a vision of a more distributed driving intelligence, in his presentation, “Vehicle Teleoperation: A Long-Term View,” to IEEE’s Intelligent Vehicles Symposium 2023, held this summer.
Zhang stated that teleoperations, which help operate AVs from a distance, will increasingly use artificial intelligence (AI). Presently, teleoperations involve humans either remotely operating AVs, or giving backup help if needed, with growing remote ADAS (advanced driver assistance system) capabilities enabled by AI. For example, AI-enabled ADAS may provide remote situational awareness, predicting hazards ahead and/or helping to avoid collisions. Eventually, teleoperators may become more artificially intelligent, with software taking over more remote driving capabilities.
Over the long term, artificial driving intelligence could spread between AVs and remote entities, like cloud and edge computing systems, providing solutions for many challenges facing AVs. This distributed intelligence could include AVs and local network servers using edge learning – collecting and sharing information with each other and the cloud.
This automated collaboration could enable several capabilities. It could contribute to the building of AI models required to support AV operations. This distributed driving intelligence could also enable “collaborative safety,” meaning all intelligence entities would work together to protect AVs, such as at crossings and in hazardous situations. Moreover, this automated collaboration would enable continuous learning, benefiting AV operations in other ways.
Zhang also pointed out that realizing distributed intelligence depends on testing, creating a supporting architecture, developing security, standardization and more.
Automated vehicles (AV) will need updates on driving conditions. Past studies envision roadside infrastructure transmitting such updates via beams of concentrated, millimeter radio waves. The challenges, though, are:
Accurately determining the location of a rapidly moving AV so as to track it with a beam; and
Forming the optimum beam within a short time slot that will reliably transmit data at high rates and low latency.
The method’s reinforcement learning helps the roadside infrastructure optimize the predictions of rapidly moving AV locations based on their downlinks. It also helps the roadside infrastructure form and adjust optimum beam patterns for transmitting data to AVs.
This method was based on NIST researchers using a reinforcement learning framework, in which they mapped the parameters that influence the performance of vehicle-to-infrastructure communications into state, action, and reward forms. They also found that beam tracking accuracy and beam optimization could be increased by revising this framework.
NIST researchers used simulations to assess the method. The results showed that this method performs well in tracking accuracy, data rate, and temporal efficiency. Simulations also show that the selected framework outperformed other frameworks that were considered.
Sign up now for NIST’s free, virtual workshop on Standards and Performance Metrics for On-Road Automated Vehicles (AV), September 5-8, 2023. Registration and schedule are available online.
This workshop brings together the automated vehicle community to provide an update on NIST’s work in the area, provide a forum for feedback, and set a path forward to ensure that NIST’s AV program provides the greatest value to the community. NIST’s work has focused on AV perception, communications, cybersecurity, artificial intelligence, and system interactions.
Keynote speakers include:
Overall Keynote: Ann Carlson (Chief Counsel, NHTSA)
AI Keynote: Aleksander Madry (Director of the Center for Deployable Machine Learning, MIT)
Communication Keynote: Jim Misener (Global V2X Ecosystem Lead, Qualcomm)
Cybersecurity Keynote: Anuja Sonalkar (CEO and co-founder, STEER)
Infrastructure Keynote: Ed Straub (VP and Director of Automation Office, SAE)
Systems Interaction Keynote: David Agnew (Vice President, DataSpeed)
This is the second in a series of workshops. The first, held in March 2022, drew over 800 attendees. Many stakeholders view these workshops as key to understanding AV measurement and the future of automated vehicles. On-road AVs are expected to significantly influence key aspects of daily life. Yet, these complex systems can pose a safety risk in the event of their unexpected performance. Thus, industry and government agencies seek to characterize performance in order to mitigate risks to manufacturers and consumers.
On September 12, 2023, NIST will conduct its 6G Core Networks Workshop, online and onsite at the National Cybersecurity Center of Excellence in Rockville, Maryland.
The workshop’s goal is to discuss the transition from the 5G to 6G ecosystem. This transition poses great challenges while leading to profound economic, scientific and social changes. It is important to identify challenges, develop more stringent key performance indicators, and find solutions, and create new standards as needed.
Specifically, the workshop will address:
Evolving 5G service-based architectures and technologies (e.g., network slicing), to meet 6G needs
Managing the growing number of users, devices, and network traffic
6G core network architectures and automation
Enabling end-to-end service and quality assurance
The roles of AI in 6G core networks and end-to-end service enablement
Managing differentiated and even customized network services
Cloud and edge computing for 6G
How opensource and open testbeds can help advance 6G
Making 6G systems resilient
The workshop’s morning sessions will focus on the current landscape, gaps, and challenges. Afternoon sessions will consist of panel discussion on selected topics. More information on the workshop and registration are available online.
Annually, NIST selects students for Summer Undergraduate Research Fellowships (SURF) to work in areas that support its mission. NIST seeks to inspire students to serve in these fields. During their 11-week internship, students are exposed to cutting edge research areas and NIST researchers benefit from their fresh perspectives and thinking. This summer, the following SURF students researched and presented on projects in NIST’s Smart Connected Systems Division:
Benjamin Philipose, Seattle University: After working as a 2022 SURF intern on automated vehicle behaviors, Philipose returned in 2023 to investigate the impact of communications network latency on time-critical decisions for automated vehicles (AVs). Philipose examined how physics and network co-simulations could assess such scenarios as AVs braking at varying speeds, with and without communications.
Benjamin Winig, University of Maryland: Winig assessed uses and benefits of simulating manufacturing environments using NIST’s newly released SimPROCESD. Specifically, Winig focused on simulating and graphing manufacturing events, like those in processing a part. This event graphing included degradation of machine health over time and showing data that indicated when machine repairs were needed.
