The Smart Manufacturing Operations Planning and Control Program will develop and deploy advances in measurement science that enable performance, quality, interoperability, wireless and cybersecurity standards for real-time prognostics and health monitoring, control, and optimization of smart manufacturing systems.
This program concluded in 2018. Related research is now in the Model-Based Enterprise Program.
The Smart Manufacturing Operations Planning and Control Program will enhance U.S. innovation and industrial competitiveness by facilitating the adoption of smart manufacturing systems (fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs). This program will enable smart manufacturing based on performance metrics and process control, prognostics and health management (including diagnostics and maintenance), integrated wireless platforms, industrial control security, and efficient systems analysis during operations. Test beds will be used to evaluate architectures, standards, and scale effects for reference implementations. The resulting manufacturing tools and resources will enable: increased efficiency of operations; manufacturing process development cycles at reduced risk, time and cost; and orders of magnitude expansion of manufacturers across increasingly diverse market segments. These efforts will directly address critical manufacturing needs as reported in 2011.
What is the problem?
The success of smart manufacturing systems depends upon the ability to easily and rapidly reconfigure factory production and supply networks to optimize system performance. Such systems must deal effectively with uncertainty and abnormal events and learn from past experience to enable continuous improvement. These systems must enable seamless interoperability between small, medium, and large manufacturers. The complexity of the overall challenge is due to:
Other specific problems exist within many existing manufacturing environments. Detailed automated prognostics and diagnostics are conducted at the lowest levels whereas higher-level prognostics and diagnostics are reliant upon detailed undocumented, ad-hoc human intervention and/or broadly-defined automated means. Methods, protocols, and tools are lacking for easily integrating and assuring operational efficiency of networked machine tools and robots, resulting in high costs and long lead times in responding to dynamic production requirements. The machine tools, robots, tooling, and equipment needed for smart manufacturing systems are complex. Modeling the information needed to easily integrate this equipment and measure its performance requires experts in many manufacturing areas, each with their own approaches.
In addition, common security technologies, such as encryption and device authentication have not been widely applied in smart manufacturing systems. Manufacturers are hesitant to adopt these advanced security measures due to potential negative performance impacts. This is exacerbated by a threat environment that has changed dramatically with the appearance of advanced persistent attacks (e.g. Stuxnet) that specifically target industrial systems.
Open standards-based technology is lacking that enables smart manufacturing systems to communicate, interact, exchange information, make decisions, detect and respond to faults, and perform in a collaborative and reliable manner. Specific needs include standard data models, communication protocols, interface standards, and security procedures. Filling these technology gaps would enable widespread adoption of smart manufacturing systems and increase America's opportunity to obtain a greater manufacturing market share in the global environment. NIST participation is needed to speed development of this technology. NIST-developed testing tools and performance metrics are needed to ensure that the technologies are sound.
What is the new technical idea?
The new idea is to address the measurement science needs in smart manufacturing that will:
What is the research plan?
The research plan consists of a portfolio of interrelated projects that focus on key areas of measurement science needed to achieve successful development and implementation of smart manufacturing systems. Collectively they provide a comprehensive approach that will lead to new industry standards and practices, which will result in efficient dynamic production systems. These projects all will be initiated with an assessment effort where industrial requirements for the project are elicited and analyzed. These requirements will scope the projects and guide the development and standardization work in subsequent years.
The Wireless Platforms for Smart Manufacturing project will deliver an integrated methodology and protocols to enable, assess, and assure the real-time performance of secure wireless platforms in smart manufacturing systems. This will involve the development of performance metrics, measurement science-based methodology, and guidelines that will facilitate the deployment of wireless technologies in the smart manufacturing environment. A smart manufacturing environment will require a variety of wireless technologies to provide seamless connectivity from low-power sensor nodes to high data rate video links. Standards-based wireless protocols will likely be used for cost reduction purposes. Even through this project focuses on standards-based wireless protocols used in industrial environments, the metrics, methodology, and guidelines developed could be applied to proprietary wireless protocols as well.
The Digital Thread for Smart Manufacturing project involves the effective communication of product designs to manufacturing engineering activities, the communication of manufacturing and assembly process considerations back to design engineers, and the use of the final component specifications for form and fit in the inspection systems and in-process measurement systems associated with product manufacture and assembly. This project is primarily concerned with the information content for the purpose of sharing in these various stages of the process rather than the mechanism for data transfer. It seeks to identify the information requirements, to formalize and promulgate descriptions of those requirements, and to support development and promulgation of standards for the interchange of that information. Use cases and activity models will be developed that will be used to derive the high-level information requirements. Likewise the state of the practice will be analyzed to determine what solutions exist, and work with industry to prioritize efforts to address gaps. This project will also include identification of information elements to support filling high priority gaps. Information elements will be formally modeled and codified in a way that is useful to the development of supporting tools. The analysis and dissemination of its results will provide the technical underpinning for new standards and tooling that enable more rapid development of manufacturing systems that reliably produce high-performance products.
The Prognostics, Health Management and Control project will identify the requirements for diagnostics and prognostics through the analysis of existing industry capabilities and best practices. Then, a hierarchical methodology will be developed to determine data sources for diagnostics and prognostics at the component, sub-system, and system levels within smart manufacturing systems. Once the methodology has been developed, protocols will be designed to facilitate communication of metrics across the component, sub-system, and system levels for diagnostics and prognostics. Standardization begins with the development of validated reference datasets, use cases, and test scenarios for implementation of protocols needed for diagnostics and prognostics. This will allow manufacturers, solution providers, and suppliers to benchmark and assess the robustness of their diagnostic and prognostic implementations. Conformity assessment tools will then be developed for the new protocols. These tools will enable industry to assure their implementations meet industry standards supporting real-time prognostics and diagnostics to enhance the efficiency of dynamic production systems.
The Cybersecurity for Smart Manufacturing Systems project will first host a security performance impacts workshop to determine the real-time measurements required to quantitatively determine the impact of cybersecurity on real-time performance, resource use, reliability and safety of smart manufacturing systems. Two research challenges will be addressed in this phase. The first challenge is the development of comprehensive requirements and use cases that represent practical, interoperable cybersecurity approaches for real world needs of complex dynamic production systems. The second challenge is the development of a suite of specific tests that measure the impact of cybersecurity technology when fulfilling these requirements. The project will develop a smart manufacturing system cybersecurity testbed to implement the test suite, and analyze the performance impact (e.g. latency, jitter) and operational impacts (e.g. efficiency, productivity) of the cybersecurity safeguards and countermeasures. NIST will then work with standards development organizations to develop new guidelines and standards to facilitate the implementation of cybersecurity requirements in dynamic production systems that do not negatively impact the performance of the system.
The Systems Analysis Integration for Smart Manufacturing Operations project will deliver methods and protocols for unifying discipline-specific engineering analysis information and integrating it with existing unified systems modeling information. The project will use overall system models to coordinate discipline-specific engineering analysis by identifying and eliminating inconsistencies between systems and analysis models, and between analysis models themselves. The project will first identify discipline-specific analysis methods and tools useful in manufacturing operations. Then it will identify redundancies between systems models and the selected analysis methods and tools, and develop methods and protocols that prevent inconsistencies. The research will be carried out with logical formalizations of system and analysis information to identify inconsistencies, and guide development of methods and protocols to eliminate them. The result will enable systems modeling tools and discipline-specific analysis tools to efficiently exchange and use information during smart manufacturing operations.
Some recent accomplishments for Cybersecurity for Smart Manufacturing Systems:
Some recent accomplishments for Digital Thread for Smart Manufacturing:
Some recent accomplishments for Prognostics, Health Management, and Control:
Some recent accomplishments for Systems Analysis Integration for Smart Manufacturing Operations:
Some recent accomplishments for Wireless Platforms for Smart Manufacturing: