Smart Manufacturing Operations Planning and Control (SMOPAC) is one of the programs under the EL’s smart manufacturing research focus. The mission of this program is to
Develop and deploy advances in measurement science that enable performance, quality, interoperability, wireless, and cybersecurity standards for real-time prognostics and health management, control, and optimization of smart manufacturing systems.1
This program currently has a budget of $9.7 million. There are 24 full-time equivalent (FTE) employees that include 22 federal staff, 3 guest researchers, and 12 new hires.
ASSESSMENT OF TECHNICAL PROGRAMS
During the review five SMOPAC projects were presented: (1) Digital Thread for Smart Manufacturing; (2) Prognostics, Health Management, and Control; (3) Wireless Systems for Industrial Environments; (4) Cybersecurity for Smart Manufacturing Systems; and (5) Systems Analysis Integration for Smart Manufacturing Operations. In all of these projects, the teams have demonstrated multiple accomplishments through the standards that have been produced and the awards and recognition that have been received. All five projects exhibit sound approaches; each project has captured the state of the industry, identified current practices and standards, defined the challenges, and presented proposals for obtaining solutions. The projects all have inputs or collaborations with industry, academia, and/or government agencies and laboratories.
Over the past three decades certain industries (e.g., integrated circuits) have developed tools that allow the development of a product from concept to tracking in the field utilizing near-perfect models at each stage in the process. Industries associated with mechatronic products (i.e., a blend of mechanical and electronic systems), have attempted to develop a similar concept to the field tracking model-based approach as well, and the phrase “digital thread” has been employed for this concept. A great deal of resources have been devoted to such approaches, especially in the aerospace and automotive industries. These industries have validated approaches for all the key individual processes involving product development, manufacturing, and field diagnostics. However, it is desired that basic information be
1 National Institute of Standards and Technology (NIST), “Smart Manufacturing Operations Planning and Control Program,” updated March 17, 2017, https://www.nist.gov/programs-projects/smart-manufacturingoperations-planning-and-control-program.
utilized throughout the total process, as opposed to inefficient starting and stopping as the product moves from initial concept to placement in the field.
The EL is well aware of this problem area and was instrumental in developing one of the major standards involved in this process, namely the Standard for the Exchange of Product Data (STEP). The development of standards beyond STEP has been a very difficult process, and remains a major challenge for numerous reasons (e.g., industries protecting proprietary information and total systems models involving multiple time and spatial scales).
The goal of the Digital Thread for Smart Manufacturing project is to reduce the time required for part production by ensuring that the correct part requirements are provided. The team is developing a sound toolkit in order to support industry in managing their integrated design, manufacturing, and product lifecycle data based on a number of standards. It has been well-received to date, and information has been disseminated widely. There are similarities between the Digital Thread for Smart Manufacturing project and the DoD ManTech projects, which include the Army, Navy, and Air Force projects on digital thread and digital twin. It appears that there is communication between these groups. This is to be encouraged, as it allows NIST to have a government customer for collaboration.
The goal of the Prognostics, Health Management, and Control (PHMC) project is to deliver methods, protocols, and tools for robust sensing, diagnostics, prognostics, and control that enable manufacturers to respond to planned (e.g., scheduled change-overs, new productivity targets) and unplanned (e.g., faults, failures) performance changes, thereby enhancing the efficiency of smart manufacturing systems. This is done through the identification and analysis of key data that would support decision making. This project is also focused on providing vendor-neutral approaches and plug-and-play solutions. The work covers all levels—component, work cell, and systems, and is an area of great opportunity. This is clearly demonstrated by the automotive and aerospace industries’ use of diagnostics (e.g., determining on-board emissions sensor deterioration on the automobile in the field and in aircraft monitoring) that allow users to assess the health of the platform. Analysis of the data from the systems’ health monitoring is used to develop prognostics used for vehicle maintenance and sustainment.
An efficient and effective linear axis error detection methodology based on data collected from an inertial measurement unit (IMU) has been developed and verified on the linear axis testbed and machine tools within the PHMC project. Validation efforts are ongoing both internally within NIST and externally with several manufacturing collaborators. The team has developed a very unique program and testbed for prognostics and health management, and it has the proper hardware to work on machining projects for the manufacturing sector. The developed IMU sensor is a practical means for rapidly assessing machine health degradation. The team has demonstrated platform-based technologies to allow diversified users to connect machines and share Prognostics, Health Management, and Control project data. There is an expectation that this will be deployed in actual industrial settings. The plan is to develop an updated testbed with more integrated sensors to diagnose some typical problems in machining.
The Cybersecurity for Smart Manufacturing Systems project will establish a cybersecurity risk management program and validate this in real and virtual manufacturing systems. The roadmapping activity has established concerns of manufacturers relative to cybersecurity. The problem that was identified was that manufacturers are reluctant to adopt cybersecurity technologies because they are concerned about potential negative impacts to their manufacturing systems. The team has also identified the different cybersecurity standards that are currently in use. Tools and guidelines that the SMOPAC program has had a part in developing are well disseminated and are being tested on industrial control systems. A manufacturing profile is being published that will be applied to industry practices. A testbed has been established to validate the cybersecurity framework manufacturing profile. Data obtained from this research will be used to develop guidance for implementation of the framework.
The Wireless Systems for Industrial Environments project is designed to provide radio frequency (RF) measurements in factory environments that will be used to develop tools for measuring wireless and factory performance. The challenges that this project faces are in understanding how solutions may differ with the various systems, understanding the relationship between performance and the data being measured, and understanding how this information is used for prediction in future systems. The team has
invested in an RF channel emulator and has created a testbed that simulates industrial environments. This will provide significant capabilities for measurement and prediction. The approach recognizes the variability and resulting complexity of different manufacturing processes. Advanced data analytics methods will need to be demonstrated to manage interpretation of this data.
Opportunities and Challenges
The Digital Thread for Smart Manufacturing project is using a definition of “life cycle” that shows the end of the cycle as being when the initial product is delivered to the customer. This end of the life cycle is different from the DoD ManTech projects, which include the Army, Navy, and Air Force projects on digital thread and digital twin, where life cycle also includes the life of the aircraft. In all fields products have increasingly tremendous sensory and computational capabilities, and “in the field” capabilities for diagnostics and prognostics need to be considered as part of the life cycle in the digital thread. In aerospace and automotive industries (as well as with health care devices and others), the manufacturer needs to have the ability to track the vehicles and devices in the field for potential part defects as long as they are in service, and so life cycle is from cradle to grave. The SMOPAC program needs to extend the definition of life cycle to be more comprehensive and include manufacturing activities after the product is delivered.
While some of the tools and standards that are currently being developed may apply to post-delivery manufacturing, the manufacturing that occurs in maintenance, repair, and overhaul (MRO) and in depots may be different due to limitations on materials and equipment. These limitations may make the SMOPAC tools not useful in depots and in MROs. Modifications are constantly being made to many platforms for a variety of reasons that include upgrades of equipment, redesigns to improve performance, new materials, and incorporation of new technologies for manufacturing. The system needs to be flexible enough to accommodate these changes so that the goal can be achieved. The Digital Thread for Smart Manufacturing team needs to demonstrate clearly how modifications to the products and advanced technologies will be handled in the system that is being developed. The team can further consider sensor-rich and sensorless environments to integrate process and usage sensor data into closed-loop product design support.
The concept of digital thread can be applied to multiple industries of various sizes. This project is targeting medium-size manufacturers. While smaller manufacturers may either already have, or be able to adopt, these tools and standards, there is a question as to how large integrators (e.g., aerospace, automotive) will adopt NIST’s unique tools. What are the implications and challenges, technically and economically, for the range of companies that could use these tools? The SMOPAC program needs to put together a business case for small-, medium-, and large-scale industries to use these tools.
As previously mentioned, the Prognostics, Health Management, and Control team has developed an effective and efficient linear axis error detection methodology based on data collected from an inertial measurement unit (IMU), which has been developed and verified on the linear axis testbed and machine tools within the PHMC project. Validation efforts are ongoing both internally within NIST and externally with several manufacturing collaborators. The developed IMU sensor is very practical to assess machine health degradation rapidly. The team has demonstrated platform-based technologies to allow diversified users to connect to the machine and share their data, and it is planning to promote its research to system levels; however, the research scope needs to be more clearly defined (e.g., type of applications, sensors, analytics, etc.) before this happens. This is a relatively well studied and crowded research area investigated by both academia and industry. Collaboration with the Clean Energy Smart Manufacturing Innovation Institute (CESMII) will help in defining, identifying, and satisfying industry needs and requirements, as well as provide efficiencies in areas of overlapping research. The team also needs to address the need to apply this technology to other materials and processes such as those used for manufacturing polymeric materials or composites. Additionally, while this project is directed at machine
shops, the team needs to define how the technology would work at the larger systems levels that deal with integration and assembly of complex products.
The goal of the Systems Analysis Integration for Smart Manufacturing Operations project is to develop information models and transformations for submission to standards organizations to enable a more efficient integration of systems analysis. This is being done in collaboration with a number of industry and academic institutions. Currently, there are software packages that are employed in the design and manufacturing of products, and some of these are integrated to some degree. While the approach and areas for integration were well described during the review, it was not clear what had already been accomplished. There needed to be a clear linking of this project to the other projects and a description of the interdependence for data gathering and analytic development to maintain an integrated modeling environment. The relationship of this analysis and the physical findings of the testbed then needs to be demonstrated as validation of the data analytics. This project needs to be integrated with the others to show how it supports digital thread and prognostics.
The Cybersecurity for Smart Manufacturing Systems project has established a testbed to validate the cybersecurity framework manufacturing profile. The data obtained from this research will be used to develop guidance for implementation of this framework. In order to evaluate the resilience of the system, the testbed needs to include a simulation system that can generate virtual cyber threats.
A very sound approach has been established to address the impacts of cybersecurity systems on manufacturing systems. However, the team also needs to take into consideration companies that are reluctant to move to smart manufacturing because of the fear of being hacked. The frameworks and profiles that are being developed could also be enablers for companies that are considering the implementation of digital or smart manufacturing. There may be opportunities to incorporate the use of the standards at the initial setup of smart manufacturing for these companies. The team is also encouraged to include manufacturers that are not currently digital in the discussion of cybersecurity needs.
Wireless technology is important to all the other SMOPAC projects as well as the other smart manufacturing programs. There needs to be more integration in the testbeds of the Wireless Systems for Industrial Environments project with other projects and programs to determine the best wireless technologies. While the project research is just beginning, it needs to be considered more broadly.
Additionally, the team needs to demonstrate the approach to data analytics for the wireless systems. It also needs to develop testbeds that include both cybersecurity and wireless technology, and it needs to consider integrating with a cloud-based or edge-based environment to support a more tether-free monitoring system.
Several overarching suggestions for the SMOPAC program were identified during the review. First, the SMOPAC program needs to develop a high-level, integrated roadmap with measurable milestones. Such a roadmap would show the relationships of various markets (automotive, aerospace, etc.) and requirements to the technologies and between the technical areas themselves. This roadmap could help to define strategies for technology planning, resources needs, and the transition to manufacturing systems. This roadmap needs to be an integrated, high-level plan, not a set of semi-related programs. They can also develop and show project plans with measurable milestones to describe these 5-year projects. These project plans could be used as communication tools to inform management, the staff working on the project, users, emerging companies, and the public about the value of the project’s goals and when the results will be made available.
The impact to, and the role of, the manufacturers’ supply chains also needs to be defined for the projects. The SMOPAC program can also engage additional manufacturers, including new entrants to the manufacturing sector, such as start-up manufacturing companies and suppliers to ensure alignment and identify early adopters of advanced technologies, programs, tools, and processes. They could also partner with Manufacturing USA to establish STEP-like computer-aided design (CAD), computer-aided manufacturing (CAM), and computer-integrated manufacturing (CIM) Product Information Exchange Standards. They could also benchmark other smart manufacturing programs, such as those in EU Horizon 2020, to identify duplicative areas.
PORTFOLIO OF SCIENTIFIC EXPERTISE
All of the SMOPAC projects had a good mix of technical experts with varying levels of experience. With the Cybersecurity for Smart Manufacturing Systems and Wireless Systems for Industrial Environments projects, technical experts from other NIST laboratories (the Information Technology Laboratory for Cybersecurity and Wireless and the Communication Technology Laboratory for Wireless) were part of the teams. The credentials that were found in all the biographies showed achievements and recognition in their areas of expertise, such as PDES, Inc., Technical Excellence and Technical Management Awards and the DOC Bronze Medal. The technologists were very knowledgeable about the projects they had managed. The SMOPAC personnel collaborate well with each other and others outside the EL and NIST. Additionally, they have actively disseminated the results of their work in 19 journal articles and 29 conference papers.
Opportunities and Challenges
As mentioned, SMOPAC technologists were very knowledgeable about the projects they had managed. They are well-published and their publications are cited; still, more publications need to be submitted in juried technical journals.
Some of the technical personnel had industry experience that allows them to understand smart manufacturing and the challenges of an industrial environment. To improve this industry understanding, the SMOPAC program could solicit visiting scientists from manufacturing companies.
ADEQUACY OF FACILITIES, EQUIPMENT, AND HUMAN RESOURCES
Investments have been made in equipment that is required to gather data and verify the SMOPAC models in virtual environments. All of the testbeds that were toured during the review were well arranged to accomplish the work of the various SMOPAC projects; these testbeds included the Cybersecurity for Manufacturing Systems Testbed; Linear Axes Testbed; and the Industrial Wireless Systems Testbed.
Opportunities and Challenges
In some of the SMOPAC testbeds, arrangements are being made to integrate with other EL programs, such as robotics. While this is a good start to integrating the projects, they are very localized, and the level of integration is low. It would be better to have a larger space so that multiple projects and programs could be integrated into a larger, more complex testbed that more resembles some manufacturing environments. Validation of models under these conditions would provide meaningful data.
In conclusion, the SMOPAC program possesses much of the expertise, facilities, and equipment to accomplish the goals for each of its projects. While an overall perspective may still be needed, the individual projects themselves are well managed and incorporate manufacturing needs with technical challenges. The program area needs to consider related areas outside the boundaries of the work that they have defined to ensure that the technologies being measured and standardized are comprehensive.
Publishing in juried publications and making presentations at higher level conferences would help the scientists and technologists to develop their careers at a more international level.