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Digital Twins and Emerging Technologies for SME Workcells


Small-and-Medium Enterprises (SMEs) have lagged in the adoption of robotic systems within their manufacturing operations despite the potential quality and productivity advantages that automation can provide. Challenges within SME workcells include more dynamic and less-structured environments, where production is geared towards small batches of varying types of products. Current high-volume manufacturing technologies are not well-suited for such low-volume applications. Additionally, successful selection, installation, and operation of robots requires technical competences, which SMEs typically do not have in-house, creating a significant barrier to entry.

This project is focused on the identification of new automation technologies that are particularly suited for manufacturing applications within SMEs. The project will also integrate findings from SME interactions to develop tools (software, models, and prototype artifacts), test methods, metrics, and standards that facilitate the integration and evaluation of emerging technologies in SME environments. These outputs will utilize sensor-derived information about the workspace to address situations in which robots must perform tasks in less-structured surroundings. The project aims to harness new technological developments to reduce the burdens SMEs confront during selection and installation of robotic systems, ensure greater robustness during operation, support rapid retasking, and enable smooth integration of sensors, grippers, and other tooling. Such emerging technologies include Digital Twins and other Extended Reality products that are newer to the market and lack a basis for comparison or evaluation. The measurement science tools provided by this project will be leveraged to advance the competitiveness of SMEs by enabling new manufacturing capabilities, as well as provide the bases for advancements in application-driven technology designs.

Finally, this project will serve as a bridge for interactions with small manufacturers, systems integrators, the Manufacturing Extension Partnership (MEP) centers, and other stakeholders, to identify the major obstacles to greater adoption of robots and the ongoing needs of SMEs when utilizing manufacturing robots. 



To identify opportunities for and barriers to greater adoption of robotics by small manufacturers, and to establish the metrology bases needed for assessing, accepting, and advancing novel technologies that meet the current and emerging needs of small- and medium-sized manufacturers.

Technical Idea
Small manufacturers could benefit greatly from the use of robots in the factory but are limited by the technical challenges inherent to installation and use of robots, sensors, and tooling within dynamic, unstructured environments. Previous iterations of this project (formerly, “Tools for Collaborative Robots in SME Workcells”) focused on identifying specific metrology-based solutions to enable SMEs to adopt collaborative robots into their workcells, and opportunities and challenges faced by SMEs when considering automation technologies in their facilities. Based on these past efforts, this project builds on the knowledge gained to provide test methods and metrics for evaluating emerging technologies such as digital twins, process intelligence, and interfaces to enable new manufacturing capabilities and protocols. The project will work with SMEs, systems integrators, and MEP centers to identify the key obstacles to adoption of robotics. Emerging technologies that allow robots to operate in these high-mix, low-volume environments are difficult to select and integrate effectively without a basis for evaluation. NIST can leverage the measurement science being developed by projects in the Measurement Science for Manufacturing Robotics (MSMR) program to inform systems integrators and small manufacturers, and help them select and integrate new technologies that allow robots to operate in environments that require frequent re-tasking.

Additionally, the project will develop tools that enable SMEs to evaluate industrial robots more easily, and thus help lower the barrier to entry for integration of automation systems. Emerging technologies such as Digital Twin can be a helpful tool for SMEs to evaluate new automation technologies. However, guidelines and examples for use of these new technologies are needed and this project can help provide such assistance.

Research Plan
The research plan focuses on three main aspects to achieve the ideas outlined above.

  • Identifying the current automation needs of SMEs and barriers to the adoption of robotics
    As the needs of SMEs evolve over time, the MSMR program should correspondingly update its research directions. This project will assist by performing an analysis of the previously conducted SME automation survey. Planned outputs include a report on the current state of automation in small manufacturers, which will give a sense of the achievements over the past iterations of the program. Additionally, a roadmap will be developed, integrating the needs of SMEs shown by the survey results. The roadmap will identify key focus areas in robotics where NIST can provide the most assistance to small manufacturers. Barriers to adoption will be identified, as well as areas where evaluation of existing automated workcells is needed. Additional discussions will be conducted with partner SMEs and the NIST-MEP Industry 4.0 working group as needed, to clarify situations and gain more perspective. Findings will be disseminated to the MSMR program, which will provide a basis for new efforts by other projects.
  • Evaluation of emerging technologies for manufacturing applications
    Novel methods can provide advantages to SMEs in flexibility, retasking, usability, and other aspects. However, SMEs are reluctant to invest in untested technologies. This project will investigate new commercial and research products to document their performance in manufacturing applications. One such highly-salient technology is Digital Twin, where a virtual replica of a workcell can be created to accurately represent the physical workcell, updating in real time to reflect changes in the environment and process status. Such a virtual representation provides advantages in designing, commissioning, integration, remote monitoring of equipment and processes, and enabling users to safely detect errors or evaluate production. However, the fidelity of such virtual environments has not been studied in-depth, and evaluation is needed to determine factors such as the accuracy or lag of the model. The project will develop tools for testing commercial digital twin systems, and develop metrics to evaluate their capabilities and benefits as relevant to small manufacturers.  

    The project will also complete surveys of future emerging technologies to assess the viability of new methods for SMEs. This will be used to select new focus areas for testing and development of metrics. Collaborations with external organizations specializing in emerging technologies will also be pursued. In particular, the project intends to continue NIST’s relationship with the Augmented Reality for Enterprise Alliance (AREA) to explore the application of Augmented Reality solutions for manufacturing applications.

    Finally, the project will explore replication of an SME workcell to create a testbed for implementation and evaluation of these emerging technologies.

  • Developing tools to assist SMEs in platform evaluation and selection
    The project will develop accessible, easy-to-use tools to enable SMEs to self-evaluate their needs and select robot models and the corresponding tools for their applications. The first planned output is a web database of industrial robot arms available in the US. The database will provide a high-level overview of their capabilities such that SMEs can narrow down platforms that meet their specifications. Future iterations of the database will include end-effectors such as grippers and their compatibility with the included arms. The web database will include the ability for manufacturers to submit updates as new models are released, and the database entries will be refreshed accordingly. As the database begins to be used by SMEs, we predict that robot manufacturers will also see it as a useful resource for increasing their reach to small enterprises and thus be incentivized to promote their new models to the database.

    On release of the first draft of the database, the project will hold discussions with MEP centers and partner SMEs to obtain feedback about the utility of the tool and iterate accordingly. Once the tool has more users, the project will survey stakeholders to solicit requests for additional tools that would be of use to small manufacturers as they expand into automation technologies.

Additionally, this project will continue to create relationships and expand discussions with small manufacturers, systems integrators, and MEP centers that occurred during the previous five years of the project. The project will serve as a conduit between the MSMR program and the small manufacturer’s perspectives & needs, first by communicating the Automation Survey results, and later by organizing visits to & discussions with local SMEs. The project can further provide a channel via MEP centers and other external contacts in order to communicate results and useful tools from the other MSMR projects. 

Recent Accomplishments
In 2023, the project conducted an extensive survey of Small-and-Medium Manufacturers regarding their current levels of automation. Questions were tailored to the specific manufacturing industry of each respondent, and data was collected on the manual vs. automated level of industry-specific tasks and processes. The project worked with the NIST Manufacturing Extension Partnership to disseminate the survey to all 51 MEP Centers across the US. The survey provided relevant insights into small manufacturing across the US and in-depth analysis is ongoing. The full results will help frame the future directions of the project and the MSMR program.

Created May 29, 2015, Updated April 24, 2024