Manufacturing is undergoing a digital transformation. As the foundation of that digital transformation, digital twins enable manufacturers to represent, diagnose, predict, and optimize their operations. However, there are significant challenges for manufacturers, especially small and medium-sized enterprises, in implementing digital twin applications correctly and effectively. While individual companies are starting to deploy and evaluate digital twins, the lack of relevant standards for digital twins is a barrier to broader adoptions. Standards for digital twins, including vocabulary, concept, reference architecture, interoperability, and trustworthiness, are needed. Because digital twins involve highly complex collections of data and functional subsystems, many manufacturers have difficulties knowing where to start when implementing digital twins. Integrating digital twins from different lifecycle stages within the digital thread poses additional challenges.
The Digital Twins for Advanced Manufacturing project provides technical contributions to standards to help manufacturers systematically identify digital twin requirements; formulate the digital twin problem; collect and manage relevant data; develop, validate, and maintain digital twin models; analyze digital twin results; and provide actionable recommendations. The research in this project includes three thrusts. First, the project develops implementation and testing methodologies to help manufacturers create and validate their digital twins. Second, the project performs research contributions to relevant standards development and testing. Third, the project establishes, maintains, and utilizes a Digital Twin Testbed to support the R&D of digital twins within NIST.
Objective
To provide measurement science and open standards to help manufacturers better define, measure, analyze, and control advanced manufacturing systems using digital twins and enable a marketplace for digital twin users and technology providers.
Technical Idea
This Digital Twins for Advanced Manufacturing project aligns with recent advancements in smart sensors, the Industrial Internet of Things, artificial intelligence, and modeling and simulation to realize digital twins of manufacturing systems and processes. Digital twins help observe, diagnose, predict, and optimize the manufacturing system in near real-time and gain the insight needed to decide how to improve overall system performance. A digital twin can help monitor the status, detect anomalies, predict system behaviors, and prescribe future operations. Applications in manufacturing include analyzing machine health, evaluating alternative plans and schedules, setting up maintenance, and performing virtual commissioning.
To address the digital twin complexities, a system of systems approach needs to be taken to integrate and coordinate all appliable subsystems to ensure the value and credibility of digital twins. In addition, to overcome the siloed digital twin challenges, the lifecycle approach also needs to be taken to integrate digital twins for different lifecycle stages. This will provide an integrated view of the physical asset for its digital twin development, avoiding redundancy of information exchange. Combining both systems of systems and lifecycle approaches on digital twins would help establish a marketplace for digital twin users and technology providers and help improve the agility and flexibility of manufacturing systems and the competitiveness of the US manufacturing base.
Research Plan
The Digital Twins for Advanced Manufacturing project seeks to continuously lead and contribute to the development and testing of new digital twin-related standards and establish a digital twin testbed for validating and testing these standards.
Highlights