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It is generally observed in inter-organizational communication that present-day data exchange standards are too costly, large, slow to respond to industry demands, and complex to develop and use. These problems in data exchange are felt keenly by the manufacturing industry and its vast supply chains. In addressing these challenges, a successful attempt was recently made that involves two major developments. The first is the Core Components Technical Specification (CCTS), which is an ISO-approved meta-model for data exchange standards. CCTS introduces common data types, uniform structure for data models, and data usage semantics. The second is Score, which is a novel open-source software tool. Score was developed by National Institute of Standards and Technology researchers as a platform to take advantage of the CCTS for data exchange standards development and usage. This paper describes the potential of the CCTS and the Score platform, and describes the current status of the Score platform and Score-enabled industry interactions.

Manufacturing software services provisioned as unbundled “apps” could be significantly more flexible and less expensive to use than the current monolithic manufacturing applications. However, integrating such heterogeneous apps is not a trivial job. National Institute of Standards and Technology (NIST) has initiated a research and development program to advance science and engineering towards the vision of composable Smart Manufacturing systems. In April 2016, NIST hosted a workshop, called Drilling down on Smart Manufacturing – Enabling Composable Apps, to work with industry and academia on needed technical and standards-based solutions. This paper reports on key outcomes of this workshop.

The manufacturing systems of the future will be even more dependent on data than they are today. More and more data and information are being collected and communicated throughout product development lifecycles and across manufacturing value chains. To enable smarter manufacturing operations, new equipment often includes built-in data collection capabilities. Older equipment can be retrofitted inexpensively with sensors to collect a wide variety of data. Many manufacturers are in a quandary as to what to do with increasing quantities of data. Much hype currently surrounds the use of AI to process large data sets, but manufacturers struggle to understand how AI can be applied to improve manufacturing system performance. The gap lies in the lack of good information governance practices for manufacturing. This paper defines information governance in the manufacturing context as the set of principles that allow for consistent, repeatable, and trustworthy processing and use of data. The paper identifies three foundations for good information governance that are needed in the manufacturing environment-data quality, semantic context, and system context-and reviews the surrounding and evolving body of work. The work includes a broad base of standard methods that combines to create reusable information from raw data formats. An example from an additive manufacturing case study is used to show how those detailed specifications create the governance needed to build trust in the systems.

There is a convergence of interests in cyber-physical systems, systems engineering, and manufacturing innovation in the United States. The U.S. National Institute of Standards and Technology (NIST) has undertaken research programs in smart manufacturing systems to address many of the standards and measurement science issues that arise from this convergence. This chapter describes the convergence, the progress made thus far in the NIST programs in smart manufacturing systems, and the challenges that drive further research.

In enterprise integration, a data-exchange specification is an architectural artifact that evolves along with the business. Maintaining a coherent, data-exchange, semantic model is an important, yet nontrivial task. A coherent, semantic model of data — exchange specifications supports reuse, promotes interoperability, and, consequently, reduces integration costs. Components of data-exchange specifications must be consistent and valid in terms of agreed upon standards and guidelines. In this article, we propose an activity model for the creation, test, and maintenance of a shared semantic model that is coherent and supports scalable standards-based enterprise integration. While it frames our research and the development of tools to support those activities for semantic models implemented using XML (Extensible Markup Language) Schema, the activity model presented in this article is independent of the data-exchange technology.

Our high technology society continues to rely more and more upon sophisticated measurements, technical standards, and associated testing activities. This was true for the industrial society of the 20th century and remains true for the information society of the 21st century. Over the last half of the 20th century, information technology (IT) has been a powerful agent of change in almost every sector of the economy. The complexity and rapidly changing nature of IT have presented unique technical challenges to the National Institute of Standards and Technology (NIST) and to the scientific measurement community in developing a sound measurement and testing infrastructure for IT. This measurement and testing infrastructure for the important non-physical and non-chemical properties associated with complex IT systems is still in an early stage of development. This paper explains key terms and concepts of IT metrology, briefly reviews the history of the National Bureau of Standards/National Institute of Standards and Technology (NBS/NIST) in the field of IT, and reviews NIST's current capabilities and work in measurement and testing for IT. It concludes with a look at what is likely to occur in the field of IT over the next ten years and what metrology roles NIST is likely to play.

In enterprise integration, a data exchange specification is an architectural artifact that evolves along with the business. Developing and maintaining a coherent semantic model for data exchange is an important, yet non-trivial, task. A coherent semantic model of data exchange specifications supports reuse, promotes interoperability, and, consequently, reduces integration costs. Components of data exchange specifications must be consistent and valid in terms of agreed upon standards and guidelines. In this paper, we describe an activity model and NIST developed tools for the creation, test, and maintenance of a shared semantic model that is coherent and supports scalable, standards-based enterprise integration. The activity model frames our research and helps define tools to support the development of data exchange specification implemented using XML (Extensible Markup Language) Schema.

STEP (STandard for the Exchange of Product model data, ISO 10303) data for numerical control, or STEP-NC, is intended to provide full product and process data interoperability between computer-aided manufacturing systems and machine tool controllers. Numerous prototype and several commercial implementations of STEP-NC are currently being developed. Testing is an essential step toward ensuring conformance to a specification and achieving interoperability between different implementations. Test methods and valid test cases that address specific operational scenarios are vital to speed the development process. This paper describes proposed test methods and metrics, validation methods for test data, and test case development.