Model-Based Enterprise Summit Report

Lubell, Joshua; Frechette, Simon P.; Lipman, Robert R.; Proctor, Frederick M.; Horst, John A.; Carlisle, Mark; Huang, Paul

doi:10.6028/nist.tn.1820

Cited by 19 publications

(11 citation statements)

References 6 publications

(7 reference statements)

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“…In addition, these standards enhance modeling accuracy and reduce product innovation cycles, thus contributing directly to manufacturing system agility and product quality. Advancements in this area have resulted in a new product development paradigm known as model-based engineering or enterprise, or MBE [51]. Modeling practice standards define digital product-definition data practices for both two-dimensional (2D) drawings and three-dimensional (3D) models.…”

Section: Standard Organizationsmentioning

confidence: 99%

Current Standards Landscape for Smart Manufacturing Systems

Lu¹,

Morris²,

Frechette³

2016

233

144

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Today's manufacturers face ever-increasing demands of variability-greater customization, smaller lot sizes, sudden supply-chain changes and disruptions. Successful manufacturers will have to choose and incorporate technologies that help them quickly adapt to rapid change and to elevate product quality while optimizing use of energy and resources. These technologies form the core of an emerging, information-centric, Smart Manufacturing System that maximizes the flow and re-use of data throughout the enterprise. The ability of disparate systems, however, to exchange, understand, and exploit product, production, and business data rests critically on information standards. This report provides a review of the body of pertinent standards-a standards landscape-upon which future smart manufacturing systems will rely. This landscape comprises integration standards within and across three manufacturing lifecycle dimensions: product, production system, and business. We discuss opportunities and challenges for new standards, and present emerging activities addressing these opportunities. This report will allow manufacturing practitioners to better understand those standards useful to integration of smart manufacturing technologies.

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Section: Standard Organizationsmentioning

confidence: 99%

Current Standards Landscape for Smart Manufacturing Systems

Lu¹,

Morris²,

Frechette³

2016

233

144

View full text Add to dashboard Cite

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“…A webbased registry of suppliers is used to quickly find capable partners in this new environment [2]. The digital representation of precise engineering and manufacturing information is used to specify production requirements for new partners [31,37]. These proposed enhancements to the system may very well make the company more competitive, but before attempting to introduce these changes the company must fully understand the implications.…”

Section: Enhanced Manufacturing Systemmentioning

confidence: 99%

“…Relevant current manufacturing system elements Semantically rich production and process information can help to dynamically discover capable suppliers using the product information of the required production MIL-STD [31] ISO 10303 [21,40] STEP-NC [22] MTConnect [36] Tooling list (Control) Final Bill of Materials (Control)…”

Section: Potential Enablersmentioning

confidence: 99%

Performance Challenges Identification Method for Smart Manufacturing Systems

Jung¹,

Morris²,

Lyons³

et al. 2016

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Smart Manufacturing Systems (SMS) need to be agile to adapt to new situations by using detailed, precise, and appropriate data for intelligent decision-making. The intricacy of the relationship of strategic goals to operational performance across the many levels of a manufacturing system inhibits the realization of SMS. This paper proposes a method for identifying what aspects of a manufacturing system should be addressed to respond to changing strategic goals. The method uses standard modeling techniques in specifying a manufacturing system and the relationship between strategic goals and operational performance metrics. Two existing reference models related to manufacturing operations are represented formally and harmonized to support the proposed method. The method is illustrated for a single scenario using agility as a strategic goal. By replicating the proposed method for other strategic goals and with multiple scenarios, a comprehensive set of performance challenges can be identified.

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“…A methodology for measuring the product and manufacturing information (PMI) modeling capability of computer-aided design (CAD) systems has been developed to measure technology readiness and to track progress as functionality gaps are closed. A measurement methodology will enhance the ability of discrete-part manufacturing companies to implement a model-based enterprise (MBE) [1][2][3][4][5]. The use of a clear capability assessment will accelerate MBE technology development by CAD software vendors.…”

Section: Introductionmentioning

confidence: 99%

Measuring the PMI Modeling Capability in CAD Systems: Report 1 - Combined Test Case Verification

Cheney¹,

Fischer²

2015

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(NIST) has created a test system to measure conformance of Computer-Aided Design (CAD) software to American Society of Mechanical Engineers (ASME) standards for product and manufacturing information (PMI), specifically geometric dimensioning and tolerancing (GD&T) information. The test system has three main components: test cases, test CAD models, and verification and validation test results. The verification and validation results measure PMI implementation capabilities in CAD software and derivative STEP, JT, and 3D PDF files. All of the test cases, test models, test results, and other presentations are available from the project website:

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Model-Based Enterprise Summit Report

Cited by 19 publications

References 6 publications

Current Standards Landscape for Smart Manufacturing Systems

Current Standards Landscape for Smart Manufacturing Systems

Performance Challenges Identification Method for Smart Manufacturing Systems

Measuring the PMI Modeling Capability in CAD Systems: Report 1 - Combined Test Case Verification

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