Model-Based Enterprise (MBE) requires information about the capability (or provided value) of a manufacturing system to enable systems-focused decision making such that outcomes (or requirements) drive control across the product lifecycle. The Model-Based Manufacturing Capability Definition project provides technical contributions to define, measure, and control these manufacturing capabilities. The challenge of this work is that manufacturing capabilities are inherently dynamic and vary based on the type of manufacturing system to control and the type of decision to be made. The research in this project focuses on the manufacturing capability of a flexible, on-demand, pull-production work cell composed of a machine tool, coordinate measurement machine (CMM), robot, buffer, and material conveyance. Success in this research would improve the agility, flexibility, and competitiveness of the US manufacturing base by allowing decisions to be made based on the measured and predicted capability of manufacturing systems as well as additional part and process information.
Objective - The Model-Based Manufacturing Capability Definition project will develop and deploy advances in standards and measurement science to enable manufacturers to define, measure, and control the capability of smart manufacturing systems.
What is the new technical idea?
Decisions across the product lifecycle are often made based on one viewpoint from one product lifecycle stage, but these decisions often have far-reaching effects on other stages of the product lifecycle. Model-Based Enterprise (MBE) addresses this problem by allowing for decision making with a system focus such that control across the product lifecycle is based on outcomes (i.e., requirements-based production). Enabling this paradigm requires information about the capability (or provided value) of a manufacturing system. MBE aims to match the capability of a manufacturing system to the outputs required of the manufacturing system using operational control.
The challenge in defining, measuring, and controlling the capability of manufacturing systems is that these capabilities are inherently dynamic. There are also potentially a large variety of component types within these systems. Research has occurred previously in defining manufacturing capabilities (e.g., ASME B5.59, OAGi), but there is strong industry need to expand these efforts and describe these capabilities at different levels of abstraction to support the different contexts required for decision making. Such research will also require harmonization across standards in several domains to understand how manufacturing capabilities can be described for different viewpoints. Success in this research will help advance several relevant use cases for smart manufacturing, including:
This project aligns with recent advancements in smart sensors, Industrial Internet of Things (IIoT), artificial intelligence, cyber-physical systems (CPS), and modeling and simulation to realize a “digital twin” of manufacturing systems. Specifically, this research supports the development of standards to synthesize various data flows to create manufacturing capability models that enable real-time data collection, computation, communication, integration, optimization, and control via a digital representation of a manufacturing system. These digital twins would allow different operational controls systems to measure the status of production systems and processes in near real-time and gain the insight needed to decide how to improve overall system performance. For example, a digital twin with modeled manufacturing capability can help evaluate alternative plans and schedules, set up maintenance, optimize operations in near real-time, and prescribe future operations. Such an approach would improve the agility and flexibility of manufacturing systems and the competitiveness of the US manufacturing base.
In addition, the growing use of limited resources and impact of resource extraction on the climate has increased interest in applying the circular economy to improve the efficiency of material use and the resiliency of material supply chains. A challenge, though, is measuring the impact of reusing, remanufacturing, recycling, or upcycling materials on the performance of manufacturing processes and the products that they create. This effort would extend previous work in modeling unit manufacturing processes so that manufacturers can assess trade-offs between different material streams. Outputs from this work would include extensions to existing, broadly accepted manufacturing data standards; datasets, models, and tools that would enable industry to innovate and improve production activities; and best practices and demonstrations to help industry use these standards and technologies.
What is the research plan?
The research plan focuses on defining, measuring, and controlling the manufacturing capability of the components of a flexible, on-demand, pull-production work cell. The work cell of interest includes a machine tool, a CMM, two robots, a buffer, and a material conveyance. This work cell should be able to produce a finished part from stock material and resources by executing control decisions (e.g., process parameters, schedule, routing) based on the measured and predicted manufacturing capability of the work-cell components as well as additional part and process information. Manufacturing capability modeling efforts in this project focus only on those specific components included in the target work-cell use case. To address the target use case, the research plan includes three thrusts of activity:
The research plan extends previous NIST research, such as efforts to develop a standards-based means of communication within a work cell for coordination, orchestration, and control. Future efforts may extend the current research plan to other machine tools, metrology equipment, robots, or material conveyance systems or to larger manufacturing systems and supply chains.