Smart Manufacturing Systems Design and Analysis Program

Summary

Deliver measurement science, standards and protocols, and tools needed to design and analyze SMS based on a cyber-physical infrastructure for digital and manufacturing systems.

Description

Smart Manufacturing has the potential to fundamentally change how products are designed, manufactured, supplied, used, remanufactured and eventually retired. Current smart manufacturing implementations are mostly at the plant level, and use information technology, sensor networks, computerized controls, and production management software to improve efficiency. The focus of the program is design and analysis of smart manufacturing system (SMS) that will enable industries to implement at the enterprise level, implementing real-time control and data analytics throughout the extended enterprise. Implementing predictive data analytics across the supply network offers new opportunities for process optimization and emphasize prediction in the PDSA (plan, do, study, act) cycle. While industry is making progress in developing and implementing smart manufacturing technologies, the systemic cyber-physical infrastructure and measurement science, standards, and protocols needed to deliver and deploy a large scale smart manufacturing environment remains to be developed. The cost of developing and implementing open software platforms and technologies based on common standards remain high, creating a significant barrier to entry particularly for small- and medium-size enterprises (SMEs). The Smart Manufacturing Systems Design and Analysis program will address these issues and explore potential opportunities for dramatically rethinking the manufacturing system life cycle with a focus on assembly-centric production. For a smart manufacturing system, the program will build the following:

1. Reference architecture and open solution stack to enable and assess the composable SMS
2. Modeling methodology and associated tools to predict, assess, and optimize the operational performance
3. Data analytics and associated methods and tools to enable adaptive system
4. Methods and tools for system performance assurance. The results of this program will apply to broad industry sectors, and will lead to internationally accepted measures and practices.

What is the Problem?

Smart Manufacturing System (SMS) has the potential to fundamentally change how products are designed, manufactured, supplied, used, remanufactured and eventually retired. In the context of manufacturing, "smart" systems are adaptive systems with differing levels of autonomy. Built upon advanced cyber physical systems and data analytics, smart manufacturing system will enable rapid realization of products, dynamic response to changing demand, and real-time performance optimization of production and supply chain networks. Data analytics plays a critical role in enabling smart manufacturing. The recent Office of Science and Technology Policy (OSTP) memo identified big data analytics as one of the key multi-agency priorities. A recent industry report emphasized the application of big data analytics to manage complex manufacturing processes and supply chains. The President made revitalizing manufacturing a top U.S. priority, and launched the Advanced Manufacturing Partnership (AMP). The AMP Steering Committee made high-level recommendations that will set the stage for advanced manufacturing to thrive in the United States. Industry, academia, and government entities have formed the Smart Manufacturing Leadership Coalition (SMLC) to develop the smart manufacturing platform. The SMLC report (viii) outlines the outcomes and goals for SMS and identifies ten priority actions, which include creating modeling and simulation platforms, data collection and management systems, and enterprise wide integration systems for smart manufacturing.While industry is making progress in developing and implementing smart manufacturing technologies, the systemic cyber-physical infrastructure and measurement science, standards and protocols needed to deliver and deploy a large scale smart manufacturing environment remains to be developed. The next generation of smart manufacturing system needs to be developed by composing advanced manufacturing and IT services, yet the composition is not possible due to a lack of a smart manufacturing reference architecture and solution stack. There is a big measurement science gap between the existing models of manufacturing systems and the analytical models that are required to precisely characterize manufacturing systems for performance assurance. Also, the cost of developing and implementing open software platforms and technologies based on common standards remain high creating a significant barrier to entry, particularly for small- and medium-size enterprises (SMEs). The program will address these issues and explore potential opportunities for dramatically rethinking the manufacturing process with a focus on assembly-centric production . The program will build the following for a smart manufacturing system:

1. Reference architecture and open solution stack to enable and assess the design and operation
2. Modeling methodology and associated tools to predict, assess, and optimize the operational performance
3. Data analytics and associated methods and tools to enable adaptive system
4. System performance assurance methods and tools to minimize risk. The results of this program will apply to broad industry sectors, and will lead to internationally accepted measures and practices.

What is the technical idea?

The objective of this program is to deliver measurement science, standards and protocols, and tools needed to design and analysis (predict, assess, optimize, and control the performance) of smart manufacturing system. The new technical idea to achieve this objective is the following: 1) Define what the performance of SMS is and how we measure it in a computational and mathematical sense, 2) Define methods to develop analytical and compositional models of SMS, 3) Define methods to collect, analyze, and utilize data required to predict, assess, optimize, and control the performance of SMS, and 4) Define a reference architecture and methodology to implement methods and tools to predict, assess, optimize, and control the performance of SMS from a system engineering perspective.

The major projects of the Smart Manufacturing Systems Design and Analysis program are listed below.

1. Build reference architecture and open solution stack to enable and assess composable smart manufacturing systems. The new technical idea is to develop an effective systems engineering methodology for developing, maintaining, and using the reference architecture and solution stack. Smart Manufacturing Reference Architecture will be an authoritative source of information that guides and constrains the instantiations of multiple architectures and solutions.
2. Build modeling methodology and associated tools to predict, assess, and optimize the operational performance of smart manufacturing systems, including uncertainty propagation and compositional modeling. The new technical idea is to bring together the conceptual modeling, information modeling, and behavior modeling paradigms to enable analytical frameworks that use domain-specific and mathematical modeling tailored to application in smart manufacturing.
3. Build data analytics and associated methods and tools to enable and assess diagnostics and prognostics for smart manufacturing systems in real time. The new technical idea is to develop open protocols, analytical models, implementation frameworks and standards to enable the application of data analytics to turn data into insights and actions. Towards this end, this project will deliver an analytical framework for real-time (xxi) improvement of dynamic production system efficiency.
4. Build system performance assurance methods and tools for smart manufacturing systems (including, e.g., automated testing). The new technical idea is to develop measures of performance, such as metrics for energy and material for sustainability, in addition to more traditional measures including cost, quality, reliability, and productivity as the basis for measuring the performance of individual processes and system. The results of these projects will enable the convergence of intelligent devices, intelligent networks and intelligent decisions. The results will further enable information integration to support agile supply networks, real-time monitoring and controlling of manufacturing plants and assets, and rapid customization and realization of products.

What is the research plan?

The research plan is organized into architecture and modeling thrust and predictive analytics and performance assurance thrust. The detailed plan is described in the individual project plans.

The architecture and modeling thrust has two major projects:

1. Reference Architecture for Smart Manufacturing Systems: The research plan is geared to achieve necessary contributions to develop successful reference architecture and solution stack. The architecture framework will be developed to provide conceptual guidelines for developing system architectures, tools, and methodologies for designing the architectures, general principles, knowledge base and models of the smart manufacturing system, and experimental methods for architecture assessment on increasingly complex collections of smart manufacturing systems descriptions. The research emphasis will be to build architecture framework for SMS.
2. Modeling Methodology for Smart Manufacturing Systems: We have two threads of existing work to draw upon. Based on the previous work on the Sustainable Process Analytics Formalism in the Sustainable Manufacturing program and Decision Guidance Modeling Language (DGML), we will develop a general architecture for analytical frameworks. We will develop formal methods and tools for dynamic composition of manufacturing components (e.g., resources, equipment, hardware, and software) models to facilitate smart manufacturing system prediction and performance assurance. The formal models of components of SMS will be described at different levels of abstraction, namely, informational, mathematical, and behavioral. In order to predict and assure system level performance for different manufacturing scenarios, we need these models to be dynamically composable in an analytic environment, representing the larger production system. Based on international standards, we will develop methods of verification and validation and requirements traceability for SMS. The first task is to define requirements for analytical frameworks for SMS. The project will develop an architectural description of analytical frameworks, based on the requirements analysis and the previous work in the sustainable manufacturing program.
3. The predictive analytics and performance assurance thrust has two major projects: Real-Time Data Analytics for Smart Manufacturing Systems: To understand the predictive analytics workflow, this project will focus on the following research areas:
   1. Requirements analysis and the state of the art in manufacturing data management (volume, velocity, variety, veracity),
   2. Analysis of predictive modeling techniques, rules for analytical model composition, metrics, and optimization methods and tools,
   3. Identification of standards to represent a myriad of predictive modeling techniques, such as PMML (xxiv), to fully support data analytics for manufacturing applications, and
   4. Requirements analysis of interactive data visualization for analytics. Data visualization is an art and a science unto itself, and there are many techniques that can be used to get insights from the data.
4. Performance Assurance for Smart Manufacturing Systems: The project will address the problem of performance assurance from multiple directions: 1) Identify the unique characteristics for performance issues and metrics for SMS, 2) Develop methods for interpreting metrics in terms of the performance objectives for SMS, 3) Identify approaches to address performance deviation, and 4) Demonstrate the use of performance measures in a system context. The metrics for performance assurance will be approached separately for each of the three performance objectives, namely sustainability, agility, and asset utilization. Results from this project will contribute to standards development activities within ASTM E60.13.

Major Accomplishments

Some recent accomplishments for the New Technology Adoption and Industry Operations Analysis for Smart Manufacturing include:

• NIST Special Publication 1142: The Current State and Recent Trends of the US Manufacturing Industry.
• NIST Special Publication 1176: Costs and Cost Effectiveness of Additive Manufacturing
• NIST Technical Note 1810: The US Manufacturing Value Chain: An International Perspective

Some recent accomplishments for the Real-Time Data Analytics for Smart Manufacturing Systems include:

• Gaussian process regression extension to the Data Mining Group Predictive Model Markup Language (PMML) standard. The extension provides a standard mechanism to represent confidence bounds for predictive estimations in system modeling.
• Journal article describing the data generator using virtual and domain specific models of manufacturing machines.
• Journal report on data acquisition, data analytics, optimization, and UQ using existing software.
• Journal report on Uncertainty Quantification (UQ) and sensitivity analyses for prediction model for time varying data.