NSF Workshop: High Priority Research Areas on Integrated Sensor, Control and Platform Modeling for Smart Manufacturing

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1 NSF Workshop: High Priority Research Areas on Integrated Sensor, Control and Platform Modeling for Smart Manufacturing Purpose of the Workshop In October 2014, the President s Council of Advisors on Science and Technology (PCAST) released the report of the Advanced Manufacturing Partnership (AMP) 2.0 Committee entitled, Advancing U.S. Advance Manufacturing. The report motivates next generation advanced manufacturing focusing on enabling innovation, securing the talent pipeline and improving the business climate for U.S. growth and competitiveness. The report further identifies and recommends the alignment of efforts and resources to address next generation advanced manufacturing IT infrastructure technologies critical to U.S. competitiveness, encompassing growth, dynamic performance, energy and material usage, environmental sustainability and zero incidents. Advanced Sensing, Control and Platforms for Manufacturing (ASCPM) and Visualization, Informatics and Digital Manufacturing Technologies (VIDM) were established as comprehensive technology priorities: Advanced, Sensing, Control, and Platforms for Manufacturing (ASCPM): A new generation of network based information technologies has created access to new uses of data and information as new products and manufacturing methods are developed. These technologies make a seamless interaction between cyber and physical assets possible. The research in this space is focused on embedded sensing, measurement and control systems with scalable IT platforms. Visualization, Informatics and Digital Manufacturing Technologies (VIDM): This technology is important as researchers and manufacturers move from digital design, to planning, to purchasing and delivery of raw materials, and finally to the manufacture of customized products. One aspect of the technology deals with supply chain efficiency, and the other aspect deals with the speed with which products are designed, manufactured and brought to market. The research in this space is focused on embedded sensing, measurement and control systems into materials and technologies. When this link is strong, it increases productivity, product and process agility, environmental sustainability, improved energy and raw material usage, better safety performance and improved economics. Smart Manufacturing and Digital Manufacturing are the evolving descriptors for the business and operational application of these advanced cyber technologies in manufacturing. The term Manufacturing is, itself, used to reference anything that is made - energy, chemicals, materials, to artifacts. As defined in the recent AMP 2.0 workgroup reports that supported the PCAST/AMP 2.0 report, technologically, Smart Manufacturing encompasses ASCPM while Digital Manufacturing, Visualization and Informatics are closely aligned, emphasizing life cycle design innovation. While generally still early in adoption, enterprise implementation of ASCPM and Smart Manufacturing has seen somewhat wider adoption in the chemical and process industries while VIDM and Digital Manufacturing have been applied more in the discrete product and assembly industries.

2 When taken together the respective Smart Manufacturing and Digital Manufacturing cyber technologies areas can reflect technology differences because of different drivers but also significant overlaps and complements depending on the layer and method of technology deployment. It is well established that product design, manufacturing process design, product life cycle planning, manufacturability, and the actual manufacture and delivery of a product or material are interlinked regardless of continuous, batch and discrete manufacturing structures. As advanced physical manufacturing technologies progress in areas that include process intensification, micro processes/grids, new energy sources and new materials processes, energy grid, broadband power and 3D printing as well as significantly increased emphasis on product customization and value, dynamic and agile performance and product acceleration, overlaps and complementary areas are amplified in the development and application of models. The proposed workshop will emphasize the real-time sensing, control, platform and human systems models and cyber technologies, i.e. ASCPM, important to the actual manufacturing process and how these are mutually linked with digital design and manufacturing, i.e. VIDM. The workshop will bring together research and industrial experts from a cross section of continuous, batch, discrete and hybrid manufacturing structure interests who are also involved in the application of real-time enterprise smart and digital manufacturing technologies including real-time process management and control, simulation and modeling and data analysis and supply chain management, product and production design and optimization, An important objective is to understand how these interlinked cyber technologies are appropriately balanced and integrated for process and discrete product industry applications with a view toward next generation

3 manufacturing technology trends. Invited speakers with expertise in advanced sensor, control, platform, automation and design systems across manufacturing structures will set the stage for a workshop that will develop priorities about the intersection of these technologies from research, development and delivery viewpoints. The workshop deliverable will be a Roadmap report that (1) delineates and level sets on the technical distinctions and intersections of Smart Control/Automation, Smart Sensing and Smart Manufacturing with Digital Manufacturing when considering their extension across seams, heterogeneous systems and design and manufacturing processes, (2) substantively defines how continuous, batch and discrete modeling requirements and platform infrastructure can be considered together and (3) prioritizes and orders the key areas of research and development when considering delivery integration. The workshop is NOT about the development and application of any one area of cyber or physical technology but it about how these technologies should or could interrelate and integrate. A two-day workshop primarily developed around breakout sessions is planned. Background/Definitions for this workshop The 21st Century Smart Manufacturing Enterprise is data driven, knowledge enabled, and model rich with visibility across the enterprise (internal and external) such that all operating actions are determined and executed proactively by applying the best information and a wide range of performance metrics. The 21 st Century Smart Manufacturing Enterprise also encompasses the sophisticated practice of generating, growing and applying data-driven Manufacturing Intelligence (MI) throughout the lifecycle of design, engineering, planning and production. MI is a deep, comprehensive behavioral understanding of the manufacturing process through extensive enterprise data and modeling, creating a new capacity to observe and take action on integrated patterns of operation through networked data, information, analytics, and metrics. Smart Manufacturing applications use networked, information based technologies to integrate manufacturing intelligence in real-time throughout the enterprise and to use and grow MI to identify untapped opportunities to improve the manufacturing process and product that have not been achievable and/or observable in the current vertically structured enterprise. Smart control and automation is the integration of optimal operational decision making with model predictive control and enterprise modeling and analytics. Short-term scheduling, longer-term planning, economic optimization and incorporation of high fidelity modeling and analytics within the real-time control structures are significant areas of development. Smart control and automation exploit the tight synchronization of all levels of process decision-making, from measurement and actuation, to regulatory and supervisory control, and to scheduling and planning. Multi scale time and spatial modeling, time synchronization, computational mathematics and computational requirements within time constrained by the lowest time constant become key technological areas of opportunity. Smart Manufacturing s kind of Platform informatics focuses on the dynamic orchestration of data, analytics and models, the stitching together or linking of data, applications and services and the interoperable insertion and/or

4 extension of capability with existing manufacturing control and automations systems (which themselves can be smart ), using decision/action workflows across seams. A manufacturing seam is a location where two or more parts of a manufacturing enterprise (including a manufacturing supply chain - processes, systems, organizations, or domains) are joined together. In a manufacturing enterprise, the parts so joined often differ by time horizon, lexicon, technology, culture, business drivers, ability to model, and/or priorities. Manufacturing processes within a manufacturer are themselves internal supply or value chains with this definition of seams regardless of continuous, batch or discrete structures. Smart Manufacturing modeling is also based on describing manufacturing business and operational goals for the enterprise. It establishes the foundation for orchestrating dynamic, adaptive, actionable decision-making through the contextualization and understanding data both within and across seams and in time. This form of modeling makes it possible to constrain data, define time, orchestrate the use of multi-vendor databases, software environments, interface with proprietary platforms, do analyses, run models and solve specific problems. It makes it possible to give data context, make data useful through timely application and manage real-time across a wide range of time scales based on objective. Synchronization refers to any aspect of a task that requires coordination with current physical time data. It is about the fundamental considerations of orchestrating the application of data, analytics and models and building on sensors, controls and automation into actionable processes in heterogeneous environments defined in terms of seams. Smart Manufacturing s potential is centered with technology and practice that builds on sensor technologies, sensor networks, smart control and automation and extends interoperability across widely varying infrastructure and applications in highly heterogeneous environments that are formed by seams. 1. The Workshop Premise The premise for the workshop is that Smart Manufacturing and Digital Manufacturing technologies are mutually supportive with IT capability and shared IT infrastructure providing significant extensions in capability for enterprise manufacturing modeling and operations. Focused first from a manufacturing process perspective, the workshop leads with Smart Manufacturing and considers the interrelation of three cyber technology data and modeling structures that have been articulated. There has been considerable debate about the substantive intersections and distinctions among these but there has not been a concentrated effort to articulate the technical intersections that define mutual support for manufacturing needs: Advanced Sensing, Process Control and Platforms for Manufacturing (ASCPM) encompass machine-to-process-to-plant-to-enterprise-to-supply-chain aspects of sensing, instrumentation, monitoring, control, and optimization as well as hardware and software platforms for industrial control and automation. A new generation of networked based information technologies, data analytics and predictive modeling is providing unprecedented capabilities as well as access to previously unimagined

5 potential uses of data and information not only in the advancement of new physical technologies, materials and products but also the advancement of new, radically better ways of doing manufacturing, processing materials and interoperating with material and energy resources. In focusing on the manufacturing or production stage of the product life cycle, ASCPM technologies are strategically as important for US manufacturing as are the design, new product and digital thread technologies. They offer the technical elements needed in smart manufacturing that is about enabling seamless interoperation of cyber and physical assets to increase productivity, product and process agility, environmental sustainability, energy and raw material usage, and safety performance as well as economic performance. Visualization, Informatics, & Digital Manufacturing (VIDM) is the set of integrated, cross-cutting enterprise-level smart-manufacturing methodologies leveraging information technology systems that will improve US manufacturing competitiveness through end-to-end supply-chain efficiency, unprecedented flexibility, and optimized energy management to achieve error-free manufacturing of customized products and components from digital designs, when needed and where needed. The key drivers of VIDM are: 1) Increased R&D and manufacturing integration with end to end speed and productivity, supply chain efficiency, process yields, energy efficiency, improved sustainability; and 2) Improved process safety, flexibility, agility, configurability, and increased job satisfaction and pride. VIDM is focused into three sub-areas Digital Thread; 2) Integrated Information Systems; and 3) Manufacturing Big Data and Analytics. The Smart Manufacturing Platform is scaled IT infrastructure for orchestrating data management, analytics, modeling and manufacturing interfacing in broadly defined heterogeneous manufacturing environments. A Smart Manufacturing platform is shared infrastructure that facilitates access and actionable enterprise application of real-time networked data and information applied extensively throughout the business and operation of the manufacturing enterprise. From a technical standpoint, a Smart Manufacturing platform facilitates the integration of manufacturing cyber and physical (CPS) technologies to achieve extensive enterprise application of smart technologies and manufacturing intelligence in direct relation with the physical enterprise. An SM Platform must also accommodate the partnerships needed to build infrastructure that no one company can build and still support public and private sector interests. By definition the Smart Manufacturing Platform is infrastructure that is comprehensive, reflects supportive access policies with attention to privacy and security, and addresses the value of accessible software and shared data to industry and proprietary software and data to individual companies. The SM Platform is itself both agile and flexible technology with access to commercial and public resources. It is well established that product and manufacturing process design, product life cycle planning, manufacturability, and the actual manufacture and delivery of a product or material are interlinked. There is also general agreement that they intersect at the point of delivery but are distinctly different with different business objectives and technology

6 needs. The process and discrete industries have focused on these technologies differently and have fundamentally dealt with real-time with different emphases. The potential of mutually supportive intersections is evident with existing and new comprehensive systems modeling methodologies that extend model breadth beyond traditional process simulations or traditional CAD based geometrical designs to include integrated programmatic, system interfaces, multi-dimensional risk, supply chain, environmental, and other market features. In result, the product and system design model can evolve as a full lifecycle construct from conceptual design through manufacturing and operation in a rapid fashion. Models and simulations that have been traditional ways driven give way to systems defined models that are direct reflections of the product or material and the manufacturing process. Planning resources for design models are allocated relative to design, planning, and operational risks. These risks in turn determine the level of model fidelity needed. During manufacturing, data from suppliers as well as the factory floor are integrated with these models of product and process performance to produce significantly better-informed decisions and predictions of operational and business performance. In conducting design and planning processes with this system and risk perspective, the materials and design models are by definition ready for production and can serve a range of manufacturing objectives that include source material or product qualification, in production material and process qualification and a range of enterprise operations and optimizations. As risks are addressed and new bottlenecks and opportunities become apparent, the models can be updated for these new risks and at the same time remain viable for production use. Intersections are also evident with Smart Manufacturing modeling that supports cross seam modeling situations such as human-in-the-loop involvement, enterprise modeling where first principles models are typically unavailable or insufficient, modeling when there is insufficient information or exemplars to build data-based models and modeling when there is a need to mix or merge different kinds and sources of data including numeric and symbolic forms. This form of modeling supports cyber tasks that need to be decomposed when they cannot readily be formulated as time integrated and synchronized or need different resources to achieve and provides a construct for stitching or linking applications and/or data heterogeneous environments. This is contrasted with smart control and automation modeling which is characterized by sensor-to-actuator models and associated physical facilities that function within a common time frame that has been defined a priori. Cyber tasks, physical actions and system resources are well defined in time and tightly integrated such that time and data collection rates are synchronized by the fastest physical operation time constant needs. Unlike Smart Manufacturing and Smart Control and Automation, Digital Manufacturing focuses on the use of integrated, computer-based systems comprised of simulation, threedimensional (3D) visualization, analytics and various collaboration tools to create product and manufacturing process definitions simultaneously. Manufactured artifacts move seamlessly through conception, design modeling, analysis and manufacture. Digital Thread is often associated with Digital Manufacturing referencing the integrated chain of data from conception to manufacture to end product, i.e. the

7 design to manufacturing product lifecycle. Digital Manufacturing objectives are concentrated on new products and shortened design to manufacturing life cycles and new product changeovers. Time is about more design options in reduced time and reduced time to market. Digital Manufacturing addresses energy with new products and materials that lower energy requirements at the user level and are recyclable. Modeling is focused on design, prototyping, and use of CAD, CAE and CAM technologies and facilitation of a digital thread. Platform infrastructure is particularly well suited for supporting and facilitating hybrid systems methodologies among design, control and orchestration in heterogeneous environments. In design, system, material, and product design models validated for manufacturing application can be accommodated as real-time analytic, model, and simulations to provide decision support or insertion in control and automation plans. The Smart Manufacturing (SM) platform supports reuse and ready application of models and analytics at the level of need and readiness. By incorporating these methodologies, design models become a major source of software through the SM Platform Marketplace and the systems risk based methodology for resourcing and building models provides a verification, validation, and certification process for the models. In reverse, the SM Platform similarly supports a mechanism to identify, exercise, and evaluate emerging manufacturing technologies from research organizations, as model elements of larger scale commercial system design emerge. The infrastructure allows for virtual test and analysis in early design phases using model representations in a free market library available to qualified commercial entities. The SM Platform also provides a pathway to think about new scaled modeling methods that include not only a build up to broad enterprise analytics but the ability to model the impacts of scaled infrastructure for the enterprise. 2. Workshop Structure With a focus on manufacturing enterprise performance - productivity, agility, energy, environmental sustainability and safety, three areas of emphasis have been put forward by the organizing committee: (1) Intersections and distinctions of Smart Manufacturing and Digital Manufacturing modeling continuous, batch and discrete manufacturing processes including intersections and distinctions in Smart Control/automation and Smart Manufacturing Platforms (2) Modeling requirements that are scalable and benefit from platform infrastructure and the SM Platform development and investment to leverage and/or accommodate intersections and/or interfaces (3) Priority research, development and delivery areas of integration As necessary, these three areas of emphasis will be modified or expanded based on further input and discussion with the organizing committee.

8 3. Format of the Workshop The two-day workshop will be organized around an initial keynote session on the first day and two keynote panel/breakout session combinations, one each for ASCPM modeling and VIDM modeling, and a summary session about SM Platform integration and scaling of modeling and operational deployment. Keynotes will stimulate thinking within and across the areas of Smart and Manufacturing. Facilitated breakout sessions and combined session reports will address a series of questions to be addressed in each session. During the workshop, participants will examine current and emerging issues regarding the three areas of emphasis expressed above. The organizing committee will invite key contributors to support the planning of the workshop, particularly to identify key questions that for the focused breakout sessions on each of the two days

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