While many U.S. manufacturing operations utilize optimization for individual unit processes, smart manufacturing (SM) systems that integrate manufacturing intelligence in real time across an entire production operation are rare in large companies and virtually nonexistent in smaller organizations. One example of an area where SM systems can be applied is in the management of waste heat. A smart system that not only seeks to recover waste heat, but also to use energy more efficiently, is a more cost-effective approach because energy use, waste energy loss and product output are optimized together. The overall objective of this project was to develop an industry-accepted SM Platform that enables smart systems by accelerating and lowering the cost of development and implementation scales for use in diverse sets of manufacturing industry sectors, manufacturing operations and company sizes. Specifically, the project (1) designed and demonstrated the application of a prototype SM Platform for two diverse commercial test beds, (2) demonstrated at one and estimated for the other test bed the potential reduction in waste heat generation, and (3) worked with leading automation vendors to catalyze low-cost commercialization of the technology developed. The Smart Manufacturing Application and Data Platform (SM Platform) developed during this project is an innovative approach that marries IT virtualization technologies with operational data and modeling system requirements to construct a ready-to-go platform for enabling sensor – data – modeling – actuation systems, driven by real-time plant data and performance metrics. This SM Platform allows manufacturing organizations, regardless of their industry or size, to assemble new operational systems at a much lower cost, optimizing process knowledge and improving energy productivity by integrating with existing process control and automation systems. Also developed during this project is a methodology for developing energy productivity metrics that use real-time process information and provide comparisons for potential, practical, and actual performance, as well as examples and the methodology for provisioning of an Energy Dashboard that displays alternatives to optimize energy use within specific business contexts aimed at helping improve energy productivity and reduce emissions in U.S. manufacturing. The SM Platform designed utilizes current manufacturing, operational and IT industry standards. The platform can be used to expedite sensor – data – model – actuation system development and deployment in a cloud-based environment using a novel end-to-end workflow as a service capability that is already integrated with data connection, ingestion, and contextualization services. In addition, the methodology for development of energy productivity metrics and reusable application templates called Toolkits have been defined so that they can be tailored to a specific business situation. The project’s two test beds, one in a hydrogen production plant and the other in a forging, heat treatment, and machining operation, utilize sensor-driven modeling, measurement, and simulation systems. In these two test beds, the implementation of these as a comprehensive system has made it possible for energy productivity to be managed in real-time throughout the plant and enterprise using an energy dashboard. The development pathway to direct machine actuation was articulated. During the first year, the project team developed the most highly instrumented steam-methane reformer (SMR) in the U.S. using infrared cameras and thermocouples. Real-time data are streamed to high fidelity and reduced-order models for analysis. A high fidelity CFD model, parallelized in the SM Platform cloud environment, was demonstrated for operational use. This is the first real-time operational productivity system of a steam methane reformer using both high fidelity and data-driven models together in a real time situation to balance natural gas flow to the reformer tubes. An infrared camera system was developed and shown to outperform thermocouples in coverage, accuracy, reliability, and cost effectiveness for use in a high temperature, harsh environment. A forging, heat-treating, machining line operation test bed includes new instrumentation for data capture analysis, modeling, and simulation and will be integrated into SM Platform workflows so that business performance can be managed in real-time. Composable metrics were developed using the SM Platform to drive improvements in energy productivity, environmental performance, safety, asset management, costs, and overall operations. A major focus of the SM systems developed was optimization of energy productivity at the enterprise level, which resulted in reductions in waste heat generation for the two project test beds. Reductions in the waste heat generated will result in greater energy savings than will waste heat recovery due to inefficiencies in the heat transfer process. For the SMR test bed an efficiency improvement of 1 – 2% with a corresponding waste heat reduction of 5% were demonstrated. It should be noted that the test bed SMR unit had an efficiency of more than 90% as compared to 70 to 80% efficiencies of most SMR units. So implementation of similar SM systems at typical SMR units could yield more than 15 to 25% waste heat reductions. Similarly the forging, heat-treating, machining line operation test bed is a DOE AMO Platinum Level Superior Energy Performance plant with a much higher than typical overall operating efficiency. For this test bed heat integration using recuperators is estimated to result in fuel savings of 15.9% due to waste heat recovery compared to the base case without recuperators with negligible changes in part exit conditions. This results in an 18.9% energy efficiency improvement. The project team estimates that broad multi-sector adoption of the developed technology could reduce waste heat generation by an amount equivalent to 1.3% of total U.S. energy use. This waste heat reduction is associated with 69 million tons reduction in annual carbon dioxide emissions. Other potential benefits include reducing process times, solid and liquid wastes, environmental impacts, and water use. It is estimated that this investment has an anticipated payback period of less than one year in energy-intensive applications. The SM Platform could also reduce the costs of deploying SM systems by 50% in manufacturing operations. Commercialization of the SM Platform will require significant cross-industry collaboration and technology provider market changes. Steps taken toward commercialization included 1) analyzing savings from deployment of the SM Platform and 2) holding SM Platform workshops, webinars, and conferences with targeted individual industry segments, with a focus on small and medium-sized organizations. An upgraded community website has been established as a portal for accessing and distributing information. Most importantly, the SM platform enabling technology developed during this project and its commercialization will continue, evolve and expand under the Clean Energy Smart Manufacturing Innovation Institute (CESMII).

The industrial scale production of hydrogen gas through steam methane reforming (SMR) process requires an optimum furnace temperature distribution to not only maximize the hydrogen yield but also increase the longevity of the furnace infrastructure which usually operates around 1300 degree Kelvin (K). Kepler workflows are used in temperature homogenization, termed as balancing of this furnace through Reduced Order Model (ROM) based Matlab calculations using the dynamic temperature inputs from an array of infrared sensors. The outputs of the computation are used to regulate the flow rate of fuel gases which in turn optimizes the temperature distribution across the furnace. The input and output values are stored in a data Historian which is a database for real-time data and events. Computations are carried out on an OpenStack based cloud environment running Windows and Linux virtual machines. Additionally, ab initio computational fluid dynamics (CFD) calculation using Ansys Fluent software is performed to update the ROM periodically. ROM calculations complete in few minutes whereas CFD calculations usually take a few hours to complete. The Workflow uses an appropriate combination of the ROM and CFD models. The ROM only workflow currently runs every 30minutes to process the real-time data from the furnace, while the ROM CFD workflow runs on demand. ROM only workflow can also be triggered by an operator of the furnace on demand.

Historic manufacturing enterprises based on vertically optimized companies, practices, market share, and competitiveness are giving way to enterprises that are responsive across an entire value chain to demand dynamic markets and customized product value adds; increased expectations for environmental sustainability, reduced energy usage, and zero incidents; and faster technology and product adoption. Agile innovation and manufacturing combined with radically increased productivity become engines for competitiveness and reinvestment, not simply for decreased cost. A focus on agility, productivity, energy, and environmental sustainability produces opportunities that are far beyond reducing market volatility. Agility directly impacts innovation, time-to-market, and faster, broader exploration of the trade space. These changes, the forces driving them, and new network-based information technologies offering unprecedented insights and analysis are motivating the advent of smart manufacturing and new information technology infrastructure for manufacturing.

21st Century Smart Manufacturing (SM) is manufacturing in which all information is available when it is needed, where it is needed, and in the form it is most useful [1,2] to drive optimal actions and responses. The 21st Century SM 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. SM also encompasses the sophisticated practice of generating and applying data-driven Manufacturing Intelligence throughout the lifecycle of design, engineering, planning and production. Workflow is foundational in orchestrating dynamic, adaptive, actionable decision-making through the contextualization and understanding of data. Pervasive deployment of architecturally consistent workflow applications creates the enterprise environment for manufacturing intelligence. Workflow as a Service (WfaaS) software allows task orchestration and facilitates workflow services and manage environment to integrate interrelated task components. Apps, and toolkits are required to assemble customized SM applications on a common, standards based workflow architecture and deploy on infrastructure that is accessible by small, medium, and large companies.Incorporating dynamic decision-making steps through contextualization of real-time data requires scientific workflow software such as Kepler. By combining workflow, private cloud computing and web services technologies, we built a prototype test bed to test a furnace temperature control model.

Cloud computing services, which allow users to lease time on remote computer systems, must be particularly attractive to smaller engineering organizations that use engineering simulation software. Such organizations have occasional need for substantial computing power but may lack the budget and in-house expertise to purchase and maintain such resources locally. The case study presented in this paper examines the potential benefits and practical challenges that a medium-sized manufacturing firm faced when attempting to leverage computing resources in a cloud computing environment to do model-based simulation. Results show substantial reductions in execution time for the problem of interest, but several socio-technical barriers exist that may hinder more widespread adoption of cloud computing within engineering.

Cloud computing services, which allow users to lease time on remote computer systems, must be particularly attractive to smaller engineering organizations that use engineering simulation software. Such organizations have occasional need for substantial computing power but may lack the budget and in-house expertise to purchase and maintain such resources locally The case study presented in this paper examines the potential benefits and practical challenges that a medium-sized manufacturing firm faced when attempting to leverage computing resources in a cloud computing environment to do model-based simulation. Results show substantial reductions in execution time for the problem of interest, but several socio-technical barriers exist that may hinder more widespread adoption of cloud computing within engineering.

: This project was a study by the Information Sciences Institute (ISI), the on Competitiveness (Council), Pratt & Whitney (P&W), Ohio Supercomputer Center (OSC), and Georgetown University (GU) intended to: 1) identify why companies that do not currently employ HPC for advanced modeling and analysis have failed to adopt this technology when the benefits have been showcased so compellingly, and 2) develop technical and business concepts that could help enable these desktop-only users to employ more advanced computing solutions in their manufacturing design cycles. The products of this study include: A broad industry survey of desktop and entry-level HPC users Council on Competitiveness and USC-ISI Study of Desktop Technical Computing End Users and HPC, an in-depth industry user survey Council on Competitiveness and USC-ISI In-Depth Study of Desktop Technical Computing End Users, a case study of Advanced Computational and Engineering Services (ACES), ACES: Not Ready To Play the HPC Card, a case study of Woodward, a supplier of P&W, Woodward FST: Software Costs and Finding Experts Are Stalling HPC Adoption, and a business and technical concept study Innovation - Solution Portal to Information, Resources, and Experts (I SPIRESM).

Military microsensors are networked distributed embedded systems composed of a processor, a radio, and sensors used for personnel or vehicle detection. They are most often found in minefield replacement and perimeter security applications where size, weight, power, and cost requirements are quite challenging. In this paper, we discuss the different system design approaches used in microsensor systems and introduce a modular, scalable, power-aware microsensor architecture intended span the entire dynamic range required of these systems. We describe a reference implementation of this concept and results from field experiments.

We introduce a power-aware microsensor architecture supporting a wide operational power range (from <1 mW to >10 W). The platform consists of a family of modules that follow a common set of design principles. Each module includes a local power microcontroller, power switches, and isolation switches to enable independent power-down control of modules and module subsystems. Processing resources are scaled appropriately on each module for their role in the collective system. Hard real-time functions are migrated to the sensor and radio modules for improved power efficiency. The optional Linux-based processor module supports high duty cycling and advanced sleep modes. Our reference hardware implementation is described in detail in this paper. Seven different modules have been developed. We utilize an acoustic vehicle tracking application to demonstrate how the architecture operates and report on results from field tests on tracked and wheeled vehicles.

We introduce a truly modular, power-aware, distributed microsensor architecture, capable of seamlessly spanning performance metrics from point-optimized low-power to point-optimized high-power applications. This type of performance is often needed in unattended ground sensor applications such as acoustic sensing and tracking, where long periods of minimal sensing activity are intermixed with short periods of intense sensor processing. The system design and implementation of a microsensor platform based on this architecture are described with experimental results. We show that although building a modular power-aware system requires additional hardware components, it results in system capable of rapid physical hardware and software reconfiguration with module reuse for new applications, while achieving a significant decrease in overall system power.

Military microsensors are networked distributed embedded systems composed of a processor, a radio, and sensors used for personnel or vehicle detection. They are most often found in minefield replacement, force protection, and perimeter security applications where size, weight, power, and cost requirements are equally challenging. In this paper, we discuss the different system design approaches used in microsensor systems, which range in composition from large networks of small nodes to small networks of large nodes. We introduce a modular, scalable, power-aware microsensor architecture intended to support the diversity of applications as well as the entire dynamic range required of these systems. Next, we describe a reference implementation of this concept and experimental results from field tests.

The Reconfigurable Hardware in Orbit (RHinO) project is focused on creating a set of design tools that facilitate and automate design techniques for reconfigurable computing in space, using SRAM-based field-programmable-gate-array (FPGA) technology. These tools leverage an established FPGA design environment and focus primarily on space effects mitigation and power optimization. The project is creating software to automatically test and evaluate the single-event- upsets (SEUs) sensitivities of an FPGA design and insert mitigation techniques. Extensions into the tool suite will also allow evolvable algorithm techniques to reconfigure around single-event-latchup (SEL) events. In the power domain, tools are being created for dynamic power visualization and optimization. Thus, this technology seeks to enable the use of Reconfigurable Hardware in Orbit, via an integrated design tool-suite aiming to reduce risk, cost, and design time of multi- mission reconfigurable space processors using SRAM-based FPGAs.

We introduce a distributed sensor architecture which enables high-performance 32-bit Linux capabilities to be embedded in a sensor which operates at the average power overhead of a small microcontroller. Adapting Linux to this architecture places increased emphasis on the performance of the Linux power-up/shutdown and suspend/resume cycles. Our reference hardware implementation is de- scribed in detail. An acoustic beamforming application demonstrates a 4X power improve- ment over a centralized architecture.

: The goal of PADS was to study power aware management techniques for wireless unattended ground sensor applications to extend their operational lifetime and overall capabilities in this battery-constrained environment. The analysis included embedded systems architectures, signal processing algorithms, simulation and planning tools, and collaborative networking protocols.

We investigated tradeoffs between accuracy and battery-energy longevity of acoustic beamforming on disposable sensor nodes subject to varying key parameters: number of microphones, duration of sampling, number of search angles, and CPU clock. Beyond finding the most energy efficient implementation of the beamforming algorithm at a specified accuracy, we enable application-level selection of accuracy based on the energy required to achieve this accuracy. We measured the energy consumed by the HiDRA node, provided by Rockwell Science Center, employing a 133-MHz StrongARM processor. We compared the accuracy and energy of our time-domain beamformer to a Fourier-domain algorithm provided by the Army Research Laboratory (ARL). With statistically identical accuracy, we measured a 300x improvement in energy efficiency of the CPU relative to this baseline. We present other algorithms under development that combine results from multiple nodes to provide more accurate line-of-bearing estimates despite wind and target elevation.