Projects

Project Call
DMDII projects demonstrate and apply digital manufacturing technologies to increase the competitiveness of American manufacturing.

Enterprise award projects are research, development, and demonstration projects that are selected through a competitive project call and evaluation process. These projects are required to address specific technology focus areas driven by the Institute’s strategic investment plan. A combination of private and federal funding is used to execute these projects, and the teams are generally comprised of both academic and industry members of DMDII.

Partner Innovation Projects (PIPs) are research, development and demonstration projects through DMDII that use private funding to execute the project. PIPs leverage UI LABS fundamental principles of collaboration and fit within DMDII scope and mission.

Project focus areas fall along different parts of the manufacturing process:

Structural Composites – Blade Multidisciplinary Design and Analysis – 14-01-06

Lead Organization: Green Dynamics Inc.

Other Organizations: MetaMorph Inc.; University of Delaware; Vanderbilt University; PTC, Inc.; MSC Software Corporation; Pennsylvania State University, Applied Research Laboratory; SimInsights Inc.

Awarded: July 2015

Description: Partners integrated a suite of analysis tools under a common intuitive user interface specifically focused on wind turbines. Successful implementation of this software approach will reduce barriers to entry for smaller composite material developers and shorten cycle times for current manufacturers all while providing a comprehensive cost and manufacturing model to prevent overruns.

Automated Manufacturability Analysis Software “ANA” – 14-01-07

Lead Organization: Iowa State University

Other Organizations: American Foundry Society, John Deere, The Lucrum Group, MFG.com, North American Die Casting Association, Pennsylvania State University Applied Research Laboratory, Steel Founders’ Society of America, Tech Soft 3D, University of Alabama at Birmingham

Awarded: February 2016

Description: This project will create a manufacturability analysis package that can work on any platform to provide real-time feedback on critical manufacturing issues. The ANA project builds upon work from the AVM project to develop commercially viable software that will provide feedback to designers at the conceptual design phase. The resulting analysis software will enable conceptual designers to receive immediate feedback on their designs early in the manufacturing process, cutting down the often lengthy conceptual design phase of components. The outcomes of this project will enable significant reductions in manufacturing costs, product launch costs, and time to market.

Elastic Cloud-Based Make – 14-01-10

Lead Organization: GE Global Research

Other Organizations: Rolls-Royce, Penn State University Advanced Research Laboratory, Northwestern University, Iowa State, Oregon State, Rochester Institute of Technology, Quad City Manufacturing Lab

Awarded: December 2015

Description: Developing a low-cost way for small and medium-sized manufacturers to access new technology in advanced modeling, simulation, and analysis.

Mind the Gap – Filling the Gap between CAD and CNC with Engineering Services – 14-02-02

Lead Organization: STEP Tools, Inc.

Other Organizations: Penn State ARL, Vanderbilt University

Awarded: February 2015

Description: The Mind the Gap project aims to develop and deliver cloud services to optimize and monitor computer-controlled (CNC) machining. The new services will operate on 3D digital models, which are easier to share and modify than traditional code-based models.

Automated Assembly Planning: From CAD model to Virtual Assembly Process – 14-02-04

Lead Organization: Oregon State University

Other Organizations: ESI North America

Awarded: July 2015

Description: This project aims to develop a computational tool to automatically transform a CAD (Computer-Aided Design) assembly into a set of assembly instructions with as little initial user commitment as possible. Quick predictions of an assembly plan will provide feedback to both design and industrial engineers so that they can see how their decisions impact assembly time and cost. For manufacturing companies that choose to use the developed toolset, it could result in millions of dollars in savings.

Automatic Tolerancing of Mechanical Assemblies from STEP AP203: Completion of Adaptive Vehicle Make Tasks – 14-02-05

Lead Organization: Design Automation Lab, Arizona State University

Awarded: July 2015

Description: This project will investigate algorithms to automate tolerance synthesis of mechanical assemblies. This will include first order Geometric Dimensioning and Tolerancing (GD&T) based only on geometric conditions for assemblability and partial support for second order (based on limited design intent, viz. fits and fasteners). This will result in lower product cost due to better tolerance control, lower scrap rate, and quicker product development time by reducing trial and error in tolerance allocation.

Advanced Variance Analysis & Make – 14-08-01

Lead Organization: Rolls-Royce Corporation

Other Organizations: 3D Systems, Georgia Institute of Technology, Microsoft, National Center for Supercomputing Applications (NCSA), Penn State University Applied Research Laboratory, Southwest Research Institute (SwRI)

Awarded: January 2016

Description: The Advanced Variance Analysis & Make project uses high-performance computing to demonstrate how data coming off of a machine relates to the part made by that machine. It will indicate whether an anomaly in the data is, in fact, related to an anomaly in performance and/or adherence to a design specification for the part.

The analyses will form the basis of a database of production anomalies available through the Digital Manufacturing Commons. Manufacturers will use the resulting data in real time to correct an anomaly if it will affect a part’s performance, or to ignore the data anomaly if there is no evidence of impact on the part’s capabilities, saving time and money during the manufacturing process.

Project Call Summary: Virtually Guided Certification (AA) - 15-07

Demonstrate technologies that use advanced computing, modeling and simulation, and data analysis to significantly reduce the time and cost of certifying a material, manufacturing process, or design.

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Project Call Summary: Completing the Model-Based Definition -15-11

Improve the seamless flow of the Model-Based Definition (MBD) information characterized by a heterogeneous software application environment and sufficient information content to accomplish life cycle activities. Potential life cycle activities for this project call should focus on enabling the digital thread in MBD for detail design, manufacturability and affordability analysis, and design to manufacturing.

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O3 – Operate, Orchestrate, and Originate – 14-06-05

Lead Organization: STEP Tools, Inc.

Other Organizations: ITI-Global (International TechneGroup Incorporated), Mitutoyo America, SystemInsights

Award Date: December 2015

Description: Developing a web environment that will enable users to orchestrate machining and measurement processes from tablets and smart phones. Time and money are wasted when a machining program creates a part that does not conform to the design requirements of the customer. O3 will allow users to check machining programs for conformance from remote locations. When conformance is not met, the service will allow the process to be adjusted using apps. The servers that will be used to host O3 tools will be located at DMDII, and the tools will be available to all manufacturers through the Digital Manufacturing Commons.

Adaptive Machining Tool Kit - 14-07-01

Lead organization: GE Global Research

Other organizations: Western Illinois University, Metrologic Group, University of Wisconsin-Madison, Sivyer Steel, Genesis Systems

Award date: June 2016

Description: Developing a plug-and-play kit that allows CNC machines to adjust to variations in the geometry of manufactured components, reducing the need for manual intervention.

Integrated Manufacturing Variation Management – 14-07-02

Lead Organization: Caterpillar Inc.

Other Organizations: Missouri University of Science and Technology, University of Illinois Urbana-Champaign

Award Date: January 2016

Description: Generating a system by which a manufacturer, in an automated fashion, can compensate for machine tool workspace (machine tool) errors induced due to part, fixture, tooling, or machine tool errors. This should allow for large reductions in setup times for new parts, new fixtures, or parts that see a large variation in the rough condition as delivered to the machining operation while minimizing human interaction in the machining setup process. The innovation over the present state of technology will yield significant improvement in process reliability and efficiency in the entire value stream.

Intelligent Adaptive Machining Fixtures for Castings (IAMFixR) – 14-07-03

Lead Organization: Product Development & Analysis (PDA) LLC

Other Organizations: Arizona State University’s Design Automation Lab, American Foundry Society, Steel Founders’ Society of America

Award Date: August 2015

Description: Developing a set of methods and a software enabler, called “IAMFixR” to reduce the setup time for machining of large castings and fabrications and to virtually eliminate scrapping any of these high value parts. The team aims to incorporate the casting industry standard into a 3D model and use digital technology to capture the changing dimensions of features critical to machining operation for every part produced in a production environment.

FactBoard: Real-Time Data-Driven Visual Decision Support System for the Factory Floor – 15-02-08

Lead Organization: Iowa State University

Other Organizations: Boeing, Factory Right, John Deere, ProPlanner

Awarded: March 2016

Description: This project will develop FactBoard, a shop floor decision support system that will convert thousands of data inputs from logistics and production systems into a collection of visual dashboards—all in real-time. The dashboards will consist of mobile support displays that can be accessed by a variety of users, from plant managers to factory floor foremen. FactBoard will enable manufacturers to make quick adjustments to respond to resource changes, saving them time and money. Many companies are often not in a position to make major upfront investments in shop floor data collection, so FactBoard would ultimately enable manufacturers to use existing data effectively while increasing the quality of information and decision-making as additional data sources become available in the future.

SPEC-OPS: Standards-based Platform for Enterprise Communication enabling Optimal Production and Self-awareness – 15-03-02

Lead Organization: Palo Alto Research Center (PARC)

Other Organizations: ITAMCO, MTConnect Institute, System Insights

Awarded: March 2016

Description: SPEC-OPS aims to provide a first-of-its-kind platform to tightly integrate machine tools and the multiple systems involved in the total manufacturing process, such as manufacturing execution systems, enterprise resource planning systems, dynamic planning and scheduling and process analytics. The capability to integrate multiple systems to transfer data back and forth does not exist today, and SPEC-OPS is the first major effort to address the challenge. The final platform will result in savings in planning, scheduling, execution, and maintenance time for manufacturers.

Manufacturing Work Instructions on Wearable and Mobile Devices with Augmented Reality – 15-04-01

Lead Organization: Rochester Institute of Technology

Other Organizations: Harbec, Optimax, OptiPro

Awarded: March 2016

Description: This project aims to move shop floor instructions off of paper and into interactive, easy-to-use wearable technology. Using augmented reality technology, users will be able to see how to complete a task in real time, with virtual guides showing them what—and what not—to do. At the same time, the system will collect valuable real-time shop floor data that is not typically captured and harness it to improve future manufacturing processes. The system will be based on open standards to achieve another key goal of the project: the creation of technology that is cost-effective for SMEs.

Authoring Augmented Reality Work Instructions by Expert Demonstration – 15-04-03

Lead Organization: Iowa State University

Other Organizations: Boeing, Daqri, Design Mill, John Deere, Purdue University

Awarded: March 2016

Description: This proposal seeks to create work instructions for augmented reality systems by developing the Augmented Reality Expert Demonstration Authoring (AREDA) product. The end product will be a simple and intuitive method to quickly create augmented reality work instructions using 3D cameras with advanced image processing and computer vision algorithms. The cameras will track experts as they manipulate parts to complete a project, capturing minute details and translating them into virtual instructions. AREDA stands to benefit companies like project team partners John Deere and Boeing by making assembly line training more cost-effective through augmented reality.

DMDII-15-05 Systems Design Using the Digital Thread (AME)

We aim to demonstrate technologies that can use data from across the product life cycle and from across the value chain to improve product design and manufacturing.

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DMDII-15-06 Operating System for Cyberphysical Manufacturing (IM)

We aim to develop an operating system for manufacturing that provides both horizontal and vertical resource management from the lowest hardware to the highest enterprise level.

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DMDII-15-14 Hardware/Software Tool Kit for Real-Time Machine and Process Diagnostics, Monitoring, and Self-Correction

We aim to implement machine intelligence into manufacturing machines and to promote the adoption of relevant standards for sensing systems, sensing system communications, and integration into manufacturing machines and systems. The scope includes both new machines with built-in sensors and intelligence, as well as legacy machines and systems that have been retrofitted with sensors and intelligence.

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DMDII-15-15 Agile Manufacturing to Compensate for Production Variability

We aim to demonstrate revolutionary new approaches to measuring the geometry and composition of manufactured components and to use this data in other parts of the digital thread. The ultimate goal is to use digital manufacturing technologies to mitigate production variability, and to reduce the time and cost to develop and maintain manufactured products.

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DMDII-15-16 Open Source Software Applications for Digital Manufacturing

We aim to populate the DMC online community with open data and open software, and to demonstrate use cases that solve real-world problems for manufacturing businesses.

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DMDII-16-01 Analytical Solutions for Life Cycle Feedback

We aim to reduce total life cycle costs of complex systems by collecting data from different parts of the product life cycle, allowing data to flow across the product life cycle and to use this information to improve decision-making.

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DMDII-16-02 Industrial Internet of Things (IIoT) Retrofit Kit for Legacy Manufacturing

We aim to develop an affordable means to retrofit legacy production systems with a wide array of sensors, and to provide the capability to securely and rapidly collect, store, and transmit the data, enabling participation in the digital enterprise.

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DMDII-16-03 Seamless Work Flows from Design to Fabrication

We aim to develop software solutions that significantly reduce the manual input and expertise required to rapidly translate designs into fabricated parts during manufacturing, and thus to fully utilize the capabilities of available machine tools.

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DMDII-16-04 Real-Time Optimization of Factory Operations

We aim to improve factory decision-making by transforming raw data into meaningful and useful information for analysis and decision

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DMDII-16-05 Low-Cost Robotics and Automation

We seek robotics and automation solutions that are affordable, reconfigurable, and adaptable, and that exhibit the precision, repeatability, and productivity of conventional automated solutions. They must also exhibit flexibility at a cost that makes them accessible to small and midsize businesses.

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17-01 & 17-02 Project Call Summaries

The Digital Manufacturing and Design Innovation Institute (DMDII), a UI LABS collaboration, today announces the release of its 2017 Project Call, which requests proposals addressing the DMDII technology thrust: Agile, Resilient Supply Chain. The technical and cost proposals are due on October 2nd by 12pm (CDT).

The title and goal of each request for proposal is listed below.

17-01 Digitally Enabled Supply Chain
Industry has long expressed interest in the use of model-based methods and digital thread capabilities to unlock breakthrough productivity improvements across the supply chain. The automation of critical information flows such as exchange of product and procurement data and reporting of key quality measures, can enhance supply chain visibility and improve performance of the overall enterprise. This request for proposal seeks to create a roadmap and playbooks for implementing digitally-enabled supply chain practices and technologies at both OEMs and SMMs.

DOWNLOAD THE 17-01 RFP

17-02 Advanced Analytics for Supply Chain Operations
Supply chain management efforts in large enterprises quickly become more complex as the number of variables increase, including growing numbers of orders, suppliers, geographic regions, data formats, interface standards, systems, and personnel involved in management. These complexities hinder the organization’s ability to proactively discover and resolve anomalies in the supply chain that impact a product’s cost, schedule and quality. DMDII is interested in projects that will develop a decision support solution which enables real-time or near-real-time identification of potential risk/issues in the supply chain.

DOWNLOAD THE 17-02 RFP

To facilitate the formation of project teams, DMDII will be hosting a virtual project teaming platform. Information on how to access this platform be provided shortly.

DOWNLOAD THE PROPOSAL PREPARATION KIT

If you have any questions, please feel free to reach out to: DMDII@uilabs.org

Supply Chain MBE/TDP Improvement – 14-06-01

Lead Organization: Rolls-Royce Corporation

Other Organizations: 3rd Dimension, Anark Corporation, ITI-Global (International TechneGroup Incorporated), Lockheed Martin, Microsoft, Purdue University

Awarded: January 2016

Description: This project seeks to push Model-Based Enterprise (MBE) technologies forward by using MBE technology to streamline the design stage of the manufacturing process. This involves the use of intelligent 3D models to eliminate the need to translate to different formats, including 2D drawings, when transferring information between original equipment manufacturers (OEMs) and other companies within the supply chain.
Using MBE ties data related to tolerance, life of product, and other product specs to the 3D model during the design phase and allows it to be used for all stages of the process, eliminating issues of unclear or inaccurate drawings. The combination of the 3D model and accompanying information is referred to as the technical data package (TDP). As a framework and best practices for MBE/TDP are standardized and disseminated more widely, they will become more useful and accessible to SMEs. As a greater percentage of the supply chain embraces MBE, the number of potential suppliers to the DoD and other major manufacturers will increase.

DMDII-15-12 Technologies Enabling Supply Chain Visibility

We aim to demonstrate technologies that can provide real-time, dynamic visibility into the status of key information to facilitate efficient response to rapidly changing conditions.

> VIEW PROJECT CALL

Assessing, Remediating and Enhancing DFARS Cybersecurity Compliance in Factory Infrastructure – 15-01-01

Lead Organization: Imprimis, Inc. (i2)

Other Organizations: SPIRE Manufacturing Solutions, Western Cyber Exchange

Awarded: December 2015

Description: This project seeks to create, test and implement a uniform cybersecurity standard for DMDII, with the goal of improving cybersecurity and supply chain security across the manufacturing industry. It reflects feedback from DMDII’s large manufacturing partners who have expressed the need for improved supply chain management. The project will review DoD cybersecurity standards for contractors, assess the costs, capabilities, and training manufacturers need to meet them, then develop a case study to aid manufacturers in meeting them. Ultimately, more manufacturers will be able to become DoD cybersecurity compliant, adding more potential contractors into the DoD and manufacturing pipeline.