Projects

MxD projects demonstrate and apply digital manufacturing technologies to increase the competitiveness of American manufacturing.


PROJECT CALL — DIGITAL TWINS FOR PROCESS MANUFACTURING

Proper mastering of the digital twin is central to achieving the promise of industrial digital transformation. This project will focus on the first challenge to achieving digital twin benefits: development and execution of a plan to collect, aggregate, and analyze all the sensor data necessary to build a digital twin of a product, process, or equipment. This RFP seeks to develop and demonstrate a software tool that will integrate and make viewable data streams from all IoT systems to enable field operators, engineers, factory managers, and other key manufacturing roles to gain data-driven insights for increased operational awareness. To accomplish these digital twin research objectives, this will project will be divided into two parts:

Part A: Proof-of-concept for ‘mobile worker’ software tool and supporting infrastructure through implementation on a testbed relevant to process manufacturing.

Part B: De-risking the development of an open architecture testbed through the creation of a framework that allows for “plug-and-play” interoperability with various vendor technologies for sensing, data aggregation, analytics, and control.

Offerors have the option to submit proposals for just Part A or Part B independently or may submit proposals for both Part A and Part B. Both Part A and Part B shall be launched and executed concurrently.

RFP responses are due on or before 5:00 p.m. Central Time, September 13, 2019.

Proposals may be submitted by individual organizations or teams, and MxD membership is not required for submission but will be required prior to project award. To facilitate project teaming, MxD will compile and disseminate contact information from parties interested in teaming during the first week of the proposal period.

If you are interested in submitting your contact info to this distributed list, please email projects@mxdusa.org by 5:00 p.m. Central Time, July 22, 2019.

For questions or more information, contact projects@mxdusa.org.

DOWNLOAD THE RFP


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 MxD.

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

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

PROJECT CALL - HIGH-VOLUME, LOW-COST ITEM SERIALIZATION

Affordable and practical item-level traceability has been urgently needed in consumer packaged goods and other high-volume manufacturing for improved quality assurance, inventory management, compliance, counterfeit mitigation, and real-time operational excellence. Although there are a number of existing solutions for item-level traceability, none are practical for high-volume, low-cost consumer packaged goods (zero cost, zero product real estate, serialize at rate of production, etc.). This Request for Proposal seeks solutions to address the gaps in existing serialization technology through research and development of “digital fingerprinting” technologies.

RFP responses are due on or before 5:00 p.m. Central Time, July 12, 2019.

Proposals may be submitted by individual organizations or teams, and MxD membership is not required for submission but will be required prior to project award. To facilitate project teaming, MxD will compile and disseminate contact information from parties interested in teaming during the first week of the proposal period.

If you are interested in submitting your contact info to this distributed list, please email projects@mxdusa.org by 5:00 p.m. Central Time, June 19, 2019.

For questions or more information, contact projects@mxdusa.org.

 Technical Summary & Program Overview

DOWNLOAD THE RFP

REQUEST FOR INFORMATION— HIGH-VOLUME, LOW-COST ITEM SERIALIZATION

Affordable and practical item-level traceability has been urgently needed in consumer packaged goods and other high-volume manufacturing for improved quality assurance, inventory management, compliance, counterfeit mitigation, and real-time operational excellence. Although there are a number of existing solutions for item-level traceability, none are practical for high-volume, low-cost consumer packaged goods (zero cost, zero product real estate, serialize at rate of production, etc.).  MxD is seeking information on existing technologies and/or research and development work being done in this area. Any individual from industry or academia may submit a response; early responses are encouraged.

RFI responses are due on or before April 3, 2019, 5:00pm CT.

MxD will utilize information received to scope projects in this subject area. Early submissions are encouraged to accelerate project scope and will be discussed with MxD membership during the Agile Tech Team Quarterly Meeting on March 29th.  Members interested in responding to the RFI are encouraged to register and participate in the March 29th meeting.

For questions or more information, contact Katie Tillery-Merk at katie.tillery-merk@uilabs.org

DOWNLOAD THE RFI

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.

Enhancing the Model-Based Definition with Manufacturing Info – 15-11-05

Lead Organization: Siemens Corporate Technology

Other Organizations: Georgia Tech, Northrop Grumman, MetaMorph

Descriptions: Siemens is leading the charge on this complex project that aims to advance state-of-the-art digital thread capabilities through model-based definition development. The project team is linking manufacturing process information including "as-built" geometry into a linked data structure that will help designers make better decisions based on producibility feedback.

Tolerance Analysis Tools, Techniques and MBE Training – 15-11-01

Lead Organization: Advanced Engineering Solutions

Other Organizations: Sigmetrix

Description: This digital-design-centric project resulted in two outcomes that both aim to improve the methods by which design engineers can establish tolerance values for parts and assemblies. One is a unique workflow that exploits existing tools within native CAD environments to perform tolerance analyses on flexible parts and assemblies. The other, developed by engineering software veterans at Sigmetrix, is a standalone, CAD-agnostic tool that performs quick and accurate 1D tolerance analyses.

Virtually Guided Certification of CNC Machine Tool via Virtual Twin – 15-07-01

Lead Organization: Boeing

Other Organizations: MS&T

Description: For large OEMs that rely on high-value machined parts with long lead times, it is vital that their chosen suppliers are capable of machining these components successfully to avoid any detrimental slips in schedule. This project has developed a virtual twin tool that enables OEMs to characterize their suppliers CNC' machines and confirm that they have the capability to produce their parts to the design specifications. This foresight allows OEMs to source their supply chains with confidence and build a trusted network of suppliers with certified capabilities.

Lower Life Cycle Costs, Improve Design & Performance Robustness Through the Digital Thread – 15-05-03

Lead Organization: Rolls-Royce

Other Organizations: Boeing, Deere and Company, Georgia Tech, Sentient Science, Pennsylvania State University

Description: Current tools and methods used in the design of products and systems have very limited capacity to support automated knowledge sharing for decision making in life cycle considerations. Additionally, the data produced by existing information systems, such as computer aided design (CAD) and design for manufacturability (DFM) systems, are already in electronic format, but all the information required to make a decision may not be available, may lack consistency, and may not be expressed in a general format. This program will develop a generic basic framework of a feedback network enabling information integration across the product life cycle to enable the rapid use of actionable knowledge to improve and optimize design.

Smart PCB Digital Factory – 15-05-06

Lead Organization: Lockheed Martin Corp.

Other Organizations: Fujitsu Network Communications, Inc., IPC International, Inc. Rochester Institute of Technology, Sanmina Corp., Siemens

Description: The software used to design printed circuit boards (PCBs) continues to evolve and PCB manufacturing processes continue to advance, but the transfer of data between PCB designers and manufacturers has changed very little in the last 15 years. PCB build data today is comprised of a combination of electronic and paper documents spread across many files and multiple formats. This project will demonstrate that a single data file, IPC-2581B, can be successfully used throughout the design, fabrication, assembly and testing. When implemented, the digital bi-directional data transfer between PCB design and manufacturing vendors will drastically reduce the amount of time and effort required to manufacture printed circuit boards. Learn more about the project and its impact here.

Predictive Modeling for Digitally-Enabled, Multi-Criteria Decision Making in Innovative Product Design and Analysis with Total Lifecycle Sustainability – 15-05-08

Lead Organization: University of Kentucky

Other Organizations: Lexmark International, Siemens

Description: Data needed for rapid, seamless product design is still being stored in disparate, sometimes incompatible systems. Poor interoperability and the lack of a digital thread linking systems impedes the use of powerful tools trade-off analysis tools. This project will develop a set of predictive computational modeling tools for total lifecycle product design optimization, simulation and uncertainty and risk analysis.

VRWP: Virtually Guided RSW Weldability Prediction – 15-07-04

Lead Organization: Wayne State University

Other Organizations: Ford

Description: Product designers rely on material suppliers and iterative testing by service companies to determine weldability of materials for resistance spot welding. This project will develop a weldability prediction tool and historical database to aid designers in choosing materials. The tool will improve design and engineering efficiency of new spot welded components and assemblies as well as reduce the costs incurred to conduct iterative testing.

From Art to Part – 15-07-05

Lead Organization: GE Global Research

Other Organizations: Techsolve, University of Cincinnati, University of Illinois

Description: 3D printing of alloy structures enables entirely new classes of parts.  However, design-validate iterations can take at least 10 weeks for a complex part. DMDII is bringing 3D manufacturing left into the design phase: knowledge guided pre-processing, modeling distortion effects, and other factors will reduce the design cycle by up to 80%.

Capturing Product Behavioral and Contextual Characteristics through a Model-Based Feature Information Network – 15-11-08

Lead Organization: Lockheed Martin

Other Organizations: Purdue University, Rolls-Royce, Siemens, MSC Software, Capvidia, PTC, and Materials Database Management.

Description: This project will develop a framework for collecting part manufacturing and lifecycle data from disparate document formats into a single digital file that is transferable between suppliers and OEMs. Currently, build and design data is created by companies in supply chains in a range of incompatible formats, making information difficult to communicate. Manufacturers often need to re-enter design data, making manufacturing and maintenance error-prone and labor-intensive. The team estimates that suppliers that automate their processes using the new compiled data format can reduce the engineering resources needed for for these tasks by as much as 95 %.

Analytical Solutions for Production Variability in Complex Assemblies – 16-01-02

Lead Organization: The Ohio State University

Other Organizations: Rolls-Royce, Siemens, Arizona State University

Description: Due to inherent variability in part manufacturing, sub-assemblies can show significant variations in assembled dimensions which can result in increased cost of manufacture due to re-work, and/or significant variations in the product performance. The prediction and control of this variability in complex assemblies requires analytical methods and tools for identification of critical features, extraction of stacks contributing to variability, tolerance synthesis, analysis and optimization. This team of designers and manufacturing engineers, software developers and academic experts from will apply emerging revolutionary methods to develop, test, validate and demonstrate methods and tools that will enable mitigation of the consequences of manufacturing variability on performance and cost.

PROJECT CALL - HIGH-VOLUME, LOW-COST ITEM SERIALIZATION

Affordable and practical item-level traceability has been urgently needed in consumer packaged goods and other high-volume manufacturing for improved quality assurance, inventory management, compliance, counterfeit mitigation, and real-time operational excellence. Although there are a number of existing solutions for item-level traceability, none are practical for high-volume, low-cost consumer packaged goods (zero cost, zero product real estate, serialize at rate of production, etc.). This Request for Proposal seeks solutions to address the gaps in existing serialization technology through research and development of “digital fingerprinting” technologies.

RFP responses are due on or before 5:00 p.m. Central Time, July 12, 2019.

Proposals may be submitted by individual organizations or teams, and MxD membership is not required for submission but will be required prior to project award. To facilitate project teaming, MxD will compile and disseminate contact information from parties interested in teaming during the first week of the proposal period.

If you are interested in submitting your contact info to this distributed list, please email projects@mxdusa.org by 5:00 p.m. Central Time, June 19, 2019.

For questions or more information, contact projects@mxdusa.org.

 Technical Summary & Program Overview

DOWNLOAD THE RFP

REQUEST FOR INFORMATION— HIGH-VOLUME, LOW-COST ITEM SERIALIZATION

Affordable and practical item-level traceability has been urgently needed in consumer packaged goods and other high-volume manufacturing for improved quality assurance, inventory management, compliance, counterfeit mitigation, and real-time operational excellence. Although there are a number of existing solutions for item-level traceability, none are practical for high-volume, low-cost consumer packaged goods (zero cost, zero product real estate, serialize at rate of production, etc.).  MxD is seeking information on existing technologies and/or research and development work being done in this area. Any individual from industry or academia may submit a response; early responses are encouraged.

RFI responses are due on or before April 3, 2019, 5:00pm CT.

MxD will utilize information received to scope projects in this subject area. Early submissions are encouraged to accelerate project scope and will be discussed with MxD membership during the Agile Tech Team Quarterly Meeting on March 29th.  Members interested in responding to the RFI are encouraged to register and participate in the March 29th meeting.

For questions or more information, contact Katie Tillery-Merk at katie.tillery-merk@uilabs.org

DOWNLOAD THE RFI

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.

Rapid Process Certification & Verification for High-value, Low-volume parts – 15-07-07

Lead Organization: Northwestern University

Other Organizations: Siemens, Northern Illinois University, QuesTek, PDA

Description: Aerospace companies rely on additive manufacturing to create components that make up complex systems like jet engines. Northwestern University is leading an effort to develop an integrated toolkit that predicts the print results pertaining to characteristics such as deformation and structural performance, and subsequently certifies the printing processes for high-value, low-volume parts. The outcome of this project will be a plugin for the popular Siemens NX CAD program.

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.

Virtually Guided Certification of Die Cast Manufacturing Processes – 15-07-06

Lead Organization: University of Illinois

Other Organizations: NADCA, Chicago White Metal Casting, RCM Industries, Twin City Die Castings Co., Visi-Trak Worldwide, Mercury Machine Company

Description: This die cast simulation project is leveraging University of Illinois'  materials science expertise to build out a new generation of material flow, cooling and structural performance simulation tools for die-cast parts. The integrated simulation environment is being validated on real-world use-cases that the North American Die Casting Association (NADCA) is helping to provide through their extensive industry partnerships.

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.

New Digital Tricks for Trusted Friends – 16-02-07

Lead Organization: Georgia Institute of Technology

Other Organizations: Steelcase Inc.

Description: Smaller manufacturing companies have begun looking for ways to incorporate digitization to improve productivity and quality at their facilities. Many of these companies rely on legacy machines for the entirety of their operations and need an economical way to harness the associated data to drive better business decisions. This project has developed sensor kit solutions that can be adapted for a variety of applications while maintaining a price point of less than $250 even if bought in low quantities. These kits have been installed at Steelcase Inc. to help track parts, tool usage, motor health and other use cases. The data from these kits is already providing valuable insights for the Steelcase team.

Retrofit Kit for Legacy Machine Sensing in Secure Data Environments – 16-02-03

Lead Organization: Georgia Institute of Technology

Other Organizations: ITAMCO, Mazak Corporation, Caterpillar Inc.

Description: Legacy equipment platforms represent a critical element of the manufacturing ecosystem, especially for small to medium manufacturing organizations. As these organizations begin their digital journey, there is a clear need for cost-effective technology that allows them to harness the data on Legacy machines. This project has developed a sensor retrofit kit that utilizes flexible off-the-shelf components, communicates data using standard manufacturing protocols and is secured using Mazak’s hardened SmartBox technology. the kits are currently being installed at Caterpillar for a variety of production use cases.

Robust Adaptive Turbine Airfoil Manufacturing in a Production Environment via the Digital Thread – 15-15-02

Lead Organization: GE Global Research

Other Participants: Arconic Power and Propulsion, ITI, University of Wisconsin

Description: Jet engine manufacturers are dealing with more demanding production requirements as they strive to improve overall engine fuel efficiency. In the case of turbine airfoil cooling holes, they are seeing increased manufacturing losses because they must meet these complex requirements while dealing with substantial variations in turbine blade castings. To resolve this problem, this project is developing an automated, adaptive manufacturing process that connects the material vendor to the jet engine OEM via the digital thread. In this process, geometric data for the castings is sent directly to the OEM, where an algorithm has been developed to classify the parts and then dictate the respective manufacturing parameters that are key to adapting the machining process for a successful build.

Bottom-Up Plug-and-Play Hardware/Software Toolkit for Monitoring, Diagnostics and Self-Correction – 15-14-09

Lead Organization: NCMS

Other Participants: Perisense, Georgia Tech, Ace Clearwater

Description: A large fraction of manufacturers rely on unconnected, legacy machines to drive their production. Manufacturers are looking for new ways to monitor their operations but replacing these machines with their newer, connected counterparts is out of the question due to financial constraints. This project is developing sensor retrofit kits and a software application that will allow manufacturers to scale up their legacy assets to a cloud infrastructure and visualize their operations using real-time data. The team is preparing a commercial offering that has been validated at Ace Clearwater and additional regional SMMs.

OSCM: An Operating System for Cyberphysical Manufacturing – 15-06-01

Lead Organization: University of Illinois-Urbana Champaign

Other Organizations: Caterpillar Inc., Microlution Inc., Northwestern University

Description: A network of manufacturing resources is envisioned to provide seamless integration, access and visibility into geographically distributed organizations. Such a framework must safely and securely provide capabilities/capacity information and current availability of manufacturing resources for production scheduling. This project will enable a network of machines for users to easily and efficiently access machining capacity and capabilities to monitor and control emergent behavior in the overall network.

Non-Invasive Computer Vision Toolkit for Legacy Machines using MTConnect – 16-02-06

Lead Organization: University of Cincinnati

Other Organizations: Raytheon, Faurecia, ITI, and TechSolve.

Description: Manufacturers seeking to digitize their operations often need to incorporate data from expensive legacy manufacturing equipment in new, innovative processes without disrupting production, creating failure points, or voiding equipment warranties. This project, led by the University of Cincinnati, is developing an open source framework for computer vision-enabled cameras to recognize and read a variety of legacy digital displays and analog dials in order to produce information in the increasingly accepted MTConnect format. The final software and hardware toolkit is projected to cost less than $1,000 per machine, enabling even the smallest manufacturing company to update their processes without replacing costly legacy equipment.

Automated CNC Process Planning Software: CNC-RP – 16-03-01

Lead Organization: Iowa State University

Other Organizations: Deere and Company

Description: The manufacturing planning process required to produce a physical part can be tedious and time consuming. This drives enormous costs for short run production and prototyping. This project will develop a software module that will dramatically decrease the amount of time required to complete the manufacturing planning process. The benefits of this solution lie in a reduction of pre‐process engineering time to provide direct savings in labor and sunk costs of elaborate fixture setups and will also drive down time to market, and/or allow much faster iteration on new designs.

Achieving Smart Factory through Predictive Dynamic Scheduling – 16-04-01

Lead Organization: Forcam Inc.

Other Organizations: Predictronics, Northeastern University, and Lockheed Martin.

Description: Currently machine sensor systems monitoring the actual condition of equipment do not connect with manufacturing execution systems (MES) to schedule preventative maintenance. Forcam Inc. and its project team are developing a solution that allows users to schedule maintenance within their existing production scheduling systems, optimizing overall equipment effectiveness by decreasing machine downtime and ensuring that machines are only being serviced when necessary. The team is prototyping the integration of their software within an OEM's MES and anticipates decreasing their machine downtime by 10%.

Integrated Scheduling and Control for Real-Time Optimization of Factory Operations – 16-04-02

Lead Organization: The Dow Chemical Company

Other Organizations: Siemens, University of Michigan, Kent Displays, University of Wisconsin

Description: Two of the concepts that underpin the notion of digital manufacturing are the automation and optimization of operational decisions and the integration of decisions that lie in different functional and temporal domains. This project will address these concepts directly by integrating supply chain decisions embodied in batch production planning and scheduling with lower level process control and automation decisions. The solution will generate work flow automation techniques and generate performance models to optimize production and guide human interventions in factory operations.

REQUEST FOR INFORMATION— HIGH-VOLUME, LOW-COST ITEM SERIALIZATION

Affordable and practical item-level traceability has been urgently needed in consumer packaged goods and other high-volume manufacturing for improved quality assurance, inventory management, compliance, counterfeit mitigation, and real-time operational excellence. Although there are a number of existing solutions for item-level traceability, none are practical for high-volume, low-cost consumer packaged goods (zero cost, zero product real estate, serialize at rate of production, etc.).  MxD is seeking information on existing technologies and/or research and development work being done in this area. Any individual from industry or academia may submit a response; early responses are encouraged.

RFI responses are due on or before April 3, 2019, 5:00pm CT.

MxD will utilize information received to scope projects in this subject area. Early submissions are encouraged to accelerate project scope and will be discussed with MxD membership during the Agile Tech Team Quarterly Meeting on March 29th.  Members interested in responding to the RFI are encouraged to register and participate in the March 29th meeting.

For questions or more information, contact Katie Tillery-Merk at katie.tillery-merk@uilabs.org

DOWNLOAD THE RFI

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.

Supply Chain Visibility - Capability Modeling for the Digital Factory – 15-12-05

Lead Organization: UIUC

Other Organizations: Texas State University, The Innovation Machine, ITAMCO

Description: Existing e-sourcing solutions for supply chain have several deficiencies including systemic rigidity, lack of visibility into the information modeling approach, and a high-level dependency on manual interaction in supplier discovery. The objective of this project is to significantly increase the intelligence and effectiveness of various supply chain decisions by providing dynamic insight into the technological capabilities, capacities, and quality history of prospective suppliers. This will be accomplished through introducing a formal ontology for manufacturing capability and developing a cloud-based, service-oriented platform for supply chain configuration. Supply chain managers can use this platform to build out their requirements, match with the suppliers that can satisfy those requirements and compare the resulting options to identify the best supply chain.

An Analytics Based Supply Chain Risk and Event Management Decisions Support Framework – 17-02-01

Lead Organization: The Dow Chemical Company

Other Organizations: ITAMCO, Microsoft, IUPUI, RIT

Description: Limitations in identifying, prioritizing, communicating and mitigating against supply chain disruptions can be
costly to Businesses. This team will develop an integrated decision support framework that utilized Advanced Analytics and Blockchain data representation, embedded functionality across the key modules of the framework will advance the predictive power of supply chain event management solutions and drive proactive risk management. The framework will outbound logistics performance by predicting disruption events, as well as associated risk levels in a customized manner.

Enabling Real-Time Supply Chain Visibility Through Predictive Analytics – 15-12-02

Lead Organization: University of Washington

Other Organizations: GE Global Research, ITAMCO, University of Illinois Urbana-Champaign

Description: This project will allow both buyers and suppliers to reduce their overall supply chain costs and come up with a more resilient manufacturing strategy through enhanced real-time visibility into part deliveries and demands by creating a model that will ensure that the part availability predictions are always conservative, redundant predictive factors are removed, greater weightage is given to recent purchase orders during model development, and confidence intervals are provided on the prediction accuracy based on the quality of the available data. Additionally, the project will provide insights on how to best visualize complex supply chain data given different stakeholders’ tasks and goals

Digitally Enabling the Supply Chain: Integrating Existing Tools and Capabilities to Guide Application – 17-01-01

Lead Organization: Auburn University

Other Organizations: Raytheon, Rockwell Collins, ITI, MBD360, Rolls-Royce, Lucrum Group

Description: Reduction in lead time and error rate have long been goals of manufacturers. A contributor to both long lead times and increasing error in processes are interoperability problems between systems in the design/manufacture/deliver/sustain product realization process and is a significant source of pain, difficulty and increased costs. Information flow in the supply chain adds an additional level of complexity placed upon the product realization system. This project delivers combat these inefficiencies by developing a set of playbooks designed to accelerate the depth and breadth of adoption for digital supply chain practices and technologies. The resulting benefits of reduced cost and time along with greater innovation better position the U.S. industrial base to compete in the global market.

REQUEST FOR INFORMATION— HIGH-VOLUME, LOW-COST ITEM SERIALIZATION

Affordable and practical item-level traceability has been urgently needed in consumer packaged goods and other high-volume manufacturing for improved quality assurance, inventory management, compliance, counterfeit mitigation, and real-time operational excellence. Although there are a number of existing solutions for item-level traceability, none are practical for high-volume, low-cost consumer packaged goods (zero cost, zero product real estate, serialize at rate of production, etc.).  MxD is seeking information on existing technologies and/or research and development work being done in this area. Any individual from industry or academia may submit a response; early responses are encouraged.

RFI responses are due on or before April 3, 2019, 5:00pm CT.

MxD will utilize information received to scope projects in this subject area. Early submissions are encouraged to accelerate project scope and will be discussed with MxD membership during the Agile Tech Team Quarterly Meeting on March 29th.  Members interested in responding to the RFI are encouraged to register and participate in the March 29th meeting.

For questions or more information, contact Katie Tillery-Merk at katie.tillery-merk@uilabs.org

DOWNLOAD THE RFI

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.

Factory Operations & Industrial Control Systems Cyber Security Assessment, Tools, and Solutions – 15-01-02

Lead Organization: University of Illinois at Urbana-Champaign

Other Organizations: Heartland Science and Technology Group , Lockheed Martin, HL Precision Manufacturing, and Integrity Technology Solutions

Description: The project team developed a Cyber Secure Dashboard to help organizations understand the costs, capabilities, and effectiveness of DoD-required security measures for factory operations. The dashboard is designed to inituitively guide organizations , especially the small to mid-sized manufacturers that made up approximately 98% of U.S. manufacturers in 2015,[2] through the process of securing their information technology systems by providing detailed, step-by-step instructions, reference materials, industry best practices, and links to available templates and tools. It provides concrete implementation guidance for adhering to the nationally-accepted National Institute of Standards and Technology (NIST) cybersecurity framework, the DoD-mandated control requirements of the NIST SP 800-171 r1, and the NIST SP 800-53r4 cybersecurity control standard.