DE-FOA-0003329.0001_Smart Manufacturing Tech for Material & Process Innovation.pdf

Template Version November 2023

U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE)

Smart Manufacturing Technologies for Material and Process

Innovation

Funding Opportunity Announcement (FOA) Number: DE‐FOA‐0003329 FOA Type: 0001

Assistance Listing Number: 81.086

FOA Issue Date: 7/18/2024

Submission Deadline for Concept Papers: 8/22/2024 5:00 p.m. ET

Submission Deadline for Full Applications: 11/18/2024 5:00 p.m. ET

Expected Submission Deadline for Replies to Reviewer Comments: 1/14/2025 5:00 p.m. ET

Expected Date for EERE Selection Notifications: April 2025

Expected Timeframe for Award Negotiations: Apr‐Jun 2025

Applicants must submit a Concept Paper by 5:00 p.m. ET on the due date listed above to be eligible to submit a Full Application.

To apply to this FOA, applicants must register with and submit application materials through EERE eXCHANGE at https://eere‐eXCHANGE.energy.gov, EERE’s online application portal.

Applicants must designate primary and backup points‐of‐contact in EERE eXCHANGE with whom EERE will communicate to conduct award negotiations. If an application is selected for award negotiations, it is not a commitment to issue an award. It is imperative that the applicant/selectee be responsive during award negotiations and meet negotiation deadlines. Failure to do so may result in cancelation of further award negotiations and rescission of the selection.

Unique Entity Identifier (UEI) and System for Award Management (SAM) ‐ Each applicant (unless the applicant is excepted from those requirements under 2 CFR 25.110) is required to: (1) register in the SAM at https://www.sam.gov before submitting an application; (2) provide a valid UEI number in the application; and (3) maintain an active SAM registration with current information when the applicant has an active federal award or an application or plan under consideration by a federal awarding agency. DOE may not make a federal award to an applicant until the applicant has

Questions about this FOA? Email SM.FOA.FY24@ee.doe.gov Problems with EERE Exchange? Email EERE‐ExchangeSupport@hq.doe.gov Include FOA name and number in subject line.

complied with all applicable UEI and SAM requirements and, if an applicant has not fully complied with the requirements by the time DOE is ready to make a federal award, DOE will determine that the applicant is not qualified to receive a federal award and use that determination as a basis for making a federal award to another applicant.

NOTE: Due to the high number of UEI requests and SAM registrations, entity legal business name and address validations are taking longer than expected to process.

Entities should start the UEI and SAM registration process as soon as possible. If entities have technical difficulties with the UEI validation or SAM registration process they should use the HELP feature on SAM.gov. SAM.gov will address service tickets in the order in which they are received and asks that entities not create multiple service tickets for the same request or technical issue. Additional entity validation resources can be found here: GSAFSD Tier 0 Knowledge Base ‐ Validating your Entity.

Modifications

All modifications to the FOA are HIGHLIGHTED in the body of the FOA.

Mod. No. Date Description of Modification

0001 9/9/2024 The FOA has been modified to remove the cost share reduction option for Topic Area 1, Area of Interest 3. Revisions have been made in Section I.B.i and III.B to delete the cost share reduction language.

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Table of Contents

Table of Contents ................................................................................................................................................ i

I. Funding Opportunity Description

A. Background and Context

i. Background and Purpose

ii. Technology Space and Strategic Goals

B. Topic Areas Topic Areas

i. Topic 1: Smart Manufacturing for a Circular Economy

ii. Topic 2: Smart Manufacturing of Tooling and Equipment for Sustainable Transportation

iii. Topic 3: Smart Manufacturing for High Performance Materials

iv. Topic 4: Smart Technologies for Sustainable and Competitive U.S. Mining

C. Applications Specifically Not of Interest D. Research & Development (R&D) Community Benefits Plan E. Authorizing Statutes

II. Award Information

A. Award Overview

i. Estimated Funding

ii. Period of Performance

iii. New Applications Only

B. EERE Funding Agreements

i. Cooperative Agreements

ii. Funding Agreements with Federally Funded Research and Development Center (FFRDCs)

III. Eligibility Information

A. Eligible Applicants

i. Domestic Entities

ii. Foreign Entities

B. Cost Sharing

i. Legal Responsibility

ii. Cost Share Allocation

iii. Cost Share Types and Allowability

iv. Cost Share Contributions by FFRDCs

v. Cost Share Verification

vi. Cost Share Payment

C. Compliance Criteria D. Responsiveness Criteria E. Other Eligibility Requirements

i. Requirements for DOE/NNSA FFRDCs Listed as the Applicant

ii. Requirements for DOE/NNSA and Non‐DOE/NNSA FFRDCs Included as a Subrecipient

F. Limitation on Number of Concept Papers and Full Applications Eligible for Review G. Questions Regarding Eligibility

IV. Application and Submission Information

A. Application Process

i. Additional Information on EERE eXCHANGE

B. Application Forms C. Content and Form of the Concept Paper subject line.

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D. Content and Form of the Full Application

i. Full Application Content Requirements

ii. SF‐424: Application for Federal Assistance

iii. Technical Volume

iv. Resumes

v. Letters of Commitment

vi. Impacted Indian Tribes Documentation

vii. Statement of Project Objectives (SOPO)

viii. R&D Community Benefits Plan

ix. Budget Justification Workbook

x. Summary for Public Release

xi. Summary Slide

xii. Subrecipient Budget Justification (if applicable)

xiii. Budget for DOE/NNSA FFRDC (if applicable)

xiv. Authorization for Non‐DOE/NNSA or DOE/NNSA FFRDCs (if applicable)

xv. SF‐LLL: Disclosure of Lobbying Activities

xvi. Waiver Requests

xvii. Current and Pending Support

xviii. Transparency of Foreign Connections

xix. Potentially Duplicative Funding Notice

E. Content and Form of Replies to Reviewer Comments F. Post Selection Information Requests G. Unique Entity Identifier (UEI) and System for Award Management (SAM) H. Submission Dates and Times I. Intergovernmental Review J. Funding Restrictions

i. Allowable Costs

ii. Pre‐Award Costs

iii. Performance of Work in the United States (Foreign Work Waiver)

iv. Construction

v. Foreign Travel

vi. Equipment and Supplies

vii. Build America Buy America Requirements for Infrastructure Projects

viii. Lobbying

ix. Risk Assessment

x. Invoice Review and Approval

xi. Prohibition Related to Foreign Government‐Sponsored Talent Recruitment Programs

xii. Affirmative Action and Pay Transparency Requirements

xiii. Foreign Collaboration Considerations

V. Application Review Information

A. Technical Review Criteria

i. Concept Papers

ii. Full Applications

iii. Criteria for Replies to Reviewer Comments

B. Standards for Application Evaluation C. Other Selection Factors

i. Program Policy Factors D. Evaluation and Selection Process

i. Overview

ii. Pre‐Selection Interviews

iii. Pre‐Selection Clarification

iv. Recipient Responsibility and Qualifications subject line.

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v. Selection E. Anticipated Notice of Selection and Award Negotiation Dates

VI. Award Administration Information

A. Award Notices

i. Ineligible Submissions

ii. Concept Paper Notifications

iii. Full Application Notifications

iv. Applicants Selected for Award Negotiations

v. Alternate Selection Determinations

vi. Unsuccessful Applicants

B. Administrative and National Policy Requirements

i. Registration Requirements

ii. Award Administrative Requirements

iii. Foreign National Participation

iv. Subaward and Executive Reporting

v. National Policy Requirements

vi. Environmental Review in Accordance with National Environmental Policy Act (NEPA)

vii. Flood Resilience

viii. Applicant Representations and Certifications

ix. Statement of Federal Stewardship

x. Statement of Substantial Involvement

xi. Subject Invention Utilization Reporting

xii. Intellectual Property Provisions

xiii. Reporting

xiv. Go/No‐Go Review

xv. Conference Spending

xvi. Uniform Commercial Code (UCC) Financing Statements

xvii. Real Property and Equipment

xviii. Implementation of Executive Order 13798, Promoting Free Speech and Religious Liberty

xix. Participants and Collaborating Organizations

xx. Current and Pending Support

xxi. U.S. Manufacturing Commitments

xxii. Interim Conflict of Interest Policy for Financial Assistance

xxiii. Data Management Plan

xxiv. Fraud, Waste, and Abuse

xxv. Human Subjects Research

VII. Questions/Agency Contacts

VIII. Other Information

A. FOA Modifications B. Government Right to Reject or Negotiate C. Commitment of Public Funds D. Treatment of Application Information E. Evaluation and Administration by Non‐Federal Personnel F. Notice Regarding Eligible/Ineligible Activities G. Notice of Right to Conduct a Review of Financial Capability H. Requirement for Full and Complete Disclosure I. Retention of Submissions J. Title to Subject Inventions K. Government Rights in Subject Inventions L. Rights in Technical Data subject line.

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M. Copyright N. Export Control O. Prohibition on Certain Telecommunications and Video Surveillance Services or Equipment P. Personally Identifiable Information (PII) Q. Annual Independent Audits

Appendix A – Cost Share Information

Appendix B – Sample Cost Share Calculation for Blended Cost Share Percentage

Appendix C – Waiver Requests For: 1. Foreign Entity Participation; and 2. Foreign Work

Appendix D – Required Use of American Iron, Steel, Manufactured Products, and Construction Materials Buy America Requirements for Infrastructure Projects

Appendix E – Definition of Technology Readiness Levels

Appendix F – List of Acronyms

Appendix G – TEA/LCA RESOURCES

Appendix H – R&D Community Benefits Plan Guidance

Appendix I – Additional Smart Manufacturing Background Information

Appendix J – Critical Materials Collaborative (CMC) Information subject line.

I. Funding Opportunity Description

A. Background and Context

i. Background and Purpose This funding opportunity announcement (FOA) is being issued by the U.S. Department of Energy’s (DOE) Advanced Materials and Manufacturing Technologies Office (AMMTO).

The mission of AMMTO is: “We inspire people and drive innovation to transform materials and manufacturing for America's energy future.” This is in alignment with AMMTO’s vision for the future – a globally competitive U.S. manufacturing sector that accelerates the adoption of innovative materials and manufacturing technologies in support of a clean, decarbonized economy.

To realize a clean, decarbonized economy, we must drive innovation to transform resources, materials, and manufacturing for energy applications. The U.S. ramp up of production of technologies that are needed for this energy transition will require new materials, new manufacturing processes, and new circular materials flows.

The modernization of manufacturing can help bring these innovations on‐line at the needed scale and quality faster. Among the most important trends to impact the manufacturing sector is the drive toward digitaliza on, which is the process of employing digital technologies and informa on to transform the manufacturing enterprise system and business opera ons across the total produc on lifecycle. Smart manufacturing provides a systemic approach for the digital transforma on of manufacturing that holds great promise to significantly improve produc vity, efficiency, safety, security, and sustainability of U.S. manufacturing and energy systems.

While there has been significant support of digitization innovation, the manufacturing sector has been relatively slow to adopt digital technologies. This is problema c as digital transforma on—through its improvements in cost, quality, produc vity, me to market, efficiency, safety, security, and sustainability—holds great promise to transform how we manufacture products and materials for our energy future.

Smart manufacturing refers to the suite of pla orm technologies that directly support the digital transforma on of the manufacturing enterprise across the entire production lifecycle, which includes design, process, production, supply network, and enterprise levels. Platform technologies are manufacturing technologies that can be applied to manufacture multiple products. The digital transforma on of manufacturing through smart manufacturing pla orm technologies would promote the development of cyberphysical systems (CPS) for manufacturing. A vision for CPS for manufacturing is the development of modular, interconnected systems that combine physical processes, computa onal resources, and networked communica on to create a highly automated, subject line.

efficient, and flexible manufacturing environment. In such systems, physical components (e.g., machinery, robots, sensors) would be interconnected with so ware and data networks, enabling real‐ me monitoring, control, and op miza on of manufacturing processes. This would enable a host of significant capabili es including real‐ me data processing and feedback, advanced automa on and control, enhanced flexibility and adaptability, and improved efficiency and quality.

Figure 1. Smart manufacturing program pillars and associated smart manufacturing technologies.

AMMTO’s smart manufacturing priorities address major end‐use industrial opportunities for smart manufacturing in the energy sector. Figure 1 lays out AMMTO’s primary pillars of smart manufacturing, which include: (1) Enhance Product Quality, (2) Support Process Innovation, (3) Accelerate Materials Development, (4) Enable Materials Circularity, (5) Drive Asset Optimization and (6) Increase Workforce Readiness.

a. Concepts and Definitions:

As used by AMMTO and in this FOA, smart manufacturing comprises the following concepts and definitions. Smart manufacturing builds upon the pillars of lean manufacturing, digital manufacturing, and continuous manufacturing to achieve superior throughput, efficiency, quality, and precision in the manufacturing process. The term lean manufacturing refers to creating maximum outputs with reduced inputs in terms of cost, time, energy, and effort.

Digital manufacturing emphasizes the digitization and integration of manufacturing activities with the properties and performance of products.

Continuous manufacturing transforms the traditional batch manufacturing subject line.

process into an integrated process to enable faster production. Smart manufacturing frameworks are being applied across most industry sectors and can be leveraged beyond traditional manufacturing processes. For example, the principles of smart manufacturing can also be applied to improve how new materials are designed or to enhance how materials are recovered at the end of a product’s life. At its core, smart manufacturing is a framework that encompasses generating, sharing, and using data through a combination of physical and virtual processes to enable leveraging complex data for a resilient, responsive, and efficient manufacturing sector.

For the full background and for specific terms and concepts, refer to Appendix I.

b. Purpose and Desired Impact:

This FOA supports administration goals by developing options for a national plan for smart manufacturing technology development and deployment as per the language set forth in The Energy Act of 2020, Sec. 6006 on pages 1113 and 1115.

DOE, in partnership with the National Academies of Science, Engineering, and Medicine (NASEM), conducted workshops to study the options for developing a national plan for smart manufacturing. The final consensus study report1 examined the technical frameworks and processes, identified possible timelines and necessary resources, and explored policies and general roles for government, industry, and academia to address near‐, medium‐, and long‐term challenges to improve the productivity and energy efficiency of the manufacturing sector of the United States and ensure U.S. competitiveness.

Building a clean and equitable energy economy and addressing the climate crisis is a top priority of the Biden Administration. This FOA will advance the Biden Administration’s goals to achieve carbon pollution‐free electricity by 2035 and to “deliver an equitable, clean energy future, and put the United States on a path to achieve net‐zero emissions, economy‐wide, by no later than 2050“2 to the benefit of all Americans by demonstrating how U.S. manufacturing can be significantly improved and aid in meeting these goals through the use of smart manufacturing. The Department of Energy is committed to pushing the frontiers of science and engineering, catalyzing clean energy jobs through research, development, demonstration, and deployment (RDD&D), and ensuring environmental justice and inclusion of underserved communities. Critical minerals and materials are a key element of the ongoing transition to an energy economy that is decarbonizing and lowering energy costs to American families and businesses, and also critical to national competitiveness–for grid storage, for

1 Options for a National Plan for Smart Manufacturing | National Academies 2 Executive Order 14008, “Tackling the Climate Crisis at Home and Abroad,” January 27, 2021.

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the resilience of homes and businesses, and for electrification of the transportation sector.

The research and development (R&D) activities to be funded under this FOA will support the government‐wide approach to the climate crisis by driving the innovation that can lead to the deployment of clean energy technologies, which are critical for climate protection.

ii. Technology Space and Strategic Goals This FOA seeks applications to address the development of smart manufacturing technologies –including through “smart RD&D” – that can contribute to a resilient, responsive, leading‐edge, and efficient manufacturing sector that delivers the technologies needed for the nation’s clean energy transition. The manufacturing community can reduce manufacturing costs and accelerate time‐ to‐market by integrating performance characteristics of final products with processes aided by a smart manufacturing framework. The information‐driven collaborative orchestration of physical and digital processes across the entire value chain is one aspect of smart manufacturing. Smart manufacturing relies on a combination of physical and virtual technologies to make designing, processing, and manufacturing faster, higher value‐added, more resilient, sustainable, and more cost‐effective. Individual processes within a plant, factory, or entire value chain are integrated and continually monitored with sensing, process modeling, and predictive analytics. The physical processes focus on controlling and optimizing processing conditions for desired outcomes, while virtual processes uncover underlying complex interactions between the physical processes and provide insight into better ways to design and manufacture products (i.e., feedback for physical processes). This FOA seeks to implement smart manufacturing across several diverse application areas and market sectors to: (a) show the benefits of incorporating smart manufacturing in areas aligned with AMMTO’s mission, and (b) to provide practical experience which will be used to further refine AMMTO’s smart manufacturing strategy and future R&D direction. Detailed technical descriptions of the specific Topic Areas are provided in the sections that follow.

B. Topic Areas

Topic Areas This FOA includes four topic areas, all involving aspects of smart manufacturing.

Topic # Topic Area

1 Smart Manufacturing for a Circular Economy subject line.

Smart Manufacturing of Tooling and Equipment for Sustainable Transportation

3 Smart Manufacturing for High Performance Materials

4 Smart Technologies for Sustainable and Competitive U.S. Mining

Applications under these topic areas should meet the following requirements:

Applicants must describe their innovation’s connection to smart manufacturing. Technologies that have applications in more than one industry sector are encouraged.

While the topics are written broadly to be inclusive of a variety of approaches, proposed efforts should be specific and clearly describe the technology being developed and validated.

Applicants must propose to perform a techno‐economic analysis (TEA) and a lifecycle assessment (LCA) as part of their potential projects except where noted in the following Topics. These analyses must include a comparison of the current, commercially available state‐of‐the‐art technology with the proposed approach, including comparisons of functionality. The comparison should include an initial TEA and an initial LCA that considers:

1) current, commercially available state‐of‐the‐art technology; 2) relevant preliminary data that demonstrates the current developmental stage of the proposed solution (experimental, literature‐based, or both); and 3) the proposed solution’s targets that will be achieved by end of project. The preliminary TEA and LCA must be included in the technical volume of the application, should be conducted using credible methodologies and assumptions and will need to be updated throughout the life of selected projects. Proposed technologies/approaches must demonstrate economic feasibility and a net decrease in overall lifecycle energy consumption.

In all cases, letters of commitment from partner organizations, and especially those that pledge cost share, will make for stronger applications.

Applications must include the process improvements required for their proposed solutions to move to the next step in commercialization.

The role of modeling and simulations, experimentation, lab, bench, and pilot‐scale testing should be explained. Anticipated technical barriers should be described, along with a planned approach to overcome them.

Applicants should explain the underlying research to date, including any literature review or experimental data to support the proposed R&D approach and justify the R&D needs.

The cost share must be at least 20% of the total allowable costs (i.e., the sum of the government share, including FFRDC costs if applicable, and the recipient share of allowable costs equals the total allowable cost of the project).

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All applications must also define credible and measurable baselines, supported by prior data from literature and/or experimentation, against which their user defined metrics will be evaluated. The quality and scientific depth of these proposed baselines and metrics will be an important element of the technical evaluation of applications.

Applications must clearly identify the starting and ending TRL for the project and justify the TRLs assigned. Applicants are encouraged to also review the Adoption Readiness Level (ARL) framework and describe their technology’s ARL as well.3 ARL represents important risk factors for commercialization beyond technology readiness.

Recognizing that not all applicants may have prior experience with TEA and LCA at the appropriate level of sophistication, the DOE has developed tools such as the Greenhouse gases, Regulated Emissions, and Energy use in Technologies model (GREET) developed by the Argonne National Laboratory4 and5 tool.

Appendix G includes additional TEA and LCA resources available to applicants.

If selected for award negotiations, project teams should be sensitive to cybersecurity and describe in their cybersecurity plans how they will use best practices to protect their data; it is recommended that applicants follow NIST 800‐171 or similar methods to ensure best practices are used. Projects that have higher data management risks (e.g., projects that utilize large data sets, projects involving software development, etc.) may be required to develop more extensive data management and/or cybersecurity plans.

Applicants who plan to generate data sets to inform machine learning (ML) or artificial intelligence (AI) that would be broadly applicable to other AMMTO projects may be required during award negotiation to make such data available in an agreed data repository. These applicants may also be required to submit an expanded data management plan to include an explanation of the handling protocol for generated data, identification of potential sensitive data and how it will be handled, identification of personal privacy protection protocols, and other intellectual property (IP) and data management considerations as determined to be appropriate for the scope of work.

3 DOE Office of Technology Transitions. Adoption Readiness Levels (ARL): A Complement to TRL. Available from:

https://www.energy.gov/technologytransitions/adoption‐readiness‐levels‐arl‐complement‐trl 4 Argonne National Laboratory. Greenhouse Gases, Regulated Emissions, and Energy use in technologies model (GREET). 2022; Available from: https://greet.es.anl.gov/.

5Materials Flows through Industry Supply Chain Modeling Tool | Advanced Manufacturing Research | NREL subject line.

i. Topic 1: Smart Manufacturing for a Circular Economy

Background:

The aim of this FOA topic is to increase the viability of circular supply chains (Re‐ X pathways), such as recycling, repairing, remanufacturing, and reuse, by improving their efficiency and economics. Smart manufacturing technologies have the potential to address key challenges in deploying economically viable Re‐X supply chains that reduce environmental impacts and encourage secure and sustainable domestic supply chains.6 There are three areas of interest within this topic area: smart manufacturing technologies for improved sorting and characterization, interoperable and open supply chains for expanded Re‐X, and improved data transparency for accurate LCAs.

Area of Interest 1: Smart Manufacturing Technologies for Improved Sorting and Characterization The challenge of sorting and characterizing materials is one of the primary barriers to broader implementation of Re‐X pathways. For example, many manufacturers are interested in incorporating greater proportions of recycled materials (“secondary feedstocks”) into their products, but they encounter difficulty in sourcing feedstock streams that are of a consistent and acceptable quality and price. This issue is relevant across various high‐volume materials including, but not limited to, glass, plastics, metals, paper, textiles, and building materials, as well as specialty material streams such as composites, specialty alloys, and critical materials. To facilitate the recovery and recycling of these materials into new products, it is crucial to improve our ability to identify, sort, and/or characterize these streams. Flat and real‐time data collection has the potential to address this challenge by integrating sensors and machine learning tools to enhance sortation and characterization processes. This integration enables a wider utilization of secondary materials. For example, Google has demonstrated that using this approach can substantially reduce contamination in recycling streams, leading to higher quality secondary material outputs.7,8 However, this required significant computing power; deployable technologies that facilitate improved sorting without requiring large data centers are needed.

6 Schumacher, K., Last, N., Morris, K., Ferrero, V., Hapuwatte, B., Mathur, N. and Reslan, M., Fostering a Circular Economy of Manufacturing Materials Workshop Report. 2023; https://doi.org/10.1520/amcoe‐economy‐of‐ manufacturing‐materials.

7 Herzog, A., Rao, K., Hausman, K., Lu, Y., Wohlhart, P., Yan, M., Lin, J., Arenas, M.G., Xiao, T., Kappler, D. and Ho, D., 2023. Deep RL at scale: Sorting waste in office buildings with a fleet of mobile manipulators. arXiv preprint arXiv:2305.03270.

8 Werner, Mike. (April 11, 2024). Keynote Address: 2024 REMADE Circular Economy Tech Summit and Conference.

Washington, DC., United States.

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Applications to this area of interest encompass a range of potential solutions.

These may involve the development of new sensors crucial for identifying and characterizing contamination within secondary material streams, the establishment of a framework that leverages commercially available sensors to collect and utilize data collected during secondary material handling processes, and/or developing software or machine learning tools to analyze collected data, thereby facilitating appropriate sorting and grading of secondary material streams. This challenge extends beyond traditional material recovery facilities (MRFs) and is also applicable to various material streams relevant to clean energy supply chains, such as critical materials and composites.

Area of Interest 2: Interoperable and Open Supply Chains for Expanded Re‐X Another barrier to Re‐X implementation lies in the complexity of repairing or remanufacturing products not initially designed for Re‐X. For example, refurbishment and remanufacturing are often hindered by the diversity of products on the market. To effectively repair a product and prolong its service life, tools capable of identifying which components require replacement are crucial. This task becomes particularly challenging when dealing with a mixed stream of goods. Smart manufacturing approaches offer potential solutions to this challenge by fostering more interoperable and open supply chains. These approaches facilitate Re‐X interventions aimed at extending product lifetimes through strategies such as reuse and repair, and make it feasible to recover usable components for remanufacture.

Applicants may apply these principles to enhance the ability for products to enter circular pathways regardless of whether they were initially designed with Re‐X in mind. For example, applicants could propose to develop technologies aimed at collecting and analyzing data to identify components within products and identify what interventions are required to prolong their service life. This task presents challenges, particularly when dealing with a mixed stream of discarded products like consumer electronics, which feature a diverse range of components and assemblies. However, smart manufacturing tools may enable valuable components and materials to be recovered from these streams to improve circularity. Approaches to monitor and assess the health of product components to determine when repair or replacement is required to maximize product lifetimes is also of interest.

Applicants may also propose to redesign products with Re‐X in mind, making them interoperable and accessible for repair, remanufacturing, and other Re‐X pathways. This involves incorporating flexibility into the design to accommodate new Re‐X tools as they become available. This area of interest includes the need for both software and hardware, including design software and the physical tools necessary for product construction and disassembly. Improved tools and subject line.

processes are crucial to facilitate broader adoption of these Re‐X approaches to extend the usable life of products. Applicants should describe how their innovation goes beyond state‐of‐the‐art manufacturability analyses or design for disassembly and is uniquely optimal for design for Re‐X in a way that existing platforms are not.

For all applications to this area of interest, applicants must describe the business model for their innovation and consider required stakeholders across the supply chain for the entire lifecycle of the product. Extending the useful life of products can be seen as eating into profits that would come from replacing that product on a periodic basis. Furthermore, the supply chain can be complex. It is important that technologies designed for Re‐X are supported by a functional business model – for example, a product designed for repair or remanufacture should consider not only who will manufacture the original product but also who will perform the Re‐X interventions, where they will take place, and where the product will be used after the intervention. Similarly, redesign efforts to make products more interoperable by standardization across manufacturers should discuss the business case for this and how it encourages participation by multiple manufacturers at all relevant points across the supply chain. The business cases for these innovations are complex, and applicants should consider what is needed to make the innovation commercially viable and logistically feasible considering the need for industry cooperation. Project teams should have representation by all key stakeholders across the supply chain that the chosen business model requires.

Area of Interest 3: Improved Data Transparency for Accurate Lifecycle Assessments A third challenge to broaden implementation of Re‐X throughout the economy is the limited availability and transparency of data across supply chains. Enhancing the availability and transparency of data is crucial to bolster market acceptance of secondary materials. Additionally, it is vital for providing manufacturers with access to more precise data for conducting LCAs, enabling them to better understand the impacts of their process decisions.

A smart manufacturing approach to product development and manufacture involves collection and sharing of data across the supply chain. It is essential to have comprehensive knowledge of the components within a device and the production processes involved in their creation. This data can also be leveraged to improve the accuracy of LCAs to guide decision making. Applicants to this area of interest should clearly describe the challenge the proposed innovation is solving and how it goes beyond existing approaches (e.g., linking a controller to a database) to provide broader value to the community. Approaches that improve data transparency to provide stakeholders across the supply chain better subject line.

visibility both forwards and backwards along the supply chain (e.g., the sub systems that make up a part and the device that part is used in) are of interest.

Applications should clearly describe how the data will be leveraged and how it will increase the viability of Re‐X pathways.

Applicants should indicate in their submission which area of interest they are applying to. A single solution that helps address more than one area of interest is acceptable. Only one submission is permitted to this topic per applicant.

Across all three areas of interest, this FOA topic targets technologies that utilize smart manufacturing strategies. These strategies encompass a variety of approaches including the implementation of AI/ML, the integration of sensors and imaging techniques, and the collection and sharing of data throughout the supply chain. Practical examples include, but are not limited to, increasing the purity of recycled materials exiting MRFs and efforts to characterize and mitigate contamination in scrap streams, such as copper contamination in steel, and in complex mixed streams, such as composites. Additionally, rapid characterization of secondary material streams to certify their composition and/or adherence to relevant standards, as well as endeavors to increase the rates of repair and remanufacturing for mixed streams, such as streams containing more than one model of device. Use of smart manufacturing technologies to improve circularity of building materials (e.g., demolition debris, flat glass, and building systems such as HVAC systems) is also of interest.

Proposals that develop cross cutting technologies applicable to multiple applications are encouraged. When identifying opportunities to leverage smart manufacturing tools to address circularity‐related challenges, applicants are encouraged to consider both the challenges in accessing a given supply chain and the complexities associated with implementing the selected smart manufacturing approach, with the goal of identifying areas for highest impact. It is important to thoroughly consider the supply chain specific to the proposed application space. For example, recycled feedstocks are often logistically constrained as it is cost prohibitive to ship end‐of‐life materials to a centralized facility. In such cases, pre‐sorting at the point of collection to avoid excess shipping may be beneficial. Similarly, the transportation of heavy machinery to a repair or remanufacture facility presents challenges, so tools to enable remanufacture on site or to encourage maintenance to extend the useable lifetime before components degrade may be beneficial. The challenges of implementing the smart manufacturing approach should also be considered. For example, novel sensors that require significant R&D prior to deployment may need to be de‐risked. Applicants are encouraged to explain how their proposed subject line.

approach would address challenges associated with accessing the supply chain or advancing difficult‐to‐implement smart manufacturing technologies.

All applications should involve industry participation, with representation by all key stakeholders across the relevant supply chain. Applicants are encouraged to involve stakeholders from small and medium businesses, as well as underrepresented groups, within these teams. This inclusive approach enhances the diversity of perspectives and expertise, fostering comprehensive solutions and promoting equitable participation in the innovation process. Proposed projects should have already demonstrated proof of concept (TRL 3) and target a 24 to 36 month scope of work that focuses on validating the capability of the technology in a relevant environment (TRL 6). Project teams must include representation of stakeholders across the supply chain and applicants should describe what additional stakeholder engagement will be required to deploy the technology.

Candidate Metrics and Targets:

A successful project should demonstrate pathways to reduce waste, reduce energy consumption and carbon emissions, reduce other environmental emissions, demonstrate cost competitiveness, and improve supply chain resiliency. Applicants must identify and justify appropriate target metrics (e.g., material, energy, and emissions benefits), and clearly indicate how the proposed project will satisfy the metrics. Metrics should be specific to target material waste streams and Re‐X strategy, and must define appropriate baselines, minimum targets, and stretch targets. Metrics should also illustrate the value of the smart manufacturing approach being utilized. Successful applicants must undergo periodic assessments of their metrics during the award period to evaluate potential impacts. Examples of metrics include the following:

Objective Metric Minium Target

Stretch Target

Baseline

Reduce energy consumption energy/unit (product mass or part basis)

10% 50% Applicant Defined

Increased purity of secondary feedstock exiting MRF

% purity of selected material stream

10% 30% Applicant Defined

Increased production of secondary feedstock

Metric Tons 20% 60% Applicant Defined

Reduce carbon intensity

% Carbon intensity change as measured by ton CO2e/kg product

20% 25% Applicant Defined subject line.

Reduce Production Cost

% reduction $/unit 10% 25% Applicant Defined

Increased number of use cycles for product components cycles/component 10% 30% Applicant Defined

Applications Specifically Not of Interest:

Applications that do not advance technology beyond state of the art or incremental improvements to existing technologies

Applications without metrics tied to the benefits of the selected smart manufacturing approach(es)

Technologies that do not advance Re‐X (recycling, repair, remanufacturing, etc.)

Technologies that have no pathway to economic viability

Applications that duplicate work funded by DOE in previous solicitations or projects

Technologies that target fuel production from waste streams (e.g. plastic waste to fuels)

Technologies focused on Re‐X for energy storage/battery materials.

ii. Topic 2: Smart Manufacturing of Tooling and Equipment for

Sustainable Transportation

Background:

This topic focuses on developing and validating broadly applicable smart manufacturing technologies that can advance manufacturing processes and strengthen supply chains for production of sustainable transportation. In the context of this FOA, sustainable transportation refers to transitioning from traditional internal combustion engine‐based vehicles to electric vehicles (based on either batteries or fuel cells across all vehicle types from automobiles to fleets to all truck classes) as selected by applicant. Successful projects would: (a) make tooling and equipment needed to produce applicant‐selected key components of these vehicles faster and more economically through the application of smart manufacturing concepts, or (b) focus on reducing component defects and enabling new functionalities by means of smart manufacturing. Many project innovations may also apply to other clean energy applications. Detailing any such broader application potential is not required but is encouraged.

Machinery, industrial controls, and automation are one of the largest and most competitive sectors of the U.S. manufacturing economy. Advancing machinery manufacturing is crucial as it impacts all other manufacturing sectors. After a subject line.

sharp decline in exports in 2009 and subsequent recovery, exports of machinery manufacturing totaled $166 billion in 2015. Machinery production and use are vital for the U.S trade balance. Machinery helps create output in almost all the other sectors of the U.S. economy:

Some machines are used to manufacture other goods, Machinery, industrial controls, and automation are used in sustainable transportation (for example, automotive and aerospace industry), Construction machinery is used to build structures, Agricultural machinery is used to harvest and process crops, and

Mining and oil field machinery are used to produce energy.

The U.S. is highly reliant on imports for machine tools, and increasing global demand will make it more difficult for U.S. manufacturers to import enough. The continuous decline of machine tool production and enabling capabilities, alongside dependency on foreign markets, threatens the foundation of U.S.

innovation, productivity, competitiveness, and even national security. For the machinery, industrial controls, and automation industry, technology‐specific challenges still exist for flexibility (product platforms that create a common core that invites third parties to develop and market an increasing number of product variants), high precision and repeatability, production rate, delivery cycle, reliability and maintainability, safety, ergonomics, environmental response, and user‐friendliness. This is further coupled with the need for improved and secure controls that cater to automation for next‐generation cyber‐physical systems.

Moving forward, in the machinery, industrial controls, and automation industry there is a need for:

Raising productivity in the nation’s supplier base, Broadening participation in exports, Ramping up skilled workforce development, and

Exploring opportunities to upgrade plants and equipment for next‐ generation cyber‐physical systems.

In particular, small and midsize suppliers in the U.S. industrial base need next‐ generation equipment and technologies to boost productivity. Sales of many types of machinery inherently accompany a variety of high‐value services, including specialized architecture, engineering, and logistics. Therefore, innovations and technology development that cater to improving domestic capability and promoting U.S. competitiveness are needed.

There are several opportunities for the U.S. to play a pivotal role in shaping the economy of the machinery manufacturing subsector. These include innovation and R&D in areas such as smart manufacturing encompassing, for example, digital thread and digital twin, smart sensor networks for advanced automation subject line.

and control, AI/ML for next generation smart machinery, a related products industrial machinery supply network, machinery asset management, advancing infrastructure critical to digital connectivity, and training the next‐generation skilled workforce.

The U.S. is well‐poised to handle the opportunities and challenges associated with advancing automation and controls for secure next‐generation cyber‐ physical manufacturing systems. The U.S. has a strong leadership position in relevant software and hardware technologies: digital thread and digital twin, and Industrial Control Systems (ICS); advanced control systems and interfaces;

sensors and networks for advanced machine control and optimization; and high‐ performance computing facilities. AMMTO looks to connect these U.S.

advantages to meet known challenges when increasing sustainable transportation volumes. Representative example challenges include the long lead time and high cost of molds and stamping tools used for producing many automotive components and costs of downtime. In 2006, auto industry manufacturing executives showed stopped production cost on average of $22,000/minute or $1.3M/hour. Smart manufacturing principles can be used to help reduce and mitigate these costs, performance constraints, and other issues that result in sub‐optimal productivity for the level of capital and operating expenditures that can be tolerated. Descriptions of desired technology focus areas and related R&D follow.

Applications can focus on:

a tooling and equipment manufacturing and/or operational subsector or industry that supports sustainable transportation.

addressing smart manufacturing technology area(s) applicable to a wide range of industries.

To overcome the challenges described before and claim the benefits of smart manufacturing for tooling and equipment, AMMTO seeks projects that focus on development of broadly applicable smart manufacturing platforms that can be adapted to augment the production of a variety of tooling or equipment, relating to one or more of the following areas of interest:

Area of Interest 1: Automation For automation, R&D efforts should focus on the integration of hardware and software systems in manufacturing tooling and equipment to improve component productivity. For example, this might require interfaces and interoperability of machinery, tools, and products to enable connected systems across a plant’s production line, or an enterprise system across multiple plant locations and companies. Process control automation will dramatically improve subject line.

the productivity of connected systems to either: (a) produce tooling and equipment more quickly and cost effectively, and/or (b) increase functionality of the tooling and equipment. For the latter, it is critical to build on industry‐driven reference architecture based on open standards and protocols to enable the next generation of digital machinery including cybersecurity for “things” – devices, sensors, actuators, and controllers.

Smart manufacturing innovations for tooling and equipment for sustainable transportation areas can include but are not limited to:

AI/ML for smart machines and control;

Advanced sensors and control and data acquisition, open control interfaces, for machinery manufacturing, tools, or components to improve inter‐ connected system productivity;

Digital thread and digital twin for smart manufacturing for tooling and equipment;

Computing and industrial infrastructure for advanced control systems and prescriptive analytics for machinery, automation, digital twin for “things” – devices, sensors, actuators, and controllers;

Open ecosystem for the implementation and deployment models, application tool kits, apps, and marketplace for apps for manufacturing machinery;

Industry standards for open control systems and interfaces and actual control systems.

Based on industry state‐of‐the‐art benchmarks, activities are expected to:

At the Build Stage:

Significantly increase domestic tooling and equipment production though innovation both in hardware and software; and

Significantly increase the domestic tooling and equipment components (tools, jigs, fixtures, etc.) supply network and production.

At the Use Stage:

Reduce or eliminate the true downtime cost (planned and unplanned) by a significant factor;

Refine computation and overall equipment effectiveness (OEE) and improve OEE by a significant factor;

Reduce or eliminate maintenance costs (preventive, unscheduled, and most importantly unnecessary maintenance), and

Facilitate transitioning from traditional ICE vehicles to electric vehicles (batteries and fuel cells).

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At the Optimization Stage:

Increase energy productivity of tooling and equipment through better scheduling optimization using the industrial internet of things and advanced control interfaces, and

Optimize the tooling and equipment life based on data analytics and increasing the prediction accuracy of component failures and of the remaining life of parts and components.

Area of Interest 2: Manufacturing Asset Management for Improving System Performance For supply network and manufacturing asset management, R&D efforts will potentially focus on improving the performance (cost, quality, and throughput) of the tooling and equipment, industrial controls, and automation network.

Efforts must integrate activities at the machine, machine tool, machine part, and component level. Equipment capability needs to include energy‐aware and efficient operations with standardization and integration. Recent advances in modeling and simulation, machine learning, and AI can be leveraged to improve productivity and performance of sustainable transportation tooling and equipment while reducing breakdowns, unplanned downtimes, and scheduled repairs.

Practical examples and target metrics of applying smart manufacturing (automation, asset management, etc.) include, but are not limited to:

Current state of the art automotive industry tooling and equipment for composite material components manufactured using processes such as injection molding or compression molding typically cost hundreds of thousands of dollars and require months to procure.