NIST - Manufacturing Technology Roadmapping

AI-Enhanced Multimodal Sensing of Materials and Processes for Complete Product Lifecycle Performance

Roadmap Overview

Strategy

With recent advances in artificial intelligence (AI) and machine learning (ML) over the past few years, alongside improvements in distributed sensing, Internet of Things (IoT), and Edge Computing, there is a tremendous opportunity to combine AI/ML with physical domain knowledge (materials, processes, etc.) for manufacturing. This integration would make it possible to leverage the multi-scale, multimodal data streams from across a product’s service life to achieve significant improvements at each stage in its lifecycle, from materials synthesis and selection, to product design, manufacturing, deployment, and eventual retirement and/or recycling.

This roadmap sets out a vision for this desired future state, identifies major challenges and opportunities related to achieving the vision, and provides a set of implementation plans for overcoming the challenges and realizing the benefits of AI-enhanced multimodal sensing of materials and processes for complete product lifecycle performance.

Goals and Objectives

This roadmap establishes a plan to develop multimodal sensing and AI/ML-driven closed-loop control capabilities and data insights to help manufacturers improve full product lifecycle performance. There are four key objectives to enable this goal:

1. Integrate Multimodal Data Over Multiple Timescales (throughout the product lifecycle)
2. Capture Domain Insights from Materials Science and Manufacturing to guide Core Technology Developments
3. Develop Feedback Strategies over Multiple Timescales that impact Materials Synthesis and Selection, Product and Process Design, Process Control, Product Service Life, and Retirement
4. Develop Core Technologies necessary to achieve the vision (AI/ML, Multimodal Sensing and Imaging, Process Control and Automation, Physics-based Computational Models)

Core Focus Areas

The core focus areas for this roadmap are:

1. Sensing, Data Acquisition & Data Management Across the Product Lifecycle
2. Applications of AI/ML & Data Analytics Across the Product Lifecycle
3. Comprehensive Integration of Data and AI/ML Insights Across the Product Lifecycle

Roadmap Release - February 27, 2024

We will also hold initial planning discussions for a proposed consortium, as the activities proposed in this roadmap are beyond the scope, funding, and capabilities of any single organization. Achieving the roadmap’s vision will require the coordinated efforts of a multistakeholder consortium, and we invite those interested in shaping and driving that consortium to attend and contribute.   

The release event will include an overview of the roadmap, including the critical challenges and opportunities, as well as specific recommendations for research and development activities to begin building the needed Tools & Equipment, Standards & Techniques, and Training & Educational Resources.

To REGISTER and learn more about this in-person event, click here.

Please contact Nick Barendt nickbarendt@case.edu for additional information.

Download Roadmap

Download A Manufacturing Technology Roadmap for AI-Enhanced Multimodal Sensing of Materials and Processes for Complete Product Lifecycle Performance.

Roadmap Committee

This effort was lead and organized by:

• Nicholas Barendt
• Robert Gao
• John Lewandowski
• Kenneth Loparo

Nexight Group supported the overall roadmapping process and helped to prepare the roadmap document.

Mike Yost led consortium-related activities.

Roadmap Contributors

* = internal roadmap development team

Funding Acknowledgement

This work was performed under financial assistance award #70NANB22H049 from U.S. Department of Commerce, National Institute of Standards and Technology.