2321728
Cooperative Agreement
Overview
Grant Description
SBIR Phase II: An Artificial Intelligence System for Autonomous Numerical Control Programming for Advanced Manufacturing - The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project includes an increase in efficiency and productivity in manufacturing supply chains, which can lead to economic growth, job creation, improved product quality, and reduced waste.
The project can also enhance the U.S. industrial base, which is critical to national security by mitigating manufacturing supply chain risks. This technology can provide new learning opportunities for students, facilitate increased partnership between academia and industry, and advance scientific knowledge on precision manufacturing, leading to the development of new artificial intelligence algorithms and techniques with applications beyond manufacturing.
The solution will be a step towards addressing the challenge of reshoring manufacturing given the technical skills gap crisis in the U.S. by helping increase the productivity of computer numerical control machinists and sparking greater interest in this field among new workforce entrants. The manufacturing landscape is shifting to more automation, and this solution could help train the next generation of artificial intelligence-augmented machinists.
This solution has broad applicability across commerce, government, and academia, in a range of end market applications such as aerospace, defense, and medtech. This SBIR Phase II project will result in a fully functional "beta" prototype of an artificial intelligence-assisted, autonomous, numerical control programming software that can be tested within an operational environment and be near-ready for commercial launch.
The end product will be an artificial intelligence-powered software embedded in the computer numerical control programmers' existing workflow environment. The software will provide machining strategy, cutting tool and machining parameters, and tool path recommendations across milling, drilling, and turning operations.
By offering these recommendations to the end user (i.e., the numerical control programmer), the product has the potential to: 1) shorten the learning curve for new talent, 2) reduce the degree of variability across skill levels, 3) reduce the time/iterations needed to generate computer numerical control programs, and 4) increase the probability of generating optimal (i.e., lowest overall machining cost) programs.
The product has the potential to significantly increase productivity of the existing and new workforce, while also reducing the non-recurring and recurring costs for precision machining. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. - Subawards are not planned for this award.
The project can also enhance the U.S. industrial base, which is critical to national security by mitigating manufacturing supply chain risks. This technology can provide new learning opportunities for students, facilitate increased partnership between academia and industry, and advance scientific knowledge on precision manufacturing, leading to the development of new artificial intelligence algorithms and techniques with applications beyond manufacturing.
The solution will be a step towards addressing the challenge of reshoring manufacturing given the technical skills gap crisis in the U.S. by helping increase the productivity of computer numerical control machinists and sparking greater interest in this field among new workforce entrants. The manufacturing landscape is shifting to more automation, and this solution could help train the next generation of artificial intelligence-augmented machinists.
This solution has broad applicability across commerce, government, and academia, in a range of end market applications such as aerospace, defense, and medtech. This SBIR Phase II project will result in a fully functional "beta" prototype of an artificial intelligence-assisted, autonomous, numerical control programming software that can be tested within an operational environment and be near-ready for commercial launch.
The end product will be an artificial intelligence-powered software embedded in the computer numerical control programmers' existing workflow environment. The software will provide machining strategy, cutting tool and machining parameters, and tool path recommendations across milling, drilling, and turning operations.
By offering these recommendations to the end user (i.e., the numerical control programmer), the product has the potential to: 1) shorten the learning curve for new talent, 2) reduce the degree of variability across skill levels, 3) reduce the time/iterations needed to generate computer numerical control programs, and 4) increase the probability of generating optimal (i.e., lowest overall machining cost) programs.
The product has the potential to significantly increase productivity of the existing and new workforce, while also reducing the non-recurring and recurring costs for precision machining. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. - Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH PHASE II (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE II", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23516
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Brentwood,
California
94513-2438
United States
Geographic Scope
Single Zip Code
Lambda Function was awarded
Cooperative Agreement 2321728
worth $999,942
from National Science Foundation in September 2023 with work to be completed primarily in Brentwood California United States.
The grant
has a duration of 2 years and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Cooperative Agreement was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase II Programs (SBIR/STTR Phase II).
SBIR Details
Research Type
SBIR Phase II
Title
SBIR Phase II:An artificial intelligence system for autonomous numerical control programming for advanced manufacturing
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project includes an increase in efficiency and productivity in manufacturing supply chains, which can lead to economic growth, job creation, improved product quality, and reduced waste. The project can also enhance the U.S. industrial base, which is critical to national security by mitigating manufacturing supply chain risks. This technology can provide new learning opportunities for students, facilitate increased partnership between academia and industry, and advance scientific knowledge on precision manufacturing, leading to the development of new artificial intelligence algorithms and techniques with applications beyond manufacturing. The solution will be a step towards addressing the challenge of reshoring manufacturing given the technical skills gap crisis in the U.S. by helping increase the productivity of computer numerical control machinists and sparking greater interest in this field among new workforce entrants. The manufacturing landscape is shifting to more automation, and this solution could help train the next generation of artificial intelligence-augmented machinists. This solution has broad applicability across commerce, government, and academia, in a range of end market applications such as aerospace, defense, and MedTech._x000D_ _x000D_ This SBIR Phase II project will result in a fully functional “beta” prototype of an artificial intelligence-assisted, autonomous, numerical control programming software that can be tested within an operational environment and be near-ready for commercial launch. The end product will be an artificial intelligence-powered software embedded in the computer numerical control programmers’ existing workflow environment. The software will provide machining strategy, cutting tool and machining parameters, and tool path recommendations across milling, drilling, and turning operations. By offering these recommendations to the end user (i.e., the numerical control programmer), the product has the potential to: 1) shorten the learning curve for new talent, 2) reduce the degree of variability across skill levels, 3) reduce the time / iterations needed to generate computer numerical control programs, and to 4) increase the probability of generating optimal (i.e., lowest overall machining cost) programs. The product has the potential to significantly increase productivity of the existing and new workforce, while also reducing the non-recurring and recurring costs for precision machining._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
M
Solicitation Number
NSF 23-516
Status
(Complete)
Last Modified 9/22/23
Period of Performance
9/15/23
Start Date
8/31/25
End Date
Funding Split
$999.9K
Federal Obligation
$0.0
Non-Federal Obligation
$999.9K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2321728
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
MWVEVFWYZ6D5
Awardee CAGE
8MZS1
Performance District
CA-10
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $999,942 | 100% |
Modified: 9/22/23