2335491
Project Grant
Overview
Grant Description
Sbir Phase I: Methods for Embedding User Data into 3D Generative AI Computer-Aided-Design Models -The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the development of a novel artificial-intelligence-powered generative design solution that is able to address the needs of industrial and consumer-product manufacturers by exploiting the abundance of data (social media, usage, telemetry) currently available. The proposed framework will create new opportunities for American design and manufacturing firms to better align their products with rapidly evolving consumer needs while reducing the product development challenges that currently exist.
It also enables faster cycle times for product development and the near-real-time inclusion of consumer sentiment into product design. The proposed computational methods will translate consumers? digital insight into new ways to increase the quality of the design concepts and the diversity of consumer perspectives incorporated into AI-generated design concepts, thereby enhancing the designers? ability to innovate socially aware, consumer-centric products. This project will foster the design of novel, effective, and efficient design models, augment designers? creativity, promote designer-AI co-creation and bias mitigation, and bridge the gap between consumer-needs discovery, design for excellence (DFX) engineering, and social impact.
This has ramifications for nearly every industry and application. This Small Business Innovation Research (SBIR) Phase I project will enable a generational leap in three-dimensional generative design capabilities by integrating qualitative and quantitative information into generative AI models for the efficient production of novel designs. The primary objective is to develop a testable demonstrator for fusing consumer data, data from the Internet of Things (IoT), and design for excellence (DFX) engineering specifications into 3D geometric data. The Phase I project will focus on exploring new methods for natural language processing, generative modeling, and data fusion models to integrate consumer data and technical requirements with IoT-based telemetric data, drawing inferences for product design, and building novel semi-supervised models to inject these data inputs into 3D CAD generative models.
The project will determine how to directly connect consumer needs with functional performance and study the real-world effectiveness and efficiency gains from the generalizability of 3D generative design. The project will address several challenges of current generative design solutions, including the translation of qualitative and quantitative metadata into concepts, the control and iteration of automatically generated designs, and their seamless integration into manufacturing processes and workflows. 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.
It also enables faster cycle times for product development and the near-real-time inclusion of consumer sentiment into product design. The proposed computational methods will translate consumers? digital insight into new ways to increase the quality of the design concepts and the diversity of consumer perspectives incorporated into AI-generated design concepts, thereby enhancing the designers? ability to innovate socially aware, consumer-centric products. This project will foster the design of novel, effective, and efficient design models, augment designers? creativity, promote designer-AI co-creation and bias mitigation, and bridge the gap between consumer-needs discovery, design for excellence (DFX) engineering, and social impact.
This has ramifications for nearly every industry and application. This Small Business Innovation Research (SBIR) Phase I project will enable a generational leap in three-dimensional generative design capabilities by integrating qualitative and quantitative information into generative AI models for the efficient production of novel designs. The primary objective is to develop a testable demonstrator for fusing consumer data, data from the Internet of Things (IoT), and design for excellence (DFX) engineering specifications into 3D geometric data. The Phase I project will focus on exploring new methods for natural language processing, generative modeling, and data fusion models to integrate consumer data and technical requirements with IoT-based telemetric data, drawing inferences for product design, and building novel semi-supervised models to inject these data inputs into 3D CAD generative models.
The project will determine how to directly connect consumer needs with functional performance and study the real-world effectiveness and efficiency gains from the generalizability of 3D generative design. The project will address several challenges of current generative design solutions, including the translation of qualitative and quantitative metadata into concepts, the control and iteration of automatically generated designs, and their seamless integration into manufacturing processes and workflows. 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 (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding Agency
Place of Performance
Boston,
Massachusetts
02115-3134
United States
Geographic Scope
Single Zip Code
ADA Tech was awarded
Project Grant 2335491
worth $275,000
from in March 2024 with work to be completed primarily in Boston Massachusetts United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Methods for Embedding User Data into 3D Generative AI Computer-aided-Design Models
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the development of a novel Artificial-Intelligence-powered generative design solution that is able to address the needs of industrial and consumer-product manufacturers by exploiting the abundance of data (social media, usage, telemetry) currently available. The proposed framework will create new opportunities for American design and manufacturing firms to better align their products with rapidly evolving consumer needs while reducing the product development challenges that currently exist. It also enables faster cycle times for product development and the near-real-time inclusion of consumer sentiment into product design. The proposed computational methods will translate consumers’ digital insight into new ways to increase the quality of the design concepts and the diversity of consumer perspectives incorporated into AI-generated design concepts, thereby enhancing the designers’ ability to innovate socially aware, consumer-centric products. This project will foster the design of novel, effective, and efficient design models, augment designers’ creativity, promote designer-AI co-creation and bias mitigation, and bridge the gap between consumer-needs discovery, Design for Excellence (DFX) engineering, and social impact. This has ramifications for nearly every industry and application.
This Small Business Innovation Research (SBIR) Phase I project will enable a generational leap in three-dimensional generative design capabilities by integrating qualitative and quantitative information into generative AI models for the efficient production of novel designs. The primary objective is to develop a testable demonstrator for fusing consumer data, data from the Internet of Things (IoT), and Design for Excellence (DFX) engineering specifications into 3D geometric data. The Phase I project will focus on exploring new methods for natural language processing, generative modeling, and data fusion models to integrate consumer data and technical requirements with IoT-based telemetric data, drawing inferences for product design, and building novel semi-supervised models to inject these data inputs into 3D CAD generative models. The project will determine how to directly connect consumer needs with functional performance and study the real-world effectiveness and efficiency gains from the generalizability of 3D generative design. The project will address several challenges of current generative design solutions, including the translation of qualitative and quantitative metadata into concepts, the control and iteration of automatically generated designs, and their seamless integration into manufacturing processes and workflows.
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
AI
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 5/6/24
Period of Performance
3/1/24
Start Date
2/28/25
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2335491
Additional Detail
Award ID FAIN
2335491
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
KUGFE2DLNNE9
Awardee CAGE
9L6H6
Performance District
MA-07
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Modified: 5/6/24