SBIR Phase I: Methods for Embedding User Data into 3D Generative AI Computer-aided-Design Models

这个小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是开发一种新颖的人工智能驱动的生成设计解决方案，该解决方案能够通过利用当前可用的丰富数据（社交媒体，使用情况，遥测）来满足工业和消费品制造商的需求。拟议的框架将为美国设计和制造公司创造新的机会，使其产品更好地满足快速变化的消费者需求，同时减少目前存在的产品开发挑战。它还可以加快产品开发的周期，并将消费者情绪近乎实时地纳入产品设计。提出的计算方法将把消费者的数字洞察力转化为提高设计概念质量的新方法，并将消费者视角的多样性纳入人工智能生成的设计概念，从而提高设计师创新具有社会意识、以消费者为中心的产品的能力。该项目将促进设计新颖、有效和高效的设计模型，增强设计师的创造力，促进设计师与人工智能的共同创造和减少偏见，并弥合消费者需求发现、卓越设计（DFX）工程和社会影响之间的差距。这对几乎所有行业和应用程序都有影响。这个小企业创新研究（SBIR）第一阶段项目将通过将定性和定量信息集成到生成人工智能模型中，实现三维生成设计能力的代际飞跃，从而有效地生产新颖的设计。主要目标是开发一个可测试的演示器，用于将消费者数据、物联网（IoT）数据和卓越设计（DFX）工程规范融合到3D几何数据中。第一阶段项目将重点探索自然语言处理、生成建模和数据融合模型的新方法，将消费者数据和技术需求与基于物联网的遥测数据相结合，为产品设计绘制推论，并建立新的半监督模型，将这些数据输入注入3D CAD生成模型。该项目将确定如何直接将消费者需求与功能性能联系起来，并研究3D生成设计的通用性在现实世界中的有效性和效率收益。该项目将解决当前生成设计解决方案的几个挑战，包括将定性和定量元数据转换为概念，自动生成设计的控制和迭代，以及将其无缝集成到制造过程和工作流程中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.