Collaborative Research: HCC: Small: End-User Guided Search and Optimization for Accessible Product Customization and Design

协作研究：HCC：小型：最终用户引导的搜索和优化，以实现无障碍产品定制和设计

• 批准号: 2327136
• 负责人: Jennifer Mankoff
• 金额: $ 50万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327136&HistoricalAwards=false
• 关键词: Collaborative; Research; HCC; Small; End

The widespread availability of inexpensive 3D printers, knitting machines, laser cutters, embroidery machines, and other desktop manufacturing devices has the potential to drive a revolution in the creation of bespoke, person-specific manufacturing. However, designing artifacts for manufacturing currently requires a great deal of expertise and the ability to use advanced computer aided design tools. This project seeks to put the power of design in the hands of end users using generative artificial intelligence and guided search algorithms. This project's framework will enable domain experts, and even end users, to create domain-specific design tools without any programming expertise. For example, a clinician might use the project's framework to make a tool for generating custom hand-splints that fit the needs of specific patients with hand injuries. The research team will focus on the important domain of accessibility devices, physical objects that address the varied accessibility needs for people with disabilities and related health concerns. The project's work will culminate with a design workshop with stakeholders from the disability community who use mobility devices (seated and standing). The benefits of this project include potentially enabling many people of different abilities to find the equivalent of bespoke solutions without the costs of custom design. The ability for domain experts to rapidly create generative design tools that address custom and specific product needs for end users would be transformative for accessibility, product development, and the consumer economy more broadly.To put optimization-based search in the hands of end users, the project will enable domain experts without programming expertise to specify three critical components of generative design using the project's novel framework, Metamorph. The research team will emphasize interpretability throughout the work. First, domain experts need to be able to describe the design space over which the generative design tool they are creating should search. While Metamorph is domain agnostic, the project's evaluations will focus on digital fabrication domains. Thus, the project will support a range of design representations including computer aided design and learned embeddings created using deep learning tools. The research team will develop novel approaches for visualizing the design space to support verification by domain experts. Second, domain experts need to be able to describe the metrics of the search in terms of both the design space and any end user input. As part of this effort, the research team will develop the first database of parameterizable optimization metrics for domain experts, and an interface for letting the user select, parameterize, and compose them, or teach by example. By treating metric specification as an end-user programming problem, the research team can apply new techniques to metric specification, such as learning and composition, and will emphasize interpretability in presenting the results back to domain experts. Finally, the project will support domain experts and end users in selecting appropriate optimization algorithms validating and verifying the resulting domain-specific design tool that Metamorph produces. This task will involve first using qualitative methods to understand the best approaches to interpretability in the optimization space, followed by iterative design to implement and test approaches informed by this work.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.

廉价 3D 打印机、针织机、激光切割机、刺绣机和其他桌面制造设备的广泛使用有可能推动定制、个性化制造的革命。然而，目前的制造设计工件需要大量的专业知识和使用先进计算机辅助设计工具的能力。该项目旨在使用生成人工智能和引导搜索算法将设计的力量交给最终用户。该项目的框架将使领域专家甚至最终用户能够创建特定于领域的设计工具，而无需任何编程专业知识。例如，临床医生可以使用该项目的框架来制作一种工具，用于生成适合手部受伤的特定患者需求的定制手夹板。研究团队将重点关注无障碍设备、满足残疾人各种无障碍需求和相关健康问题的物理对象这一重要领域。该项目的工作将以与来自使用移动设备（坐式和站式）的残疾人社区的利益相关者参加的设计研讨会结束。该项目的好处包括可能使许多不同能力的人能够找到等效的定制解决方案，而无需定制设计的成本。领域专家能够快速创建生成设计工具来满足最终用户的定制和特定产品需求，这将对可访问性、产品开发和更广泛的消费经济产生变革。为了将基于优化的搜索交给最终用户，该项目将使没有编程专业知识的领域专家能够使用该项目的新颖框架 Metamorph 指定生成设计的三个关键组成部分。研究团队将在整个工作中强调可解释性。首先，领域专家需要能够描述他们正在创建的生成设计工具应该搜索的设计空间。虽然 Metamorph 与领域无关，但该项目的评估将侧重于数字制造领域。因此，该项目将支持一系列设计表示，包括计算机辅助设计和使用深度学习工具创建的学习嵌入。研究团队将开发新的方法来可视化设计空间，以支持领域专家的验证。其次，领域专家需要能够根据设计空间和任何最终用户输入来描述搜索指标。作为这项工作的一部分，研究团队将为领域专家开发第一个可参数化优化指标数据库，以及一个让用户选择、参数化和组合它们或通过示例进行教学的界面。通过将度量规范视为最终用户编程问题，研究团队可以将新技术应用于度量规范，例如学习和组合，并将强调将结果呈现给领域专家时的可解释性。最后，该项目将支持领域专家和最终用户选择适当的优化算法，验证和验证 Metamorph 生成的特定领域设计工具。这项任务将首先使用定性方法来了解优化空间中可解释性的最佳方法，然后通过迭代设计来实施和测试这项工作所提供的方法。该奖项反映了 NSF 的法定使命，并通过使用评估方法被认为值得支持。基金会的智力价值和更广泛的影响审查标准。