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.

廉价的3D打印机，编织机，激光切割机，刺绣机和其他台式机制造设备的广泛可用性有可能在创建定制，特定于人的制造商的创建方面革命。但是，设计用于制造的工件目前需要大量的专业知识以及使用高级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指导搜索算法将设计的力量掌握在最终用户的手中。该项目的框架将使域专家甚至最终用户能够创建特定于域的设计工具，而无需任何编程专业知识。例如，临床医生可能会使用该项目的框架来制作一种工具，以生成适合特定手动伤害患者需求的自定义手动拼图。研究团队将重点关注可访问性设备的重要领域，物理对象，以满足残疾人的各种可访问性需求以及相关的健康问题。该项目的工作将与设计研讨会的最终合作，与使用移动设备（座位和站立）的残疾人社区的利益相关者。该项目的好处包括可能使许多具有不同能力的人能够找到相当于定制解决方案的情况，而无需定制设计。域专家快速创建生成设计工具的能力，以更广泛地解决最终用户的定制和特定产品需求，以实现可访问性，产品开发和消费者经济的变革性。要将基于优化的搜索置于最终用户的手中，该项目将使域专家无需编程专业知识即可使用该项目的新型框架，而无需编程专业知识，可以使用该项目的三个关键组件，使用该项目的新型框架框架，Metamorph，Metamorph，Metamorph，Metamorph。研究团队将在整个工作中强调可解释性。首先，域专家需要能够描述应搜索其创建的生成设计工具的设计空间。尽管Metamorph是域的不可知论，但该项目的评估将集中在数字制造域上。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。研究团队将开发出可视化设计空间的新方法，以支持域专家的验证。其次，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的指标。作为这项工作的一部分，研究团队将为域专家开发第一个可参数化优化指标的数据库，以及让用户选择，参数化和构成或以示例进行教学的界面。通过将指标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，例如学习和组成，并将强调解释性在将结果呈现给域专家时。最后，该项目将支持域专家和最终用户选择适当的优化算法验证和验证Metamorph生产的特定领域特定设计工具。该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，然后进行迭代设计，以实施和测试这项工作所告知的方法。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来支持的。