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

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、个性化制造的创造革命。然而，目前为制造业设计人工制品需要大量的专业知识和使用先进的计算机辅助设计工具的能力。该项目寻求将设计的力量交到最终用户手中，使用生成性人工智能和引导式搜索算法。该项目的框架将使领域专家，甚至最终用户，在没有任何编程专业知识的情况下创建特定于领域的设计工具。例如，临床医生可以使用该项目的框架来制作一种工具，用于生成定制的手夹板，以满足特定手部损伤患者的需求。研究团队将专注于无障碍设备这一重要领域，即解决残疾人各种无障碍需求和相关健康问题的实物。该项目的工作最终将是与使用移动设备(坐式和站式)的残疾人社区的利益攸关方举行设计研讨会。这个项目的好处包括潜在地使许多不同能力的人能够在没有定制设计成本的情况下找到相当于定制的解决方案。领域专家能够快速创建生成性设计工具，以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到最终用户手中，该项目将使没有编程专业知识的领域专家能够使用项目的新框架Metamorph来指定生成性设计的三个关键组件。研究小组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式设计工具应该在其上搜索的设计空间。虽然变形是领域不可知的，但该项目的评估将集中在数字制造领域。因此，该项目将支持一系列设计表现形式，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。研究小组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的指标。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的优化度量数据库，并开发一个界面，供用户选择、参数化和组合它们，或通过实例进行教学。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指标规范，如学习和组合，并将在向领域专家返回结果时强调可解释性。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，以验证和验证Metamorph产生的最终特定于领域的设计工具。这项任务将首先使用定性的方法来了解优化空间中可解释性的最佳方法，然后进行迭代设计来实施和测试受此工作启发的方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.