FMitF: Track I: Retargetable, Verifiable, Optimizable Computer-Aided Manufacturing

FMITF：第一轨：可重定向、可验证、可优化的计算机辅助制造

• 批准号: 2017927
• 负责人: Daniel Grossman
• 金额: $ 74.99万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017927&HistoricalAwards=false
• 关键词: FMitF; Track; Retargetable; Verifiable; Optimizable

This project applies and extends ideas from computer science formal methods to the design and fabrication of manufactured items. Computer-aided manufacturing promises to have transformative impacts on society for many reasons, including unprecedented ability for customization and low-volume manufacturing as well as more efficient and flexible fabrication plans (e.g., as material availability changes). But designers and engineers need more flexible tools that can optimize across both the design stage and the fabrication-planning stages, and be robust to changing constraints. This proposal’s novelties are foundations and prototypes that enable such tools, in particular by designing interfaces that are essentially programming languages in the computer science sense and amenable to automated program analysis for correctness and optimization. This proposal’s impacts include new languages and tools for computer-aided manufacturing as well as demonstrations that fabrication can be automatically retargeted to use different materials and/or processes.This project advances the state-of-the-art in both formal methods and computational fabrication, centered around the critical use of embedded systems. For computational fabrication, the project is organized around a central vision: manufacturing is a form of compilation that translates abstract geometric designs to equivalent concrete physical fabrication plans. By decomposing manufacturing software into compiler-like layers of abstraction, the research enables the construction of automated, reusable,cross-domain analyses, optimizations, and synthesis for fabrication. For formal methods, manufacturing introduces new challenges. It requires mixing computational and geometric reasoning, including constraints like materials that can be neither duplicated nor created from nothing, and that physical objects obey gravity and cannot occupy the same space as another object. Additional challenges include concurrency (multiple moving parts during fabrication) and numerical approximations as objects are moved in continuous space.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.

该项目应用并扩展了计算机科学形式方法的思想，以设计和制造产品。 由于许多原因，计算机辅助制造有望对社会产生变革性影响，包括前所未有的定制和小批量制造能力以及更有效和灵活的制造计划（例如，随着材料可用性的变化）。 但设计师和工程师需要更灵活的工具，可以在设计阶段和制造规划阶段进行优化，并对不断变化的约束保持鲁棒性。 这个建议的新颖性是基础和原型，使这样的工具，特别是通过设计接口，基本上是编程语言在计算机科学的意义上，并服从自动程序分析的正确性和优化。 该项目的影响包括计算机辅助制造的新语言和工具，以及制造可以自动重新定位以使用不同材料和/或工艺的演示。该项目推进了正式方法和计算制造的最新技术，围绕嵌入式系统的关键使用。对于计算制造，该项目是围绕一个中心愿景组织的：制造是一种编译形式，将抽象的几何设计转换为等效的具体物理制造计划。 通过将制造软件分解为类似编译器的抽象层，该研究能够构建自动化，可重用，跨域的分析，优化和合成制造。 对于正式方法，制造业带来了新的挑战。它需要混合计算和几何推理，包括限制，如材料既不能复制也不能从无到有创造，物理对象服从重力，不能与另一个对象占据相同的空间。 其他挑战包括并发性（制造过程中的多个移动部件）和物体在连续空间中移动时的数值近似。该奖项反映了NSF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。

项目成果

Robotic Jigsaws: Path Planning for Non-Holonomic Cutting Robots

机器人拼图：非完整切割机器人的路径规划

• 发表时间: 2021
• 期刊: IROS 2021
• 作者: Zhao, Haisen;Talwekar, Yash;Lan, Wenqing;Sharma, Chetan;Rus, Daniela;Schulz, Adriana;Lipton, Jeffrey I
• 通讯作者: Lipton, Jeffrey I

Generative Design of Sheet Metal Structures

• DOI: 10.1145/3592444
• 发表时间: 2023-07
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Amir Barda;Guy Tevet;Adriana Schulz;Amit H. Bermano

Fabrication‐Aware Reverse Engineering for Carpentry

• DOI: 10.1111/cgf.14375
• 发表时间: 2021-07
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: James Noeckel;Haisen Zhao;B. Curless;Adriana Schulz

Co-Optimization of Design and Fabrication Plans for Carpentry

• DOI: 10.1145/3508499
• 发表时间: 2021-07
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Haisen Zhao;Max Willsey;Amy Zhu;Chandrakana Nandi;Zach Tatlock;J. Solomon;Adriana Schulz