CHS: Medium: Collaborative Research: Discovery and Exploration of Design Trade-Offs

CHS：媒介：协作研究：设计权衡的发现和探索

• 批准号: 1954028
• 负责人: Adriana Schulz
• 金额: $ 12.5万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1954028&HistoricalAwards=false
• 关键词: CHS; Medium; Collaborative; Research; Discovery

To build functional, complex, and integrated products, we need tools that allow engineers to efficiently explore a design space that encompasses geometry, materials, electronics, software, and fabrication processes, and these computational tools must be able to simultaneously optimize conflicting objectives such as mechanical performance, weight, and cost. Currently, experts and novice users alike go through a long process of trial-and-error to identify top-performing designs, and a critical bottleneck then occurs when engineers convert concepts created by designers into high-performance products because design variations that improve one performance metric may worsen another, so that there is no single best possible design but rather a set of "Pareto optimal" designs that reflect different trade-offs. This research will create algorithms that can discover the Pareto front of trade-offs efficiently and can then present it to engineers and designers seeking to optimize the performance of a design along more than one axis. Project outcomes will make foundational contributions to computer-aided manufacturing, with demonstrations on a variety of pipelines for creating different types of objects. The resulting methods will both allow engineers to create high-performing products in less time while also dramatically improving the quality of the products themselves. The work will have additional broad impact through involvement of both undergraduate and high school students in the research activities.To achieve the project's goals, novel methods will be developed for capturing the full space of optimal design trade-offs whose shape is typically nonlinear and even disconnected. These methods will be driven by probabilistic analysis in the context of multi-objective stochastic optimization. Efficient representations and algorithms for exploring trade-offs with real-time feedback, showing which designs achieve desired performance characteristics, will also be defined. These techniques will then be used to implement a system that conveys an intuitive understanding of design trade-offs, allowing engineers to quickly discover desired solutions or find efficient ways to iterate on the design specifications. In addition, for complex designs requiring assembly-based approaches, tools will be created for iteratively constraining and refining the design space until the engineer feels confident with the assembled artifact. The workflow will allow engineers to find solutions that are better than what a human or a computer alone would find.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.

为了构建功能强大、复杂和集成的产品，我们需要能够让工程师有效探索包括几何、材料、电子、软件和制造工艺在内的设计空间的工具，这些计算工具必须能够同时优化相互冲突的目标，如机械性能、重量和成本。 目前，专家和新手用户都经历了一个漫长的试错过程，以确定性能最好的设计，当工程师将设计师创建的概念转换为高性能产品时，就会出现一个关键的瓶颈，因为提高一个性能指标的设计变化可能会恶化另一个，因此没有单一的最佳设计，而是一组反映不同权衡的“帕累托最优”设计。 这项研究将创建算法，可以有效地发现帕累托前沿的权衡，然后可以将其呈现给工程师和设计师寻求优化性能的设计沿着多个轴。 项目成果将为计算机辅助制造做出基础性贡献，并展示用于创建不同类型对象的各种管道。 由此产生的方法将使工程师能够在更短的时间内创造出高性能的产品，同时也大大提高了产品本身的质量。 这项工作将通过本科生和高中生参与研究活动产生额外的广泛影响。为了实现项目的目标，将开发新的方法来捕获最佳设计权衡的整个空间，其形状通常是非线性的，甚至是断开的。 这些方法将由多目标随机优化背景下的概率分析驱动。 还将定义用于探索与实时反馈的权衡的有效表示和算法，显示哪些设计实现了所需的性能特性。 然后，这些技术将用于实现一个系统，传达对设计权衡的直观理解，使工程师能够快速发现所需的解决方案或找到有效的方法来验证设计规范。 此外，对于需要基于装配的方法的复杂设计，将创建用于迭代约束和细化设计空间的工具，直到工程师对装配的工件感到自信为止。 该工作流程将使工程师能够找到比人类或计算机更好的解决方案。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。