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

为了制造功能性、复杂性和集成性的产品，我们需要能够让工程师高效地探索包含几何、材料、电子、软件和制造工艺的设计空间的工具，而这些计算工具必须能够同时优化机械性能、重量和成本等相互冲突的目标。目前，专家和新手用户都要经过漫长的试错过程才能找到性能最好的设计，当工程师将设计师创造的概念转化为高性能产品时，一个关键的瓶颈就会出现，因为改进一个性能指标的设计变化可能会恶化另一个指标，因此不存在单一的最佳可能设计，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，可以有效地发现权衡的帕累托前沿，然后可以将其呈现给寻求沿多个轴优化设计性能的工程师和设计师。项目成果将为计算机辅助制造做出基础性贡献，并在创建不同类型对象的各种管道上进行演示。由此产生的方法既可以让工程师在更短的时间内创造出高性能的产品，又可以极大地提高产品本身的质量。通过本科生和高中生参与研究活动，这项工作将产生额外的广泛影响。为了实现该项目的目标，将开发新的方法来捕捉整个优化设计权衡空间，这些空间的形状通常是非线性的，甚至是脱节的。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，以显示哪些设计实现了所需的性能特征。然后，这些技术将被用来实现一个系统，该系统传达对设计权衡的直观理解，使工程师能够快速找到所需的解决方案或找到高效的方法来迭代设计规范。此外，对于需要基于组装的方法的复杂设计，将创建工具来迭代地约束和细化设计空间，直到工程师对组装的构件感到放心为止。该工作流程将允许工程师找到比人类或计算机单独找到的更好的解决方案。这一奖项反映了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。

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.