Advances in CAD and CAM have enabled engineers and design teams to digitally design parts with unprecedented ease. Software solutions now come with a range of modules for optimizing designs for performance requirements, generating instructions for manufacturing, and digitally tracking the entire process from design to procurement in the form of product life-cycle management tools. However, existing solutions force design teams and corporations to take a primarily serial approach where manufacturing and procurement decisions are largely contingent on design, rather than being an integral part of the design process. In this work, we propose a new approach to part making where design, manufacturing, and supply chain requirements and resources can be jointly considered and optimized. We present the Generative Manufacturing compiler that accepts as input the following: 1) An engineering part requirements specification that includes quantities such as loads, domain envelope, mass, and compliance, 2) A business part requirements specification that includes production volume, cost, and lead time, 3) Contextual knowledge about the current manufacturing state such as availability of relevant manufacturing equipment, materials, and workforce, both locally and through the supply chain. Based on these factors, the compiler generates and evaluates manufacturing process alternatives and the optimal derivative designs that are implied by each process, and enables a user guided iterative exploration of the design space. As part of our initial implementation of this compiler, we demonstrate the effectiveness of our approach on examples of a cantilever beam problem and a rocket engine mount problem and showcase its utility in creating and selecting optimal solutions according to the requirements and resources.

翻译：CAD与CAM技术的进步使工程师和设计团队能够以前所未有的便捷性进行零件的数字化设计。当前的软件解决方案已配备一系列模块，可用于根据性能需求优化设计、生成制造指令，并通过产品生命周期管理工具以数字化形式追踪从设计到采购的全过程。然而，现有解决方案迫使设计团队与企业主要采用串行化方法，即制造与采购决策在很大程度上取决于设计方案，而非作为设计过程的有机组成部分。本研究提出一种新的零件制造方法，能够协同考量并优化设计、制造及供应链的需求与资源。我们提出生成式制造编译器，其输入包括：1）工程零件需求规格，涵盖载荷、域空间、质量与合规性等参数；2）商业零件需求规格，包括生产批量、成本与交付周期；3）关于当前制造状态的背景知识，如本地及供应链中相关制造设备、材料与人力资源的可用性。基于这些要素，该编译器生成并评估制造工艺备选方案，推导各工艺对应的最优衍生设计，支持用户在引导下对设计空间进行迭代探索。作为该编译器的初步实现，我们通过悬臂梁问题与火箭发动机支架问题的案例，展示了该方法在根据需求与资源创建并选择最优解决方案方面的有效性与实用价值。