STTR Phase I: A Next-Generation Computationally Efficient Software for Simulating Additive Manufacturing Processes of Metals

STTR 第一阶段：用于模拟金属增材制造工艺的下一代计算高效软件

• 批准号: 2112175
• 负责人: Ajit Achuthan
• 金额: $ 25.6万
• 依托单位: Additive Manufacturing Innovations LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112175&HistoricalAwards=false
• 关键词: STTR; Phase; Next; Generation; Computationally

The broader impact of this Small Technology Transfer Research (STTR) Phase I project is to advance simulation software with high computational speed and efficiency for small-scale manufacturing. The proposed technology will perform simulations faster (from days to hours) using a fraction of current resources (from clusters to desktop), while still capturing the underlying physics accurately. Such a powerful tool will serve as a platform that facilitates more innovations in the field of additive manufacturing, bringing paradigm changes in part design and material development. This will bring efficiency improvements of 50-100x to many industrial sectors. This STTR Phase I project addresses low computational efficiency of existing manufacturing process simulation models due to the finite element analysis (FEA) framework on which the models rely. The proposed innovation consists of three novel concepts: a new FEA framework called additive FEA (AFEA) framework, and two methods called Topologically Conformal Regenerative Discretization (TCRD) and Hybrid Modeling (HM). The proposed project will advance these techniques for computational simulation of AM processes for metals, engineering them for translation at industrial scale.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.

这项小型技术转移研究（STTR）I阶段项目的更广泛影响是在小规模制造中推进具有高计算速度和效率的模拟软件。 所提出的技术将使用当前资源（从集群到桌面）更快地（几天到数小时）进行仿真，同时仍准确地捕获基础物理。如此强大的工具将用作一个平台，可以促进添加剂制造领域的更多创新，从而在部分设计和材料开发中变化。这将为许多工业部门带来50-100x的效率提高。该STTR I阶段项目解决了由于模型所依赖的有限元分析（FEA）框架，现有制造过程仿真模型的计算效率较低。提出的创新由三个新颖概念组成：一个称为添加剂FEA（AFEA）框架的新FEA框架，以及两种称为拓扑结构复原再生离散化（TCRD）和混合模型（HM）的方法。 拟议的项目将推进这些技术，以用于对金属的AM流程进行计算模拟，并在工业规模上进行工程翻译。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估来支持的。