SBIR Phase I: Metal Additive Manufacturing Simulation on a Desktop

SBIR 第一阶段：桌面金属增材制造仿真

• 批准号: 1721342
• 负责人: Praveen Yadav
• 金额: $ 22.3万
• 依托单位: SciArt LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1721342&HistoricalAwards=false
• 关键词: SBIR; Phase; Metal; Additive; Manufacturing

This SBIR phase I project will provide a unique computational capability to additive manufacturing (AM) designers, engineers, and technicians for making better decisions with regards to metal 3D printing. Through AM, one can fabricate complex parts that were previously impossible to manufacture. Perfecting an AM process is however non-trivial, entailing time-consuming and expensive shop-floor experimentation. For example, fabricating a typical hand-sized component on a metal AM machine can take several hours to complete, costing thousands of dollars. Consequently, there is significant interest today to replace experiments with computer simulation. But AM simulations today require massively parallel computer clusters, such as those available only at national labs. This has largely deterred industrial progress and innovation. The proposed research has the potential to yield an algorithmically advanced AM simulation program that can run on standard desktop workstations without loss of accuracy. An environmental impact of this SBIR proposal is that by promoting virtual simulation of AM, the amount of wasted material will be minimized. The proposed research lies at the intersection of computational mechanics, and manufacturing. It proposes to develop a novel computational approach to additive manufacturing simulation that bypasses the memory bottleneck observed in traditional finite element analysis (FEA). By using a voxel representation where all elements are congruent (i.e. possess a rigid transformation symmetry) and have identical material properties, the memory requirements can be reduced dramatically and thereby speed up the simulation by orders of magnitude compared to traditional FEA software. Similar approach has been successfully demonstrated for problems in structural mechanics and topology optimization. The proposal aims to extend the approach to in-process AM simulation where G-code will be used to identify elements in a heat affected zone for laser and electron beam based processes, and the congruent elements will be identified for each phase in the process to reduce simulation time.

这个SBIR I期项目将为添加剂制造（AM）设计师，工程师和技术人员提供独特的计算能力，以在金属3D打印方面做出更好的决策。通过AM可以制造以前无法制造的复杂零件。然而，完善AM过程是不平凡的，需要耗时且昂贵的车间实验。例如，在金属AM机器上制造典型的手动组件可能需要几个小时才能完成，花费数千美元。因此，今天有很大的兴趣用计算机模拟代替实验。但是今天的AM模拟需要大量平行的计算机簇，例如仅在国家实验室提供的群集。这在很大程度上阻止了工业进步和创新。拟议的研究有可能产生算法高级AM模拟程序，该计划可以在标准桌面工作站上运行而不会丧失准确性。该SBIR提案的环境影响是，通过促进对AM的虚拟模拟，浪费材料的数量将被最小化。拟议的研究在于计算力学和制造的交集。它建议开发一种新型的计算方法来进行加性制造模拟，以绕过传统有限元分析（FEA）中观察到的记忆瓶颈。通过使用所有元素都是一致的体素表示（即具有刚性变换对称性）并具有相同的材料属性，与传统的FEA软件相比，可以大大减少内存需求，从而通过数量级来加快模拟。对于结构力学和拓扑优化的问题，已经成功证明了类似的方法。该提案旨在扩展到程序内AM模拟的方法，在该方法中，G代码将用于识别激光和电子束基过程中热影响区域中的元素，并且将在过程中为每个阶段确定一致的元素以减少模拟时间。