SBIR Phase I: Machine learning-powered simulation of additive manufacturing for real-time design and process optimization

SBIR 第一阶段：基于机器学习的增材制造仿真，用于实时设计和流程优化

• 批准号: 2151667
• 负责人: Runze Huang
• 金额: $ 25.59万
• 依托单位: EXLATTICE, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151667&HistoricalAwards=false
• 关键词: SBIR; Phase; Machine; learning; powered

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to accelerate larger-scale adoption of additive manufacturing (AM) through ultrafast engineering simulation software. The AM industry was worth $12.6 billion in 2020 and holds great potential in providing advanced designs and enabling distributed supply chains for the US aerospace, medical, and automotive industries. However, AM is facing slow adoption due to trial and error processes casued by the lack of an efficient and reliable engineering workflow. The proposed ultrafast simulation technology may provide real-time predictions of possible manufacturing issues for AM parts in the design phase, thereby reducing manufacturing failures and prototyping. The project also seeks to generate systematic knowledge of how machine learning can help overcome some long-lasting fundamental challenges in scientific computing and help advance engineering software used for digital manufacturing. This Small Business Innovation Research (SBIR) Phase I project integrates machine learning with finite element methods (FEM) to develop a proof-of-concept for 3-5 orders of magnitude faster process simulation software for AM used to predict manufacturing failures due to high temperature, residual distortion, and residual stresses. The traditional computation method for part-scale AM simulation takes hours to days and relies on an iterative, layer-wise approach. The proposed project seeks to replace the most time-consuming steps in the traditional simulation method with deep learning and implement a one-step approach. The proposed hybrid data-driven plus physical simulation framework includes the development a feature-driven, deep learning model and a process parameter-based transfer learning model, and coupling these models with the finite element method. The project also aims to apply and benchmark hybrid datasets from AM physical modelling, three-dimentional (3D) scanning of manufactured parts, and in-situ monitoring for training and model scalability. The team seeks to demonstrate technological advantages through pilot testing with streamlined user interfaces and application programming interfaces (APIs) developed in this project.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响是通过Ultrafast工程模拟软件加速大规模采用增材制造（AM）。 AM行业在2020年价值126亿美元，在提供高级设计并为美国航空，医疗和汽车行业提供分布式供应链方面具有巨大潜力。但是，由于缺乏高效且可靠的工程工作流程而导致的试验和错误过程，AM面临缓慢的采用。拟议的超快模拟技术可能会在设计阶段为AM零件的可能制造问题提供实时预测，从而减少制造故障和原型制造。该项目还试图对机器学习如何帮助克服科学计算中的一些持久基本挑战，并帮助用于数字制造的高级工程软件。这项小型企业创新研究（SBIR）I阶段项目将机器学习与有限元方法（FEM）集成在一起，以开发3-5个数量级的概念验证，用于预测由于高温，残留失真和残留应力而导致的制造故障的3-5个数量级。用于零件尺度AM模拟的传统计算方法需要数小时到几天，并依赖于迭代，层次的方法。拟议的项目旨在通过深度学习和实施一步方法来代替传统仿真方法中最耗时的步骤。拟议的混合数据驱动以及物理模拟框架包括开发功能驱动的，深度学习模型和基于过程参数的传输学习模型，并将这些模型与有限元方法耦合。该项目还旨在从AM物理建模，制造零件的三维（3D）扫描以及现场监控中应用和基准基准混合数据集，以供培训和模型可伸缩性。该团队试图通过试点测试来证明技术的优势，该测试具有该项目中开发的简化用户界面和应用程序编程界面（API）。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优点和更广泛影响的评估标准来通过评估来获得支持的。