CPS/Synergy/Collaborative Research: Smart Calibration Through Deep Learning for High-Confidence and Interoperable Cyber-Physical Additive Manufacturing Systems

CPS/协同/协作研究：通过深度学习进行智能校准，实现高可信度和可互操作的网络物理增材制造系统

• 批准号: 1544917
• 负责人: Qiang Huang
• 金额: $ 35万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544917&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Smart

Additive Manufacturing holds the promise of revolutionizing manufacturing. One important trend is the emergence of cyber additive manufacturing communities for innovative design and fabrication. However, due to variations in materials and processes, design and computational algorithms currently have limited adaptability and scalability across different additive manufacturing systems. This award will establish the scientific foundation and engineering principles needed to achieve adaptability, extensibility, and system scalability in cyber-physical additive manufacturing systems, resulting in high efficiency and accuracy fabrication. The research will facilitate the evolution of existing isolated and loosely-connected additive manufacturing facilities into fully functioning cyber-physical additive manufacturing systems with increased capabilities. The application-based, smart interfacing infrastructure will complement existing cyber additive communities and enhance partnerships between academia, industry, and the general public. The research will contribute to the technology and engineering of Cyber-physical Systems and the economic competitiveness of US manufacturing. This interdisciplinary research will generate new curricular materials and help educate a new generation of cybermanufacturing workforce. The research will establish smart and dynamic system calibration methods and algorithms through deep learning that will enable high-confidence and interoperable cyber-physical additive manufacturing systems. The dynamic calibration and re-calibration algorithms will provide a smart interfacing layer of infrastructure between design models and physical additive manufacturing systems. Specific research tasks include: (1) Establishing smart and fast calibration algorithms to make physical additive manufacturing machines adaptable to design models; (2) Deriving prescriptive compensation algorithms to achieve extensible design models; (3) Dynamic recalibration through deep learning for improved predictive modeling and compensation; and (4) Developing a smart calibration server and APP prototype test bed for scalable additive cyberinfrastructures.

添加剂制造具有革新制造业的希望。一个重要的趋势是用于创新设计和制造的网络添加剂制造社区的出现。但是，由于材料和过程的变化，设计和计算算法当前在不同的增材制造系统上的适应性和可伸缩性有限。该奖项将建立在网络物理添加剂制造系统中实现适应性，可扩展性和系统可伸缩性所需的科学基础和工程原则，从而实现高效率和准确性的制造。这项研究将促进现有的隔离和松散连接的添加剂制造设施的演变，以增加功能增加的网络物理添加剂制造系统。基于应用程序的智能接口基础架构将补充现有的网络添加剂社区，并增强学术界，工业和公众之间的伙伴关系。该研究将有助于网络物理系统的技术和工程以及美国制造业的经济竞争力。这项跨学科研究将生成新的课程材料，并帮助教育新一代的网络制造劳动力。这项研究将通过深度学习来建立智能和动态的系统校准方法和算法，这将使高信任和互操作的网络物理添加剂制造系统。动态校准和重新校准算法将在设计模型和物理增材制造系统之间提供基础架构的智能接口层。特定的研究任务包括：（1）建立智能和快速校准算法，以使设计模型适应物理添加剂制造机； （2）得出规定的补偿算法以实现可扩展的设计模型； （3）通过深度学习的动态重新校准，以改善预测性建模和补偿； （4）开发智能校准服务器和应用程序原型测试床，用于可扩展的添加节网络基础结构。