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

加法制造有望给制造业带来革命性的变化。一个重要的趋势是出现了用于创新设计和制造的网络添加剂制造社区。然而，由于材料和工艺的不同，设计和计算算法目前在不同的添加剂制造系统中的适应性和可扩展性有限。该奖项将确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，从而实现高效率和高精度的制造。这项研究将促进现有孤立和松散连接的添加剂制造设施演变为具有更高能力的全功能网络物理添加剂制造系统。基于应用的智能接口基础设施将补充现有的网络添加剂社区，并加强学术界、产业界和公众之间的伙伴关系。这项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。这种跨学科的研究将产生新的课程材料，并有助于培养新一代网络制造劳动力。这项研究将通过深度学习建立智能和动态的系统校准方法和算法，使高置信度和可互操作的网络物理添加剂制造系统成为可能。动态校准和重新校准算法将在设计模型和物理添加剂制造系统之间提供智能接口层的基础设施。具体研究任务包括：(1)建立智能、快速的校准算法，使物理添加剂制造设备能够适应设计模型；(2)推导规定的补偿算法，实现可扩展的设计模型；(3)通过深度学习进行动态重新校准，以改进预测建模和补偿；(4)为可扩展的添加剂网络故障开发智能校准服务器和APP原型试验台。

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 cyberinfractures.