Ultra-high Precision Assembly of Aerospace Composite Structures: Fusing Physics-Based and Data-Driven Models

复合材料结构由于其高的强度重量比、良好的耐恶劣环境性和高的性能可靠性而越来越多地出现在航空航天和其他应用中。由于复合材料结构具有非线性、各向异性和柔性特性以及固有的制造可变性，用于金属结构组装的常规工艺建模和质量控制方法不足以用于复合材料构件。该奖项支持基础研究，整合物理引导和机器学习模型，以推进航空航天复合材料结构的超高精度组装。该研究涉及物理和数字产品连接的无缝集成，数据科学，高级统计和全面的制造知识。该研究具有潜在的减少材料损失，减少生产流程时间，实现高生产率和质量控制的航空航天制造。该项目的科学发现也可以扩展到其他复合材料需求行业，例如，汽车，航天器和太阳能，从而提高美国工业的全球竞争力。该项目的跨学科性质将为学生提供独特的教育和研究经验，并培养具有综合制造和数据分析能力的多元化和合格的劳动力。此外，该项目将为本科核心课程开发新的学习模块，招募和指导代表性不足的学生，提供工业短期课程，并开发用于精密装配的开源软件，所有这些都可能对社会产生深远的影响。这项研究旨在通过推进物理学，为大型复杂形状的复合材料结构的超高精度组装开发基础知识和变革性技术-引导机器学习具体的研究活动包括：（1）开发用于复合材料结构超高精度装配的数字孪生模型，（2）进行物理约束主动学习，安全探索和有效利用实验设计和预测建模，（3）研究稀疏机器学习，以获得复合材料结构装配中的最佳驱动策略，以及（4）分析方法的理论特性。该项目将研究小型航空关键结构和超高精度的大型碳纤维增强复合材料机身，例如，对于约5米的直径小于0.2毫米。这些方法将在验证和确认阶段进行计算和实验评估。研究成果将（1）扩大对工程驱动的数据分析和超高精度质量控制理论的科学理解，（2）弥合预测建模，主动学习，稀疏学习和复合材料结构组装之间的知识差距，并最终，（3）实现有效的全-该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响评审标准。

Composite structures have increasingly emerged for aerospace and other applications because of their high strength-to-weight ratio, good resistance to harsh environments, and great performance reliability. Because composite structures have nonlinear, anisotropic, and compliant properties as well as inherent manufacturing variability, conventional process modeling and quality control methodologies for metal structure assembling are not adequate to composite components. This award supports fundamental research that integrates physics-guided and machine-learning models to advance ultra-high precision assembly of aerospace composite structures. The research involves seamless integration of physical and digital product connections, data science, advanced statistics, and comprehensive manufacturing knowledge. The research has the potential to minimize the material loss, decrease the production flow time and achieve high productivity and quality control for aerospace manufacturing. The scientific findings from this project may also be extended to other composites demanding industries, e.g., automotive, spacecraft and solar energy, and thus, increase the global competitiveness of the U.S. industry. The interdisciplinary nature of this project will provide students with unique educational and research experiences and cultivate a diverse and qualified workforce cognizant with combined manufacturing and data analytics abilities. In addition, the project will develop new learning modules for an undergraduate core course, recruit and mentor underrepresented students, offer industrial short courses, and develop open-source software for precision assembly, all potentially leading to profound impacts to the society.This research aims to develop fundamental knowledge and transformative technologies for ultra-high precision assembly of large complex-shaped composite structures by advancing physics-guided machine learning. Specific research activities include: (1) developing digital twin for ultra-high precision assembly of composite structures, (2) conducting physics-constrained active learning with safe exploration and efficient exploitation for experimental design and predictive modeling, (3) studying sparse machine learning for an optimal actuating strategy in composite structures assembly, and (4) analyzing theoretical properties of the methodologies. The project will research small-sized key aerospace structures as well as large-scale carbon fiber reinforced composite fuselage of ultra-high precision, e.g., less than 0.2 mm for a diameter of about 5 m. The methodologies will be computationally and experimentally evaluated in the verification and validation phase. The research outcomes will (1) expand the scientific understanding of engineering-driven data analytics and ultra-high precision quality control theory, (2) bridge the knowledge gap between predictive modeling, active learning, sparse learning, and composite structures assembly, and ultimately, (3) realize the effective all-inclusive integration of machine learning methodologies with advanced manufacturing.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.