CAREER: Smart Additive Manufacturing - Fundamental Research in Sensing, Data Science,and Modeling Toward Zero Part Defects.

智能制造致力于使用从多个传感器收集的数据来监控制造企业的方方面面-从单个机器级别到工厂级别。由此产生的效率可以将产品缺陷和制造成本减少25%以上。当与添加剂制造相结合时，智能制造有望改变美国的工业。例如，目前使用减法加工为航空航天工业制造一磅重的零件需要20磅的原材料。添加剂制造可以将这种所谓的20：1的采购与空运比率降低到2：1，同时将交货期从六个月减少到一周。实现这些潜在的制造业收益将通过提高美国先进制造业的竞争力来促进国家繁荣和福利。尽管有这些优势，但由于工艺不一致，行业对采用Additive Manufacturing犹豫不决--零件可能有未被检测到的缺陷，如气孔，这使得它们在关键任务应用中使用不安全。这一问题的一个潜在解决方案是一个被称为智能加法制造的概念，它融合了智能制造和加法制造的思想。通过这项学院早期职业发展计划(CALEAR)奖，过程中的传感器数据将被用于了解激光粉床熔化添加剂制造过程中发生的缺陷形成机制。结合了对缺陷演变的新的基本理解的高级数据分析方法将被用来实现健壮的即构建即更正方法。这项基础性工作将在包括航空航天和国防在内的许多制造部门得到应用。该奖项还将促进以发现为基础的学习方法，使学习者在多个层次上参与添加剂制造的实践探索。将启动与纳瓦霍技术大学的研究合作，以进一步扩大项目的影响，并培训未来的先进制造业劳动力。本项目的研究目标是建立一个智能添加剂制造框架，以缓解激光粉床熔融金属添加剂制造中零件质量差的问题。成功将导致混合添加剂制造策略，在同一机器内结合材料沉积(添加剂)和材料去除(减法)操作，潜在地产生零缺陷零件。该奖项涉及的研究挑战包括：1)通过使用过程中传感器实时隔离和量化潜在的工艺现象，了解某些缺陷是如何以及为什么形成的；2)发展光谱图理论的数学，以实时捕获来自不同传感器的缺陷--这是一个大数据问题；3)转发降阶模型，以了解当缺陷通过混合激光粉床融合纠正时发生的物理热机械动力学，例如层重熔和回流。

Smart Manufacturing strives to monitor every aspect of the manufacturing enterprise - from the individual machine-level to the factory-level - using data gathered from multiple sensors. Resulting efficiencies can reduce product defects and manufacturing costs by over 25 percent. When coupled with Additive Manufacturing, Smart Manufacturing promises to transform U.S. industry. For example, 20 pounds of raw material are currently required to make a one-pound part for the aerospace industry using subtractive machining. Additive Manufacturing can reduce this so-called buy-to-fly ratio of 20:1 to 2:1, while simultaneously reducing lead time from six months to one week. Realization of these potential manufacturing gains will advance the national prosperity and welfare by increasing U.S. advanced manufacturing competitiveness. Despite these advantages, industries are hesitant to adopt Additive Manufacturing due to process inconsistency - parts may have undetected defects, such as porosity, that make them unsafe for use in mission-critical applications. A potential solution to this problem is a concept called Smart Additive Manufacturing, which melds the ideas of Smart Manufacturing with Additive Manufacturing. Through this Faculty Early Career Development Program (CAREER) award, in-process sensor data will be utilized to understand the mechanisms of defect formation occurring during the Laser Powder Bed Fusion Additive Manufacturing process. Advanced data analysis approaches that incorporate the new fundamental understanding of defect evolution will be leveraged to realize a robust correct-as-you-build methodology. This foundational work will find application across many manufacturing sectors including aerospace and defense. The award will also facilitate a discovery-based learning approach to engage learners in hands-on exploration of Additive Manufacturing at multiple levels. A research collaboration with Navajo Technical University will be initiated to further broaden project impact and train the advanced manufacturing workforce of the future. The research goal of this project is to establish a Smart Additive Manufacturing framework for alleviating the poor part quality in the Laser Powder Bed Fusion-based Additive Manufacturing of metals. Success will result in a hybrid Additive Manufacturing strategy that combines material deposition (additive) and material removal (subtractive) actions within the same machine potentially giving rise to zero-defect parts. The research challenges addressed by this award include: 1) understanding how and why certain defects are formed by isolating and quantifying the underlying process phenomena as they happen in real-time using in-process sensors, 2) advancing the mathematics of spectral graph theory to capture defects from heterogeneous sensors in real-time - a big data problem, and 3) forwarding reduced-order models to understand the physical thermomechanical dynamics, such as layer re-melting and reflow, that occur when defects are corrected with hybrid Laser Powder Bed Fusion.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.