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

智能制造利用从多个传感器收集的数据，努力监控制造企业的各个方面——从单个机器级到工厂级。由此产生的效率可以将产品缺陷和制造成本降低25%以上。与增材制造相结合，智能制造有望改变美国工业。例如，目前航空航天工业使用减法加工制造一磅重的零件需要20磅的原材料。增材制造可以将所谓的采购-交货比从20:1降低到2:1，同时将交货时间从六个月缩短到一周。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的繁荣和福利。尽管有这些优势，但由于工艺不一致，行业对采用增材制造犹豫不决——零件可能存在未检测到的缺陷，例如气孔，这使得它们在关键任务应用中使用不安全。这个问题的一个潜在解决方案是一个名为智能增材制造的概念，它融合了智能制造和增材制造的理念。通过该学院早期职业发展计划（Career）奖，将利用过程中的传感器数据来了解激光粉末床熔融增材制造过程中发生的缺陷形成机制。高级数据分析方法结合了对缺陷演化的新的基本理解，将被用来实现健壮的“构建时正确”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业领域得到应用。该奖项还将促进基于发现的学习方法，让学习者在多个层面上动手探索增材制造。将启动与纳瓦霍技术大学的研究合作，以进一步扩大项目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智能增材制造框架，以缓解金属激光粉末床熔融增材制造中零件质量差的问题。成功将导致混合增材制造策略，在同一台机器内结合材料沉积（增材）和材料去除（减材）操作，可能产生零缺陷零件。该奖项解决的研究挑战包括：1)通过使用过程传感器实时隔离和量化潜在的过程现象，了解某些缺陷是如何以及为什么形成的；2)推进光谱图理论的数学，从异构传感器实时捕获缺陷——一个大数据问题；3)转发降阶模型，以理解物理热力学动力学，如层重熔和回流；当使用混合激光粉末床熔合来纠正缺陷时，就会出现这种情况。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.