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

职业：智能增材制造 - 传感、数据科学和零件零缺陷建模的基础研究。

• 批准号: 1752069
• 负责人: Prahalada Rao
• 金额: $ 50万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1752069&HistoricalAwards=false
• 关键词: CAREER; Smart; Additive; Manufacturing; Fundamental

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.

Smart Manufacturing使用从多个传感器收集的数据来监视制造企业的各个方面 - 从单个机器水平到工厂级别。产生的效率可以将产品缺陷和制造成本降低超过25％。当与增材制造相结合时，智能制造有望改变美国行业。例如，目前需要使用20磅的原材料来使用减法加工为航空航天行业制造一磅重的零件。添加剂制造可以将所谓的购买与2：2：1至2：1降低，同时将交货时间从六个月减少到一个星期。实现这些潜在的制造业收益将通过提高美国先进制造竞争力来促进国家繁荣和福利。尽管有这些优势，但由于过程不一致而导致的行业犹豫不决地采用增材制造 - 零件可能没有发现的缺陷，例如孔隙率，这使得它们在关键任务应用程序中不安全。解决此问题的潜在解决方案是一个称为智能添加剂制造的概念，它将智能制造的想法与添加剂制造融合在一起。通过这个教师早期职业发展计划（Career）奖，将利用在激光粉床融合添加剂制造过程中发生的缺陷形成机制。将利用对缺陷进化的新基本理解的高级数据分析方法将被利用，以实现强大的正确构建方法。这项基础工作将在包括航空航天和国防在内的许多制造业领域找到应用。该奖项还将促进一种基于发现的学习方法，使学习者在多个层面上动手探索添加剂。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人士。该项目的研究目标是建立一个智能的添加剂制造框架，以减轻基于激光粉末融合融合的金属添加剂制造的差零件质量。成功将导致混合添加剂制造策略结合了材料沉积（添加剂）和材料去除（减法）在同一机器中的作用，可能会产生零缺失的部分。该奖项涉及的研究挑战包括：1）了解如何以及为何通过使用过程中传感器实时隔离和量化它们在实时实时发生的情况下形成某些缺陷，2）2）在实时的模型中捕获异性传感器的频谱图的数学来捕获缺陷，以捕获异性传感器，以及在实时 - 远程模型中，以及3）的动力学，以及3）远程序列的序列机构，以及3）远程机构，以及3）远程机构的序列机构，以及3）远程序列。反映，当混合激光粉末床融合纠正缺陷时，就会发生这种奖项。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Monitoring and Prediction of Porosity in Laser Powder Bed Fusion using Physics-informed Meltpool Signatures and Machine Learning

• DOI: 10.1016/j.jmatprotec.2022.117550
• 发表时间: 2022-03
• 期刊: Journal of Materials Processing Technology
• 影响因子: 6.3
• 作者: Z. Smoqi;A. Gaikwad;Ben Bevans;Md Humaun Kobir;J. Craig;Alan Abul-Haj;A. Peralta;Prahalada K. Rao

Application of hybrid laser powder bed fusion additive manufacturing to microwave radio frequency quarter wave cavity resonators

• DOI: 10.1007/s00170-022-10547-y
• 发表时间: 2022-11
• 期刊: The International Journal of Advanced Manufacturing Technology
• 作者: A. Riensche;P. Carriere;Z. Smoqi;A. Menendez;P. Frigola;S. Kutsaev;Aurora Araujo;N. Matavalam;Prahalada K. Rao

Layerwise In-Process Quality Monitoring in Laser Powder Bed Fusion

• DOI: 10.1115/msec2018-6477
• 发表时间: 2018-06
• 期刊: Volume 1: Additive Manufacturing; Bio and Sustainable Manufacturing
• 作者: Farhad Imani;A. Gaikwad;M. Montazeri;Prahalada K. Rao;Hui Yang;E. Reutzel

A Graph Theoretic Approach for Near Real-Time Prediction of Part-Level Thermal History in Metal Additive Manufacturing Processes

金属增材制造过程中零件级热历史近实时预测的图论方法

• DOI: 10.1115/msec2019-2875
• 发表时间: 2019
• 期刊: ASME Manufacturing Science and Engineering Conference
• 作者: Yavari, Reza;Cole, Kevin D.;Rao, Prahalad
• 通讯作者: Rao, Prahalad

Prediction of recoater crash in laser powder bed fusion additive manufacturing using graph theory thermomechanical modeling

• DOI: 10.1007/s40964-022-00331-5
• 发表时间: 2022-08-05
• 期刊: PROGRESS IN ADDITIVE MANUFACTURING
• 作者: Kobir, Md Humaun;Yavari, Reza;Rao, Prahalada
• 通讯作者: Rao, Prahalada