GOALI: Online Defect Detection and Mitigation Method for Incipient Anomalies in Additive Manufacturing Processes

GOALI：增材制造过程中初期异常的在线缺陷检测和缓解方法

• 批准号: 1436592
• 负责人: Zhenyu Kong
• 金额: $ 30万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1436592&HistoricalAwards=false
• 关键词: GOALI; Online; Defect; Detection; Mitigation

Additive manufacturing offers significant advantages over conventional manufacturing, with potential to fundamentally transform the state-of-the-art in a variety of industries. Notwithstanding the enormous progress in current additive manufacturing technologies, certain intractable quality issues persist. This leads to considerable rework and high scrap rates, and thus poses significant impediments for sustainability of additive manufacturing. Consequently, there is a vital need to advance online methods for defect detection in additive manufacturing processes, so that incipient process anomalies can be identified, and possibly prevented, at an early stage during manufacture. This Grant Opportunity for Academic Liaison with Industry (GOALI) research project is anticipated to significantly advance the process monitoring and control technology in additive manufacturing, leading to improved product quality, enhanced process productivity, and higher profitability. Thus, outcomes from this research will have substantial socioeconomic impacts. The scientific findings from this research are extensible to many other advanced manufacturing processes. Furthermore, this project also includes many educational components, such as new course modules, and research experience for undergraduate students. Exposing students to this multidisciplinary research will cultivate a diverse and qualified workforce possessing the state-of-the-art technologies in advanced manufacturing.The goal of this research is to resolve critical quality issues in additive manufacturing by addressing two fundamental research questions based on an integration of novel multi-phenomena sensing techniques with advanced analytical approaches for sensor data fusion: (1) what is the dynamic behavior of various process attributes, and how does this behavior cause the onset of additive manufacturing process anomalies? and (2) what are the causal linkages between additive manufacturing quality performance and process variables? The objectives of the research include: (1) quantitatively elucidate the fundamental relationships that connect process abnormalities in additive manufacturing with features extracted from online spatiotemporal sensor signals, i.e., achieve a mapping between the sensor features with the evolving part defects in additive manufacturing, enabling early detection of surface topography related defects; and (2) establish a model correlating process conditions/settings with both continuous and attribute product quality variables in additive manufacturing processes, and identify the process variables which have significant effects on product quality, providing valuable strategies for defect mitigation and implementation of close loop control in future additive manufacturing systems. This research will utilize prediction based process monitoring to tackle underlying complexity and uncertainty in additive manufacturing processes.

增材制造相对于传统制造具有显著优势，有可能从根本上改变各种行业的最先进技术。尽管当前增材制造技术取得了巨大进步，但某些棘手的质量问题仍然存在。这导致大量返工和高废品率，从而对增材制造的可持续性构成重大障碍。因此，迫切需要推进用于增材制造过程中的缺陷检测的在线方法，使得可以在制造期间的早期阶段识别并可能防止初期过程异常。该研究项目将显著推进增材制造中的过程监测和控制技术，从而提高产品质量，提高工艺生产率和更高的盈利能力。因此，这项研究的结果将产生重大的社会经济影响。这项研究的科学发现可以扩展到许多其他先进的制造工艺。 此外，该项目还包括许多教育内容，如新的课程模块和本科生的研究经验。让学生接触到这个多学科的研究将培养一个多样化的和合格的劳动力拥有先进制造业的最先进的技术。这项研究的目标是通过解决两个基础研究问题的基础上集成的新型多现象传感技术与先进的分析方法传感器数据融合，以解决关键的质量问题在增材制造：（1）各种过程属性的动态行为是什么，以及这种行为如何导致增材制造过程异常的发生？以及（2）增材制造质量性能和工艺变量之间的因果关系是什么？研究的目标包括：（1）定量阐明增材制造中的过程异常与从在线时空传感器信号中提取的特征之间的基本关系，即，在增材制造中实现传感器特征与演变的零件缺陷之间的映射，从而能够早期检测表面形貌相关的缺陷;以及（2）建立将工艺条件/设置与增材制造工艺中的连续和属性产品质量变量相关联的模型，并识别对产品质量具有显著影响的工艺变量，为未来的增材制造系统中的缺陷减轻和闭环控制的实现提供有价值的策略。这项研究将利用基于预测的过程监控来解决增材制造过程中潜在的复杂性和不确定性。