CPS: Medium: Collaborative Research: Cyber-Enabled Online Quality Assurance for Scalable Additive Bio-Manufacturing

如果有足够数量的器官可用，仅在美国每年就可以通过器官移植挽救近100万人的生命，这可能会阻止全国35%的死亡。相比之下，由于器官严重短缺，每年仅进行约28，000例器官移植，等待名单上有120，000人。 解决这一短缺的一个有希望的潜在解决方案是通过生物增材制造（Bio-AM）进行人体器官的高质量和生产规模的3D打印。然而，正如2016年NSF关于医疗保健增材制造的研讨会所阐述的那样，目前Bio-AM的使用受到器官质量差的阻碍，部分原因是过程监测不足和缺乏综合过程控制策略。因此，尽管取得了巨大的进步，但仍然不可能将Bio-AM扩展到器官移植所要求的严格质量标准。这项研究将解决迫切需要将先进的过程模型纳入基于传感器的过程控制策略，以防止细胞损伤，减少细胞放置错误，并改善Bio-AM中的组织功能。如果能够成功实现可靠、大批量、高质量和安全的Bio-AM方法，它将在公共卫生、医疗安全和药物发现方面产生深远的社会经济效益。 该项目将通过教师研究经验（RET）创新制造计划，为教师提供参与Bio-AM前沿研究的机会，吸引6-12年级的STEM教师。该项目的目标是可靠地生产可行的3D打印生物结构（微型组织）。核心方法是将基于传感器的原位异构监测和实时闭环过程控制方法结合起来，以确保生物结构的可靠打印。这项工作涉及以下四个目标：（1）使用实验和建模来理解工艺-材料相互作用对特定Bio-AM缺陷的因果影响，（2）采用传感器来检测打印期间的初期缺陷，（3）通过使用实时决策理论模型分析传感器数据来诊断检测到的缺陷的根本原因，以及（4）通过闭环过程控制防止缺陷的传播。 调查将有助于：（1）对因果生物物理过程相互作用的基本理解，所述因果生物物理过程相互作用通过经验调查和基于传感器的数据分析来控制打印的生物组织构造的质量，（2）通过考虑复杂和动态的组织成熟现象来预测层质量的新数学模型，（3）用于从传感器数据准确分类缺陷的实时和计算上有效的决策，以及（4）两阶段，实时，闭环质量控制方法，通过在打印过程中执行智能纠正措施来防止缺陷传播。

Close to one million lives could be saved each year in the United States alone by organ transplantation if a sufficient number of organs were available, potentially preventing 35% of all deaths in the nation. In contrast, due to critical shortages of organs, only about 28,000 organ transplants are performed each year, with a waiting list of 120,000 people. A promising potential solution to this shortage is the high quality and production-scale 3D printing of human organs by bio-additive manufacturing (Bio-AM). However, as articulated in the 2016 NSF workshop on Additive Manufacturing for Healthcare, the current use of Bio-AM is impeded by poor organ quality, resulting in part from inadequate process monitoring and lack of integrated process control strategies. As a result, despite enormous strides, it is still not possible to scale Bio-AM to the stringent quality standards mandated for organ transplants. This research will address the compelling need to incorporate advanced process models into sensor-based process control strategies needed to prevent cell damage, decrease cell placement errors, and improve tissue functioning in Bio-AM. If successful methods for reliable, high-volume, high-quality, and safe Bio-AM can be realized, it will have profound socioeconomic benefits in terms of public health, medical safety, and drug discovery. The project will engage grade 6-12 STEM teachers through the Research Experiences for Teachers (RET) Innovation-based Manufacturing Program by providing opportunities for teachers to engage in cutting edge research in Bio-AM. The goal of the project is to reliably produce viable 3D printed biological constructs (mini-tissues). The central approach is to couple in-situ heterogeneous sensor-based monitoring and real-time closed-loop process control approaches for ensuring the reliable printing of biological constructs. The work involves the following four objectives: (1) using experimentation and modeling to understand the causal effect of process-material interactions on specific Bio-AM defects, (2) employing sensors to detect incipient defects during printing, (3) diagnosing the root causes of detected defects by analyzing sensor data using real-time decision-theoretic models, and (4) preventing propagation of defects through closed-loop process control. The investigation will contribute: (1) fundamental understanding of the causal bio-physical process interactions that govern the quality of printed biological tissue constructs through empirical investigation and sensor-based data analytics, (2) new mathematical models for predicting the layer quality by taking into consideration the complex and dynamic tissue maturation phenomena, (3) real-time and computationally efficient decision-making for accurate classification of defects from sensor data, and (4) a two-stage, real-time, closed-loop quality control approach for preventing propagation of defects by executing smart corrective actions during the printing process.