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

CPS：媒介：协作研究：可扩展增材生物制造的网络在线质量保证

• 批准号: 1739696
• 负责人: Prahalada Rao
• 金额: $ 20万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1739696&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Cyber

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.

如果有足够数量的器官可用，则仅在美国，每年就可以通过器官移植来挽救近一百万的生命，这有可能阻止全国所有死亡人数的35％。相比之下，由于器官的严重短缺，每年仅进行约28,000台器官移植，等待清单12万人。 这种短缺的有希望的潜在解决方案是生物添加性制造（Bio-AM）对人体器官的高质量和生产规模的3D打印。但是，正如2016年NSF关于医疗保健添加剂制造的研讨会所阐明的那样，当前的Bio-AM使用受到器官质量的障碍所阻碍，部分原因是过程监测不足和缺乏集成过程控制策略。结果，尽管大步前进，但仍无法将生物AM扩展到为器官移植的严格质量标准。这项研究将解决将高级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，减少细胞放置误差并改善Bio-AM中组织功能所需的迫切需求。如果可以实现可靠，大量，高质量和安全的生物AM的成功方法，那么它将在公共卫生，医疗安全和药物发现方面具有深远的社会经济益处。 该项目将通过为教师（RET）创新的制造计划的研究经验与6至12年级的STEM教师互动，从而为教师提供了在Bio-AM进行尖端研究的机会。该项目的目的是可靠地生产可行的3D印刷生物结构（迷你生物）。中心方法是将基于原位的异质传感器的监测和实时闭环工艺控制方法进行确保可靠的生物构造印刷。 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. 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，该过程通过基于经验的研究和基于传感器的数据分析来控制印刷生物组织构建的质量，（2）通过考虑复杂和动态组织现象的复杂和动力学效率（3），（3）用于预测层质量的新数学模型（3） （4）一种两阶段的实时闭环质量控制方法，用于通过在打印过程中执行智能纠正措施来防止缺陷传播。

项目成果

Extrusion bioprinting: Recent progress, challenges, and future opportunities

• DOI: 10.1016/j.bprint.2020.e00116
• 发表时间: 2021-03-01
• 期刊: Bioprinting
• 作者: Ramesh, Srikanthan;Harrysson, Ola L. A.;Rivero, Iris, V
• 通讯作者: Rivero, Iris, V

Monitoring and control of biological additive manufacturing using machine learning

• DOI: 10.1007/s10845-023-02092-6
• 发表时间: 2023-03-06
• 期刊: JOURNAL OF INTELLIGENT MANUFACTURING
• 影响因子: 8.3
• 作者: Gerdes，Samuel;Gaikwad，Aniruddha;Rao，Prahalada
• 通讯作者: Rao，Prahalada