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，仅在美国每年就可以通过器官移植挽救近一百万人的生命，从而可能避免全国 35% 的死亡。相比之下，由于器官严重短缺，每年仅进行约2.8万例器官移植，等待人数达12万人。 解决这一短缺问题的一个有希望的潜在解决方案是通过生物增材制造 (Bio-AM) 进行高质量和生产规模的人体器官 3D 打印。然而，正如 2016 年 NSF 医疗保健增材制造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，部分原因是过程监控不足和缺乏集成的过程控制策略。因此，尽管取得了巨大进步，但仍然无法将 Bio-AM 扩展到器官移植所规定的严格质量标准。这项研究将解决将先进过程模型纳入基于传感器的过程控制策略的迫切需求，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、高质量和安全的生物增材制造方法，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) 创新制造项目吸引 6-12 年级的 STEM 教师参与，为教师提供参与 Bio-AM 前沿研究的机会。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）。核心方法是将基于异质传感器的原位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) 使用实验和建模来了解工艺与材料相互作用对特定 Bio-AM 缺陷的因果影响，(2) 使用传感器检测打印过程中的初期缺陷，(3) 诊断根源通过使用实时决策理论模型分析传感器数据来确定检测到的缺陷的原因，以及（4）通过闭环过程控制防止缺陷传播。 该研究将有助于：(1) 通过实证研究和基于传感器的数据分析，对控制印刷生物组织结构质量的因果生物物理过程相互作用有基本的了解，(2) 通过采用新的数学模型来预测层质量考虑到复杂且动态的组织成熟现象，（3）实时且计算高效的决策，用于根据传感器数据对缺陷进行准确分类，以及（4）两阶段实时闭环质量控制方法通过执行智能纠正来防止缺陷传播打印过程中的动作。

项目成果

Process–Structure–Quality Relationships of Three-Dimensional Printed Poly(Caprolactone)-Hydroxyapatite Scaffolds

三维打印聚己内酯-羟基磷灰石支架的工艺-结构-质量关系

• DOI: 10.1089/ten.tea.2019.0237
• 发表时间: 2020-03
• 期刊: Tissue Engineering Part A
• 影响因子: 4.1
• 作者: Gerdes, Sam;Mostafavi, Azadeh;Ramesh, Srikanthan;Memic, Adnan;Rivero, Iris V.;Rao, Prahalada;Tamayol, Ali
• 通讯作者: Tamayol, Ali

Monitoring and control of biological additive manufacturing using machine learning

使用机器学习监测和控制生物增材制造

• DOI: 10.1007/s10845-023-02092-6
• 发表时间: 2023-03-06
• 期刊: J. Intell. Manuf.
• 作者: Sam Gerdes;A. Gaikwad;S. Ramesh;I. Rivero;A. Tamayol;Prahalada K. Rao
• 通讯作者: Prahalada K. Rao

Extrusion-based 3D (Bio)Printed Tissue Engineering Scaffolds: Process−Structure−Quality Relationships

基于挤出的 3D（生物）打印组织工程支架：工艺与结构与质量关系

• DOI: 10.1021/acsbiomaterials.1c00598
• 发表时间: 2021-01
• 期刊: ACS biomaterials science engineering
• 作者: Gerdes, Samuel;Ramesh, Srikanthan;Mostafavi, Azadeh;Tamayol, Ali;Rivero, Iris V;Rao, Prahalad
• 通讯作者: Rao, Prahalad

Extrusion bioprinting: Recent progress, challenges, and future opportunities

挤出生物打印：最新进展、挑战和未来机遇

• DOI: 10.1016/j.bprint.2020.e00116
• 发表时间: 2021-03-01
• 期刊: Bioprinting
• 作者: S. Ramesh;O. Harrysson;Prahalada K. Rao;A. Tamayol;D. Cormier;Yunbo Zhang;I. Rivero
• 通讯作者: I. Rivero