Process monitoring and control strategies in extrusion-based bioprinting to fabricate spatially graded structures
Process monitoring and control strategies in extrusion-based bioprinting to fabricate spatially graded structures
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DOI：
10.1016/j.bprint.2020.e00126
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发表时间：
2021-03-01
期刊：
影响因子：
--
通讯作者：
Johnson, Amy J. Wagoner
中科院分区：
文献类型：
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作者：
Armstrong, Ashley A.;Pfeil, Arielle;Johnson, Amy J. Wagoner
基于挤压的生物打印是再生医学中最常用的打印技术。尽管最近的技术进步，挤压打印的一个紧迫的挑战是低空间分辨率，这限制了打印结构的功能。空间分辨率低的原因之一是缺乏过程监控和控制策略来监控制造和纠正打印错误。对挤出工艺参数与打印保真度之间关系的研究很少。缺乏对工艺参数和打印结果之间的理解最终限制了可能结构的复杂性。例如，如果没有先进的工艺控制，制造拓扑结构在零件内部空间变化的结构是不可能的。在这里，我们使制造先进的空间梯度结构通过实施过程监测和控制策略。我们开发材料模型来更好地理解工艺参数和打印结果之间的关系。我们还提出了一个实验程序来生成一个工艺图，该工艺图提供了对产生所需挤压特征（例如，长丝的宽度）的加工空间区域的洞察。在生成工艺图和材料模型后，我们实施过程监控策略，测量特征误差并智能更新过程控制输入，以减少缺陷和提高空间保真度，从而使最终结构具有更好的功能。
Extrusion-based bioprinting is the most common printing technology used in regenerative medicine. Despite recent technological advances, a pressing challenge for extrusion printing is low spatial resolution, which limits the functionality of printed constructs. One of the reasons for the low spatial resolution is a lack of process monitoring and control strategies to monitor fabrication and correct for print errors. Few research efforts implement process control and investigate the relationship between extrusion process parameters and printing fidelity. The lack of understanding between process parameters and print results ultimately limits the complexity of the possible structures. For example, fabrication of structures whose topologies vary spatially within the part is not possible without advanced process control. Here we enable fabrication of advanced spatially graded structures by implementing process monitoring and control strategies. We develop material models to better understand the relationship between process parameters and printing outcomes. We also present an experimental procedure to generate a process map that provides insight into the regions of the processing space that produce the desired extrusion features (e.g., width of the filament). After generation of a process map and material models, we implement a process monitoring and control strategy that measures the feature error and intelligently updates the process control inputs to reduce defects and improve spatial fidelity, which will lead to better functionality of the final construct.