Monitoring and control of biological additive manufacturing using machine learning
Monitoring and control of biological additive manufacturing using machine learning
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DOI：
10.1007/s10845-023-02092-6
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发表时间：
2023-03-06
影响因子：
8.3
通讯作者：
Rao，Prahalada
中科院分区：
文献类型：
--
作者：
Gerdes，Samuel;Gaikwad，Aniruddha;Rao，Prahalada
这项工作的目标是无污染的，工业规模的生产组织结构的生物增材制造（Bio-AM）。为了实现这一目标，在骨组织结构的基于挤出的Bio-AM背景下，这项工作的目标有两个：（1）使用来自原位红外热电偶传感器的数据检测缺陷形成;（2）通过先发制人的过程控制防止缺陷形成。在实现第一个目标时，使用几种机器学习方法分析从现场传感器获得的数据签名，以确定关键的质量指标，例如印刷制度、股线宽度、股线高度和股线融合严重程度。这些质量度量旨在捕获基本1D链级到2D层级的过程状态。为此，机器学习模型被训练来分类和预测缺陷的形成。这些模型预测打印质量特征的准确率接近90%。关于第二个目标，先前训练的机器学习模型被用于通过在沉积期间改变工艺参数（打印速度）来预先防止缺陷形成，这是前馈控制的一种形式。通过前馈工艺控制，股线宽度不均匀性在统计学上显著降低，将股线两半之间的股线宽度差异降低至小于50 µm。使用这种集成的过程监测，检测和控制方法，我们证明了Bio-AM结构的一致性，可重复性生产。
The goal of this work is the flaw-free, industrial-scale production of biological additive manufacturing of tissue constructs (Bio-AM). In pursuit of this goal, the objectives of this work in the context of extrusion-based Bio-AM of bone tissue constructs are twofold: (1) detect flaw formation using data from in-situ infrared thermocouple sensors; and (2) prevent flaw formation through preemptive process control. In realizing the first objective, data signatures acquired from in-situ sensors were analyzed using several machine learning approaches to ascertain critical quality metrics, such as print regime, strand width, strand height, and strand fusion severity. These quality metrics are intended to capture the process state at the basic 1D strand-level to the 2D layer-level. For this purpose, machine learning models were trained to classify and predict flaw formation. These models predicted print quality features with accuracy nearing 90%. In connection with the second objective, the previously trained machine learning models were used to preempt flaw formation by changing the process parameters (print velocity) during deposition—a form of feedforward control. With the feedforward process control, strand width heterogeneity was statistically significantly reduced, reducing the strand width difference between strand halves to less than 50 µm. Using this integrated process monitoring, detection, and control approach, we demonstrate consistent, repeatable production of Bio-AM constructs.