In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning
In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning
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DOI：
10.1007/s10845-022-01977-2
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发表时间：
2022-06
影响因子：
8.3
通讯作者：
A. Gaikwad;Tammy Chang;B. Giera;N. Watkins;S. Mukherjee;A. Pascall;D. Stobbe;Prahalada K. Rao
中科院分区：
文献类型：
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作者：
A. Gaikwad;Tammy Chang;B. Giera;N. Watkins;S. Mukherjee;A. Pascall;D. Stobbe;Prahalada K. Rao
在按需液滴液态金属喷射（DoD-LMJ）增材制造中，复杂的物理相互作用支配液滴特性，诸如尺寸、速度和形状。这些液滴特性反过来又决定了打印部件的功能质量。因此，为了确保可重复和可靠的零件质量，有必要监测和控制液滴特性。DoD-LMJ中液滴行为的原位监测的现有方法依赖于高速成像传感器。所获得的大量液滴图像在计算上需要分析，并且阻碍了过程的实时控制。为了克服这一挑战，这项工作的目的是使用时间序列数据从过程中的毫米波传感器用于预测DoD-LMJ过程中的液滴的大小，速度和形状特性。与高速成像相反，该传感器可产生数据高效的时间序列特征，从而实现快速、实时的过程监控。我们设计了使用毫米波传感器数据来预测液滴特征的机器学习模型。具体来说，我们开发了基于多层感知器的非线性自回归模型来预测液滴的大小和速度。同样，训练监督机器学习模型以使用毫米波传感器签名中包含的频谱信息对液滴形状进行分类。高速成像数据作为模型训练和验证的基础数据。这些模型捕获的液滴特性的统计保真度超过90%，大大优于传统的统计建模方法。因此，这项工作实现了一个实际可行的传感方法的实时质量监测的DoD-LMJ过程中，代替现有的数据密集型基于图像的技术。
In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.