Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing
Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing
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DOI：
10.1115/1.4048957
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发表时间：
2020-12
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影响因子：
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通讯作者：
Qi Tian;Qi Tian;Shenghan Guo;Erika Melder;L. Bian;W. Guo
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文献类型：
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作者：
Qi Tian;Qi Tian;Shenghan Guo;Erika Melder;L. Bian;W. Guo
基于激光的增材制造（LBAM）提供了无与伦比的设计自由度，能够为广泛的工程应用制造复杂的零件。熔池是LBAM中最重要的特征之一，是工艺异常和零件缺陷的指示。LBAM期间捕获的熔池高速热图像使原位熔池监测和孔隙率预测成为可能。本文的目的是扩大目前的知识LBAM过程和孔隙度之间的关系，并提供新的可能性，有效和准确的孔隙度预测。我们提出了一种基于深度学习的数据融合方法，通过利用测量的熔池热历史和两个新创建的深度学习神经网络来预测LBAM部件的孔隙率。基于卷积神经网络的PyroNet用于将过程中的高温测量图像与逐层孔隙率相关联;基于长期递归卷积网络的IRNet用于将红外相机的序列热图像与逐层孔隙率相关联。PyroNet和IRNet的预测在决策层进行融合，以获得更准确的逐层孔隙度预测。用LBAM Ti-6Al-4V薄壁结构验证了模型的真实性。这是第一项成功融合高温计数据和红外相机数据用于金属增材制造（AM）的工作。基于基准数据集的实例研究结果表明，该方法具有较高的精度和效率，证明了该方法在LBAM原位孔隙度检测中的适用性。
Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures in LBAM and is indicative of process anomalies and part defects. High-speed thermal images of the melt pool captured during LBAM make it possible for in situ melt pool monitoring and porosity prediction. This paper aims to broaden current knowledge of the underlying relationship between process and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based on Convolutional Neural Networks, is developed to correlate in-process pyrometry images with layer-wise porosity; an IRNet, based on Long-term Recurrent Convolutional Networks, is developed to correlate sequential thermal images from an infrared camera with layer-wise porosity. Predictions from PyroNet and IRNet are fused at the decision-level to obtain a more accurate prediction of layer-wise porosity. The model fidelity is validated with LBAM Ti–6Al–4V thin-wall structure. This is the first work that manages to fuse pyrometer data and infrared camera data for metal additive manufacturing (AM). The case study results based on benchmark datasets show that our method can achieve high accuracy with relatively high efficiency, demonstrating the applicability of the method for in situ porosity detection in LBAM.