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首页> 外文期刊>Journal of manufacturing science and engineering: Transactions of the ASME >Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing
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Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing

机译：基于深度学习的基于孔隙度检测的基于深度学习的数据融合方法

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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.
机译：基于激光的加性制造（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零件。熔池是LBAM中最重要的特征之一，指示工艺异常和零件缺陷。LBAM过程中拍摄的熔池高速热图像使现场熔池监测和孔隙度预测成为可能。本文旨在拓宽LBAM过程与孔隙度之间潜在关系的现有知识，并为高效准确的孔隙度预测提供新的可能性。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零件中的孔隙率。开发了一种基于卷积神经网络的热解网络，用于将过程中的测温图像与分层孔隙度进行关联；基于长期循环卷积网络，开发了一个红外网络，用于将红外相机的连续热图像与分层孔隙度关联起来。PyroNet和IRNet的预测在决策层融合，以获得更准确的分层孔隙度预测。用LBAM Ti-6Al-4V薄壁结构验证了模型的保真度。这是第一项将高温计数据和红外摄像机数据融合到金属添加剂制造（AM）中的工作。基于基准数据集的实例研究结果表明，该方法能够以相对较高的效率实现较高的精度，证明了该方法在LBAM现场孔隙度检测中的适用性。

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