Laser Directed Energy Deposition (L-DED) has garnered significant attention due to its high flexibility and rapid processing capabilities. However, complex physical flow fields and drastic temperature variations are present during L-DED processing, leading to variations in deposition height at different layers and positions under the same processing parameters. Therefore, real-time monitoring of deposition height and timely knowledge of future deposition height are crucial for controlling geometries and arranging processing time effectively. To address this issue, a machine vision method for real-time monitoring of deposition height in noisy environments is proposed, demonstrating a remarkable similarity of 99.22% compared to values measured by a laser scanner. Addressing the complex physical phenomena during processing, specific data quantification was performed. A novel self-attention temporal convolutional network (SA-TCN) was then introduced as a data-driven model to replace physical models for predicting future deposition height, achieving an impressive accuracy of 99.71%. Experiments show that quantifying different physical phenomena with specific data to some extent improves the model prediction accuracy, providing significant support for future deposition height prediction and processing time control of parts in actual production.

中文翻译：

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机器视觉和新颖的注意力机制 TCN 用于增强定向能量沉积中未来沉积高度的预测


激光定向能量沉积（L-DED）因其高灵活性和快速加工能力而受到广泛关注。然而，L-DED加工过程中存在复杂的物理流场和剧烈的温度变化，导致相同加工参数下不同层和位置的沉积高度存在差异。因此，实时监测沉积高度并及时了解未来沉积高度对于有效控制几何形状和安排加工时间至关重要。为了解决这个问题，提出了一种在噪声环境中实时监测沉积高度的机器视觉方法，与激光扫描仪测量的值相比，其相似度高达 99.22%。针对加工过程中复杂的物理现象，进行了具体的数据量化。随后引入了一种新颖的自注意力时间卷积网络（SA-TCN）作为数据驱动模型来取代物理模型来预测未来的沉积高度，达到了令人印象深刻的 99.71％ 的准确度。实验表明，用具体数据量化不同的物理现象在一定程度上提高了模型预测精度，为未来实际生产中零件的沉积高度预测和加工时间控制提供了重要支持。