The quality of the powder layers in the 3D printing process is extremely important and directly corresponds to the quality of the structures made with this technology. Therefore, it is essential to control it. It can be made in-line with a vision system combined with image processing algorithms, which can significantly improve control of the process and help with the adjustment of powder spreading systems, especially in case of difficult-to-feed powders like magnetic ones – e.g., Fe-based metallic glass powder – Fe56.04Co13.45Nb5.5B25. In this work, two algorithms – machine learning – Support Vector Machines (SVM), deep learning – Convolutional Neural Networks (CNN) – were evaluated for their ability to detect and classify the enumerated anomalies based on powder layer images. The SVM algorithm makes it possible to efficiently and quickly analyze the powder-spreading process. CNN, however, appears to be a more promising choice for the developed application, as they alleviate the need for complex image operations.

中文翻译：

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机器视觉在增材制造过程中粉末床缺陷检测中的应用

3D 打印过程中粉末层的质量极其重要，直接关系到采用该技术制造的结构的质量。因此，有必要对其进行控制。它可以与视觉系统和图像处理算法相结合，从而显着改善过程控制并有助于调整粉末撒布系统，特别是在难以喂入磁性粉末的情况下 - 例如,铁基金属玻璃粉-Fe56.04钴13.45铌5.5乙25。在这项工作中，评估了两种算法——机器学习——支持向量机 (SVM)、深度学习——卷积神经网络 (CNN)——根据粉末层图像检测和分类所列举的异常的能力。SVM算法使得高效、快速地分析粉末铺展过程成为可能。然而，对于已开发的应用程序来说，CNN 似乎是更有前途的选择，因为它们减轻了对复杂图像操作的需求。