The advent of additive manufacturing (AM) processes brought with it intense research into various materials and manufacturing processes. At the same time, the need for validation of material properties, as well as study and forecasting of aging, has arisen. Modern imaging techniques, like X-ray computed tomography (XCT), are a convenient vehicle for such studies; however, the large datasets they produce require novel analysis techniques to efficiently extract critical information. In this paper, we present our work on developing a 3D extension of the ResNet architecture to distinguish between two build orientations of tensile bars produced by AM. Using only information from XCT, our method achieves a 99.3% correct classification at a misidentification of 1%.

中文翻译：

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使用卷积神经网络识别 3D 打印材料的构建方向

增材制造 (AM) 工艺的出现带来了对各种材料和制造工艺的深入研究。与此同时，对材料特性的验证以及老化的研究和预测的需求已经出现。现代成像技术，如 X 射线计算机断层扫描 (XCT)，是进行此类研究的便捷工具；然而，他们生成的大型数据集需要新颖的分析技术来有效地提取关键信息。在本文中，我们介绍了我们在开发 ResNet 架构的 3D 扩展方面的工作，以区分 AM 生产的拉伸杆的两个构建方向。仅使用来自 XCT 的信息，我们的方法以 1% 的错误识别率实现了 99.3% 的正确分类。