Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks
Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks
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DOI：
10.1007/s40544-023-0826-7
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发表时间：
2023-12
期刊：
影响因子：
6.8
通讯作者：
Junhyeon Seo;Prahalada Rao;B. Raeymaekers
中科院分区：
文献类型：
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作者：
Junhyeon Seo;Prahalada Rao;B. Raeymaekers
数值生成与实验表面形貌测量的特征和特征非常相似的合成表面形貌减少了执行这些复杂和昂贵的测量的需要。然而，现有的数值生成表面形貌的算法并不适合创建由激光粉末床熔融（LPBF）产生的成品表面的特定特征和几何特征，例如部分熔化的金属颗粒、孔隙度、激光扫描线和成球。因此，我们提出了一种使用逐步增长的生成对抗网络生成合成已建LPBF表面地形图的方法。我们定性和定量地证明了合成和实验建成的LPBF表面地形图之间的良好一致性，这些地形图使用面积和确定性表面地形参数，径向平均功率谱密度和材料比曲线。精确生成合成的已建LPBF表面地形图的能力减少了执行大量表面地形测量的实验负担。此外，它有助于将实验测量与合成表面地形图相结合，以创建大型数据集，例如，将建成的表面形貌与LPBF工艺参数相关联，或实施数字表面双胞胎以监测复杂的最终用途LPBF部件，以及其他应用。
Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements. However, existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion (LPBF), such as partially melted metal particles, porosity, laser scan lines, and balling. Thus, we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network. We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters, radially averaged power spectral density, and material ratio curves. The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements. Furthermore, it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate, e.g. relating as-built surface topography to LPBF process parameters, or implementing digital surface twins to monitor complex end-use LPBF parts, amongst other applications.