Multiresolution Quality Inspection of Layerwise Builds for Metal 3D Printer and Scanner
Multiresolution Quality Inspection of Layerwise Builds for Metal 3D Printer and Scanner
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金属 3D 打印机和扫描仪分层构建的多分辨率质量检查
DOI：
10.1115/1.4057013
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Revuelta, Alejandro
中科院分区：
文献类型：
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作者：
Yang, Hui;Reijonen, Joni;Revuelta, Alejandro
自动光学检测（AOI）越来越多地被提倡用于增材制造（AM）过程的现场质量监测。分层成像数据的可用性提高了制造过程中的信息可见性，从而有利于进行在线认证。然而，很少有人（如果有的话）研究了高速接触式图像传感器（CIS）（即，最初为文件扫描仪和多功能打印机开发）用于AM质量监测。此外，分层图像显示复杂的模式，并且通常包含单个尺度无法显示的隐藏信息。一种新的替代方法是用多尺度透镜来分析这些内在模式。因此，本文的目的是设计和开发一种具有接触式图像传感器的AOI系统，用于增材制造中分层构建的多分辨率质量检测。首先，我们在激光粉末床融合（LPBF）机器上使用工业相关的95 mm/s扫描速度的接触式图像传感器改造AOI系统。然后，我们设计了实验，在各种因素水平下（例如，气体流动阻塞，再涂层机损坏，激光功率变化）制造9个零件。在每一层中，AOI系统收集激光熔化前重涂粉层和激光熔化后表面光面的成像数据。其次，对分层图像进行预处理，对这九个部分的兴趣区域（roi）进行对齐、配准和识别。然后，我们利用小波变换在多个尺度上分析ROI图像，并进一步提取对过程变化敏感的显著特征，而不是无关噪声。第三，我们进行配对比较分析，探讨不同程度的因素对小波特征分布的影响。最后，这些特征在预测分层增材制造的计算机断层扫描（CT）数据中的缺陷程度方面是有效的。使用真实的AM成像数据评估和验证了所提出的多分辨率质量检测框架。实验结果表明，基于接触式图像传感器的AOI系统可用于增材制造过程中分层构件的在线质量检测。
Automated optical inspection (AOI) is increasingly advocated for in situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, few, if any, have investigated the high-speed contact image sensors (CIS) (i.e., originally developed for document scanners and multifunction printers) for AM quality monitoring. In addition, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multiscale lenses. Therefore, the objective of this article is to design and develop an AOI system with contact image sensors for multiresolution quality inspection of layerwise builds in additive manufacturing. First, we retrofit the AOI system with contact image sensors in industrially relevant 95 mm/s scanning speed to a laser-powder-bed-fusion (LPBF) machines. Then, we design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, re-coater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Second, layerwise images are pre-preprocessed for alignment, registration, and identification of regions of interests (ROIs) of these nine parts. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. Third, we perform the paired comparison analysis to investigate how different levels of factors influence the distribution of wavelet features. Finally, these features are shown to be effective in predicting the extent of defects in the computed tomography (CT) data of layerwise AM builds. The proposed framework of multiresolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.