Multi-resolution Quality Inspection of Layerwise Builds for Metal 3D Printer-and-Scanner

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Hui Yang, Joni Reijonen, A. Revuelta
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引用次数: 0

Abstract

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, 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 multi-scale lenses. This paper aims to design and develop an AOI system with contact image sensors for multi-resolution quality inspection of layerwise builds in additive manufacturing. We design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, recoater 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. 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. The proposed framework of multi-resolution 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.
金属3D打印机和扫描仪分层构建的多分辨率质量检测
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,分层图像显示出复杂的模式,并且通常包含无法在单个尺度中显示的隐藏信息。一种新的替代方法是用多尺度透镜分析这些固有模式。本文旨在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结构的多分辨率质量检测。我们设计了在各种因素水平下制造九个零件的实验(例如,气流阻塞、反冲损坏、激光功率变化)。在每一层中,AOI系统收集激光融合前重涂粉末床和激光融合后表面光洁度的成像数据。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,而不是外来噪声。使用真实世界的AM成像数据对所提出的多分辨率质量检测框架进行了评估和验证。实验结果证明了所提出的具有接触式图像传感器的AOI系统在AM过程中用于分层构建的在线质量检测的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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