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.
|Number of pages||15|
|Journal||Journal of Manufacturing Science and Engineering|
|Publication status||E-pub ahead of print - 27 Feb 2023|
|MoE publication type||A1 Journal article-refereed|
- additive manufacturing
- control and automation
- monitoring and diagnostics