Recent measurement system developments have brought new opportunities to enhance the performance of quality control (QC) systems in manufacturing. Digital cameras and 3D optical scanners are among the advanced measurement systems that can represent an entire product’s surface. These data-rich environments have been widely used in automated surface defect detection. However, despite the fact that datasets from both digital cameras and 3D optical scanners can both represent a surface, the datasets are fundamental different and contain different information regarding a part’s surface. Extensive research efforts have been conducted on developing QC tools for each of these datasets individually, very few researches were focused on approaches to take advantage of both 3D point clouds (obtained from technologies such as 3D optical scanners) and digital images (obtained from digital cameras) at the same time. This paper proposes a hybrid approach for surface inspection of additive manufacturing parts through Multilinear Principal Component Analysis (MPCA). This proposed approach is demonstrated with additively manufactured parts, which shows the advantage of combining this information for surface classification.

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