A hybrid off-line/on-line quality control approach for real-time monitoring of high-density datasets

Recent advancements in measurement systems have brought new opportunities to enhance the performance of quality control (QC) systems in modern manufacturing. Digital cameras and optical scanners are among these advanced measurement systems that are used for automated surface inspection. They can represent an entire product’s surface with high-density (HD) data in the forms of digital images and point clouds, respectively. Although both measurement systems provide HD data, their datasets are fundamental different and contain different information regarding a part’s surface. Extensive research efforts have been conducted to develop QC tools for each of these datasets individually; however, little research has focused on taking advantage of both point clouds and digital images simultaneously. To fully take advantage of information from both datasets, and more importantly their spatial cross correlation, this paper aims to use fused image/point cloud datasets to advance the capability of QC systems. A key challenge in incorporating both datasets is that the costs of acquiring data from these measurement systems differ drastically, making online monitoring using fused datasets less appealing. To overcome this challenge, a novel off-line/on-line hybrid monitoring scheme is proposed. The effectiveness of this proposed hybrid monitoring scheme is demonstrated with an additive manufacturing case study.