Invention Reference Number
Sensing of additive manufacturing processes promises to facilitate detailed quality inspection at scales that have seldom been seen in traditional manufacturing processes. Long exposure imaging using near infrared (NIR) sensors has shown promise for detecting both localized flaws and macroscopic build anomalies and failures. However, the approach is sensitive to imaging artifacts, namely pixel dead time and dropped frames, which can affect post-acquisition analysis of the data and introduce false positives. These false positives can then manifest as false lack-of-fusion detections in downstream analysis software, leading to scrapped components that would otherwise be considered acceptable. We propose a multi-sensor approach to address issues related to image artifacts present in long-exposure imaging. Multiple sensors, which can be varying imaging modalities, can fill in the gaps missing in the long-exposure NIR imaging. These sensors can either be complimentary thermal imaging modalities, such as a different wavelength in the NIR range or long wave infrared (LWIR) images, or standard imaging techniques in the visible light spectrum. Imaging artifacts present in the long-exposure imaging can then be addressed by the complementary imaging modalities, which would allow downstream analysis software to accurately count these imaging artifacts as acceptable print data instead of false positives.
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