5001.001.001.002 Correlating In-Situ Sensor Data to Defect Locations

102575.000.5001.001.001.002 Correlating In-Situ Sensor Data to Defect Locations

This project combines data from multi-modal in-situ sensors for training a machine learning algorithm to identify the defects in a powder bed fusion build.

Additive manufacturing (AM) production methods require strict dynamic control of processing parameters and conditions to achieve high-quality, fully dense components. Numerous flaws however may be inadvertently generated during the build when process parameters and conditions deviate or when unforeseen disturbances influence the process. Post process inspection technologies, such as high-resolution 3D X-ray computed tomography (XCT), are available to identify these flaws but there is a pressing need for reliable in-process sensing technologies and associated data analytics that can be employed to enable in-situ monitoring, quality assessment, and even corrective action.

Project Summary

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