Process Compensated Resonance Testing (PCRT)
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Non Destructive Evaluation↗
Engineering topics
Daniel Vaughan
Publications and source records attributed to Daniel Vaughan.
Convolutional Autoencoder for Defect Detection in Additive Manufacturing
The core idea behind using machine learning (ML) for defect detection is that it can be used to detect flaws as they are being formed in an AM part. As the part is being made, a near-infrared (NIR) sensor records each layer and creates an image of the entire build layer. These images, usually thousands, can be compiled into a ‘3D’ array of the entire part. ML tools, such as a convolutional autoencoder (CAE) can go through these images and highlight potential anomalous regions of your part.
In-Situ Monitoring↗
Nondestructive Evaluation and In-Situ Monitoring Efforts for Additive Manufacturing
Nondestructive evaluation (NDE) is required to determine the quality of additive manufactured (AM) parts due to the inherent variability of AM processes. In-situ monitoring technologies endeavor to characterize the process and part quality during production. Qualifying an in-situ monitoring technology requires a proven, causal correlation between indications in the monitoring data and flaws in the finished part.
James Mavo↗