(ORNL: Oak Ridge, TN) -- Oak Ridge National Laboratory (ORNL) researchers have developed a machine-learning framework for identifying flaws in 3D-printed products using sensor data gathered simultaneously with production, saving time and money while maintaining comparable accuracy to traditional post-inspection.

The ability to quantify this level of confidence during the printing process is new and enables manufacturers to vouch for a product’s safety and reliability when it’s created with the most common metal 3D-printing process.

The approach, developed in partnership with aerospace and defense company RTX, uses a machine learning algorithm trained on CT scans to identify flaws in printed products.

Researchers at ORNL have improved flaw detection to increase confidence in metal parts that are 3D-printed using laser powder-bed fusion. This type of additive manufacturing offers the energy, aerospace, nuclear, and defense industries the ability to create highly specialized parts with complex shapes from a broad range of materials. However, the technology isn’t more widely used because it’s challenging to check the product thoroughly and accurately; conventional inspection methods may not find flaws embedded deep in the layers of a printed part.

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