A research team involving Argonne National Laboratory and the University of Virginia has developed a new method for the real-time detection of defects in metal-based 3D printing. The method combines thermographic and high-resolution X-ray imaging with machine learning to identify so-called keyhole pores – structural defects that often occur during laser powder bed fusion and can affect the mechanical properties of additively manufactured components.
While thermal sensors are already used in many modern 3D printing systems for process monitoring, they are generally unable to reliably detect the formation of pores inside the component. The researchers therefore used synchrotron radiation from the Advanced Photon Source to compare X-ray images of the internal structures with the surface temperatures of the molten pool. This showed that the formation of keyhole pores leaves characteristic thermal signatures on the surface that can be captured by cameras.
A machine learning model trained on the basis of the X-ray data was then used to predict pore formation with high temporal resolution using the thermal information alone. The accuracy was sufficient to identify pore events on time scales of less than a millisecond. In the future, further sensor solutions are to be developed on this basis, which could also detect other types of defects and even enable interventions during the printing process in the future.
The development is particularly relevant for applications in aerospace, energy technology and medical technology, where the structural integrity of critical components plays a key role. Closer process control could reduce post-processing costs and improve component quality – an important step towards the wider industrial use of metal additive manufacturing.
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