LLNL researchers develop real-time defect detection in metal Additive Manufacturing
March 7, 2023
A team of engineers and scientists from Lawrence Livermore National Laboratory (LLNL), California, USA, has developed a method for detecting and predicting strut defects in additively manufactured metal lattice structures during the build stage. The process, involving a combination of monitoring, imaging techniques and multi-physics simulations, enables users to determine if the part will satisfy quality requirements at the earliest possible stage.
The high-strength and low-density properties of metallic lattices have found applications in many fields. However, during the Laser Beam Powder Bed Fusuon (PBF-LB) process, missing or defective struts can occur that affect the mechanical behaviour of the lattice structure. To ensure quality, scientists typically perform a post-build inspection, which takes time and is not always possible, especially with complex builds.
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As described in a paper recently published in Additive Manufacturing Letters, LLNL researchers monitored the Additive Manufacturing of a metallic micro-lattice structure using a photodiode, a pyrometer — both of which measure light emitted from the melt pool — and thermal imaging. The team produced normal struts and intentionally defective ‘half-struts’ through the PBF-LB process, measuring the thermal emissions from the melt pool. The researchers then developed a method to use those thermal emissions to predict defects with high accuracy.
“For the first time, this quality-control process was studied in metallic lattices and we developed a methodology to detect defective struts with a missing bottom part,” stated Jean-Baptiste Forien, lead author and LLNL staff scientist. “At the moment, we are capable of detecting defects that span multiple layers, but in the future, new methods will be developed to identify defects within a printed layer. This will allow a dynamic reaction and potentially the suppression of the defect before resuming printing of the rest of the build.”
The team developed their defect detection/prediction method based on observations from the test builds, high-speed imaging and multi-physics simulations of the PBF-LB melt pool. By monitoring thermal emission, researchers could predict whether a strut was present or missing with accuracy of more than 94%, validating the mechanisms behind the observed thermal emissions through high-speed thermal and optical imaging, and simulations using the ALE3D (Arbitrary Lagrangian–Eulerian three-dimensional analysis) multiphysics simulation tool developed by LLNL.
Co-authors of “Detecting missing struts in metallic micro-lattices using high speed melt pool thermal monitoring” included LLNL scientists and engineers Gabe Guss, Saad Khairallah, William Smith, Philip DePond, Manyalibo “Ibo” Matthews and principal investigator Nick Calta.