Deformations modelling of metal additively manufactured parts and improved comparison of in-process monitoring and post-process X-ray computed tomography

The comparison of potential defects detected through in-process real-time monitoring systems and actual defects measured in the fabricated parts by X-ray computed tomography offers relevant opportunities for improving the understanding, sustainability and precision of metal laser powder bed fusion processes. However, the comparison outcome strongly depends on data alignment accuracy, which is hindered by typical process-induced part deformations arising during and after production. This work presents a methodology that includes the modelling of part deformations for improving the alignment and comparison of in-process and post-process datasets. The methodology was successfully implemented, starting from deformations predicted by process simulations, and verified experimentally by producing samples including fiducials specifically designed to provide insights on local deformations. Results show an improved data alignment accuracy, which is fundamental for enabling the establishment of robust correlations aiding the reduction of false positives and false negatives in the in-process gathered signals. The approach is also found to be effective in accurately categorizing non-significant process signatures occurring during the fabrication, hence preventing the implementation of wrong corrective actions.