Measuring and Predicting the Effects of Residual Stresses from Full-Field Data in Laser-Directed Energy Deposition

This article presents a novel approach for assessing the effects of residual stresses in laser-directed energy deposition (L-DED). The approach focuses on exploiting the potential of rapidly growing tools such as machine learning and polynomial chaos expansion for handling full-field data for measurements and predictions. In particular, the thermal expansion coefficient of thin-wall L-DED steel specimens is measured and then used to predict the displacement fields around the drilling hole in incremental hole-drilling tests. The incremental hole-drilling test is performed on cubic L-DED steel specimens and the displacement fields are visualized using a 3D micro-digital image correlation setup. A good agreement is achieved between predictions and experimental measurements.

Automated summary

This article presents a novel approach that utilizes machine learning and polynomial chaos expansion to assess the effects of residual stresses in laser-directed energy deposition (L-DED). The approach involves measuring the thermal expansion coefficient of thin-wall L-DED steel specimens and using it to predict the displacement field in incremental hole-drilling tests. Experimental measurements from 3D micro-digital image correlation setup show good agreement with the predictions.