Laser scan strategy descriptor for defect prognosis in metal additive manufacturing using neural networks

Abstract

In situ and post-print defects, including excessive residual stresses and poor microstructural properties, are important concerns in the part design and setup stages of powder bed fusion (PBF) manufactured parts. Laser scan strategies are well known to be correlated with the development of these defects; however, due to the lack of complex scan strategy descriptors and the consequential imposition of simple scan strategies, these correlations are not well understood and are difficult to investigate. This work proposes a methodology for an intuitive quantitative descriptor of scan strategies that has the potential to provide quick prognoses to predict defects. To demonstrate this, a neural network is trained to accurately predict post-print residual stress distributions using the descriptor of a specified path. It is envisioned that this descriptor could allow for the circumvention of current costly prevention mechanisms and increase confidence and reliability in metal additive manufacturing technologies.