Data driven residual stress simulation for additive manufacturing - ENGIN-X

Residual stress (RS) remains a critical challenge in metal additive manufacturing (AM), potentially undermining component performance and reliability. Neutron diffraction offers a non-destructive alternative that probes deep within components and captures all three stress components. Leveraging this capability, the proposed work integrates neutron diffraction measurements, surrogate finite element analysis (FEA), and machine learning (ML) to refine and validate RS predictions in laser powder bed fusion (LPBF) steel specimens.

Two distinct sample geometries will be examined: one for model refinement (via extensive RS mapping) and another for validation. The neutron diffraction data will serve as ground-truth inputs to improve the ML-driven FEA, ensuring enhanced predictive accuracy. The outcomes of this research not only demonstrate the efficacy of neutron diffraction in facilitating robust RS assessment and model validation but also promote greater confidence in AM processes for industrial adoption.