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Deterministic computational modeling of laser powder bed fusion (LPBF)process fails to capture irregularities and roughness of the scan track, unlessexpensive powder-scale analysis is used. In this work we developed a stochasticcomputational modeling framework based on Markov Chain Monte Carlo (MCMC)capable of capturing the irregularities of LPBF scan. The model is calibratedagainst AFRL single track scan data using a specially designed tensordecomposition method, i.e., Higher-Order Proper Generalized Decomposition(HOPGD) that relies on non-intrusive data learning and construction of reducedorder surrogate models. Once calibrated, the stochastic model can be used topredict the roughness and porosity at part scale at a significantly reducedcomputational cost compared to detailed powder-scale deterministic simulations.The stochastic simulation predictions are validated against AFRL multi-layerand multitrack experiments and reported as more accurate when compared withregular deterministic simulation results.

Stochastic additive manufacturing simulation: from experiment to surface roughness and porosity prediction