A Comparative Review Of Simulation Techniques For Powder Bed Fusion Additive Manufacturing

Abstract

The integration of Powder Bed Fusion (PBF) additive manufacturing with digital development workflows has driven widespread use of simulation techniques, particularly Finite Element Analysis (FEA), for enhancing process understanding and validating component performance. However, process-induced defects, thermal history, and inherent anisotropy in PBF result in mechanical properties that differ significantly from those of conventionally manufactured components, creating challenges for reliable property prediction. This paper presents a comparative review of the current state of research on simulation techniques for PBF additive manufacturing. The reviewed literature is categorised into finite element methods, statistical approaches, and machine learning techniques. Comparative analysis indicates that Additive FEA, combined with cost function-based optimization, offers a practical balance between computational efficiency and predictive accuracy. Statistical approaches provide higher reliability but involve significant complexity. Consequently, machine learning techniques demonstrate strong potential for mechanical property prediction, however, extensive validation is still required before integration into digital development frameworks.