A differential evaporation model to predict chemistry change of additively manufactured metals

Abstract

The desire for increased performance and functionality has introduced additional complexities to the design and fabrication of additively manufactured (AM) parts. However, addressing these needs would require improved control over local properties using in-line feedback from fast-acting low-fidelity models during the fabrication process. In this regard, differential evaporation is an inherent characteristic in metal AM processes, directly influencing local chemistry, material properties, functionality, and performance. In the present work, a differential evaporation model (DEM) is presented for laser powder bed fusion (LPBF) AM to predict and control the effect of evaporation on chemistry and properties on local and part-wide scales. The DEM model is coupled with an analytical thermal model that is calibrated against 51.2 Ni [at%] nickel titanium shape memory alloy (NiTi SMA) single-track experiments and a multi-layer model that accounts for the AM part’s multi-layer design and the inherent melt pool overlap and chemistry propagation. The combined hierarchical model, consisting of the thermal, evaporation, and multi-layer components, is used to predict location-specific chemistry for LBPF AM fabrication of Ni50.8Ti49.2 [at%] SMAs. Model predictions are validated with values obtained from multi-layer experiments on a commercial LPBF system, resulting in a root mean square error (RMSE) of 0.25 Ni [at%] for predicted Ni content. Additionally, martensitic transformation temperature, Ms, is calculated and compared with empirical data, resulting in an RMSE of 18.6 K. A practical account of the cumulative and propagative thermal-induced evaporation effect on location-specific chemistry is made through this linkage of models. Fundamentally, this model chain has also provided a solution to the forward modeling problem, enabling steps to be taken towards resolving the inverse design problem of determining processing parameters based on desired location-specific properties.