Energy-based performance prediction for metals in powder bed fusion

The mechanical performance of metallic parts fabricated by powder bed fusion (PBF) additive manufacturing is directly linked to process variables. However, it remains challenging to rapidly forecast the resulting mechanical performance based on specified process conditions. To tackle this issue, this paper proposes a process -performance prediction model capable of efficiently estimating the yield strength (YS) and ultimate tensile strength (UTS) via used process parameters. The formation quality of metals related to energy absorptivity/ consumption is first determined based on the energy balance principle during PBF. Subsequently, the effective properties of as-built metals with process-induced defects are obtained using a homogenization method. Considering the relation between the thermal energy and elastoplastic strain energy, the YS and the UTS of printed parts are effectively predicted based on the force-heat equivalence energy density principle. The accuracy of the proposed model is validated by the comparison with the literature. Furthermore, the effect of the main process variables on the YS and the UTS of printed metallic parts is demonstrated and analyzed. The results show an increase of YS and UTS followed by a gradual decrease with the key process variables increasing, including the volumetric energy density, length of scan vector, layer thickness, and environment temperature. These results can serve as a guideline for improving the mechanical performance of PBF-printed metals.

Automated summary

This paper proposes a process-performance prediction model for estimating the yield strength and ultimate tensile strength of metallic parts fabricated by powder bed fusion additive manufacturing. The effect of main process variables on the mechanical performance of printed metallic parts is analyzed and the results can serve as a guideline for improvement. The accuracy of the proposed model is validated by comparison with literature.