An enhanced characteristic time-based heat input model for simulating laser heating in additive manufacturing

The Characteristic Time-Based Heat Input (CTI) model is an efficient agglomerated laser approach for simulating a heat source in additive manufacturing, significantly reducing computational costs while maintaining accuracy. In the CTI model, Goldak's moving heat source is applied to laser tracks using a characteristic heating time, based on the ratio of the heat source axis to the scanning speed. However, this characteristic heating time is valid only for material points along the central line of the deposited track, which may lead to applying inaccurate power density distribution at other points within the track. This study enhances the CTI model by reformulating the characteristic heating time through volume averaging the laser exposure time for all points within the semi-elliptical cylinder, achieving more precise temperature predictions while preserving computational efficiency. The improvements of the enhanced CTI (ECTI) model are evaluated through a comparative study with experimental data, the detailed Goldak model, and the original CTI model, focusing on laser-directed energy deposition (DED) of two Ti-6Al-4V components: a thin-wall and a cone. The refined characteristic heating time in the ECTI model demonstrates significant mitigation of temperature history deviation for DED (>7.6% error reduction). The ECTI model shows close agreement with Goldak for residual stress predictions, validated experimentally using the contour method on the cone, and aligns well with experimental measurements of distortion. Additionally, the computational time for the ECTI model is only 23% of that for the Goldak model in the thin-wall geometry and 19.3% in the cone geometry. • Enhanced CTI model was developed by applying an accurate heating time. • ECTI reduces temperature history error to 2.6% (thin-wall) and 7.6% (cone case). • ECTI distortion predictions agree well with experiment and Goldak results. • ECTI cuts runtime to 23% (thin-wall) and 19.3% (cone) of Goldak simulation.