Laser powder bed fusion (LPBF) uses a focused, high power laser to repeatedly scan geometric patterns on thin layers of metal powder, which build up to a final, solid 3D part. This process is somewhat limited in that the parts tend to have poorer surface finish (compared to machining or grinding) and distortion due to residual stress, as well as multiple other deficiencies. Typical laser scan strategies are relatively simple and use constant laser power levels. This elicits local variations in the melt pool size, shape, or temperature, particularly near sharp geometric features or overhang structures due to the relatively higher thermal conductivity of solid metal compared to metal powder. In this paper, we present a new laser power control algorithm, which scales the laser power to a value called the geometric conductance factor (GCF). The GCF is calculated based on the amount of solid vs. powder material near the melt pool. The algorithm for calculating GCF is presented along with some basic examples for clarification. Then, we detail the hardware and software implementation on the NIST additive manufacturing metrology testbed (AMMT), which include co-axial melt pool monitoring using a high-speed camera. Six parts were fabricated out of nickel superalloy 625 (IN625) with the same nominal laser power, but with varying GCF algorithm parameters. We demonstrate the effect of tailored laser power on reducing the variability of melt pool intensity measured throughout 3D build. Finally, we contrast the difference between the 'optimized' part vs. the standard build parameters, including the deflection of the final part top surface near the overhang and the variation of surface finish on the down-facing surfaces. Ultimately, the improvements to the in-situ monitoring and ex-situ part qualities demonstrate the utility and future potential tuning and optimizing more complex laser scan strategies.
Laser powder bed fusion (LPBF), scan strategies, additive manufacturing, laser power control, overhang structure, surface roughness