Benchmark models for conduction and keyhole modes in laser-based powder bed fusion of Inconel 718

This study proposes a set of novel benchmarks to detect conduction and keyhole modes in Laser-Based Powder Bed Fusion (LB-PBF) of Inconel 718. These proposed detection benchmarks for power transition mode in LB-PBF helps to establish a process window to obtain desirable part quality and improve the mechanical properties. The benchmark identifies the range of process parameters for obtaining and optimising a meltpool depth with fewer defects such as keyholes to ensure that the LB-PBF process is running in a steady state. In this study, four distinct test cases were simulated and compared with the experimental test data to compare existing and novel benchmarks for the prediction of keyhole conduction modes. Then six different test cases that produce low to high melting temperature were selected to form a shallow to deep meltpool. Numerical CFD simulation (Flow-3D V12) was completed for these scenarios and simulated depth and width of the meltpool are calculated. These simulation results were verified according to the measurement of experimentally fabricated test coupons with three repetitions. The proposed benchmarks provide an accurate criterion for the transition from conduction to keyhole transition mode. Results showed that the process temperature and thermophysical properties in LB-PBF strongly drive the observed meltpool features such as depth. By controlling the transition from conduction to keyhole mode the bonding quality can be controlled leading to enhanced quality of the printed components. The original contribution of this paper is to assess the predictive capability of existing transition benchmarks and to provide novel and coherent benchmarks based on a detailed description of the process parameters and the thermophysical properties of the feedstock. The novel benchmarks perform better than the current benchmarks and can be applied for a wide range of process parameters and different materials.