Abstract Scope |
This work presents a Bayesian methodology for layer-by-layer quality control of an additively manufactured part by integrating a physics-based simulation model with monitoring data acquired from advanced sensors, diagnosis of existing porosity, prediction of porosity in future layers, and adjustment of process parameters. From the data collected by online monitoring of the process after every layer, the porosity in the current (partially finished) part is diagnosed, and the porosity in the final part is estimated, both using a predictive model. A finite element based thermal model is first developed to simulate the powder bed fusion process. The temperature profile obtained from the monitoring is then used with the simulation model to predict porosity in the part being manufactured. If the predicted porosity is more than the specified tolerance, the process parameters for printing the next layer such as laser power and laser speed are adjusted to control porosity. |