Probabilistic predictive control of porosity in laser powder bed fusion

This work presents a Bayesian methodology for layer-by-layer predictive quality control of an additively manufactured part by integrating physics-based simulation with online monitoring data. The model and the sensor data are first used to infer porosity in the printed layers, prediction of porosity in future layers, and adjustment of process parameters. Since porosity is not directly observable during the printing process, the temperature profile obtained from the monitoring (using an infra-red thermal camera) is used to infer porosity in the finished part. The porosity inference model is constructed by first reducing the dimension of the thermal images by employing singular value decomposition. Next, in process control, the porosity in the final part is predicted, and if this predicted porosity is more than a desired threshold, the process parameters for printing the next layer are adjusted based on optimization. To ensure that the prediction model is both fast and accurate, the expensive finite element model is replaced by a surrogate model, and a discrepancy term calibrated using experimental data is used to correct the surrogate model prediction. The prediction model is also updated at every layer based on the monitoring data, and the updated model is used to predict the porosity in the final part. The effectiveness of the proposed method is demonstrated for controlling porosity in laser powder bed fusion by changing the process parameters such as laser power and laser speed.