Probabilistic Data-Driven Modeling of a Melt Pool in Laser Powder Bed Fusion Additive Manufacturing

Abstract

The widespread adoption of laser powder bed fusion (LPBF) additive manufacturing is hampered by process unreliability problems. Modeling the melt pool behavior in LPBF is crucial to develop process control methods. While data-driven models linking melt pool dynamics to specific process parameters have shown appreciable advancements, existing models often oversimplify these relationships as deterministic, failing to account for the inherent instability of LPBF processes. Such simplifications can lead to overconfident and unreliable predictions, potentially resulting in erroneous process decisions. To address this critical issue, we propose a probabilistic data-driven approach to melt pool modeling that incorporates process noise and uncertainty. Our framework formulates a problem that includes distribution approximation and uncertainty quantification. Specifically, the Gaussian distribution with higher order priors, aided with variational inference and importance sampling, is used to approximate the probability distribution of melt pool characteristics. The uncertainty inherent in both LPBF process data and the modeling approach itself are then decomposed and approximated by using Monte Carlo sampling. The melt pool model is improved further by using a novel grid-based representation for the neighborhood of a fusion point, and a neural network architecture designed for effective feature fusion. This approach not only refines the accuracy of the model but also quantifies the uncertainty of the predictions, thereby enabling more informed decision-making with reduced risk. Two potential applications, including LPBF process planning and anomaly detection, are discussed. The implementation of our model is available at https://github.com/qihangGH/probabilistic_melt_pool_model. Note to Practitioners—Modeling the melt pool behavior in laser powder bed fusion (LPBF) processes is pivotal for enhancing its quality control. However, a problem is that most existing data-driven melt pool models learn melt pool behavior with a deterministic function, which predicts the same outputs if its inputs are the same. This deviates from the reality and neglects the uncertainty in LPBF processes. As a consequence, the quality control methods based on such melt pool models lack required reliability. In response to these challenges, this work proposes to model melt pool behavior by using probability distributions with deep learning techniques, which can quantify the uncertainty in both LPBF process data and data-driven models. Aided with an elegantly designed representation for the neighborhood of a fusion point as model input, and a neural network architecture that fuses multi-modal data, the proposed model achieves accurate melt pool size prediction results. More importantly, this work quantifies and decomposes the prediction uncertainty. By accounting for noise and parameter variations, the probabilistic modeling models developed herein offer a more robust foundation for LPBF quality control than the existing ones. They can be readily applied by practitioners to perform improved process planning, defect prognosis, and real-time anomaly detection tasks.