Uncertainty-Aware Parameter Optimization for Reliable Laser Powder Bed Fusion Additive Manufacturing

Laser powder bed fusion (LPBF) is an additive manufacturing process capable of producing intricate structures with high accuracy. Despite this capability, it struggles to achieve the required reliability for mass production—specifically the stability of a production run and repeatability across multiple runs. Parameter optimization, which adjusts process parameters to regulate a specific quantity of interest (QoI), is a crucial means of quality control. Existing methods, however, have not adequately addressed both random and systematic factors in the LPBF process. The stochastic nature of the process is often neglected under the assumption that identical parameter inputs will consistently yield the same QoI. This deviates from reality and is not intended to reduce potential variations in the QoI. Moreover, many studies do not incorporate the systematic neighboring effects between scan tracks into their optimization, so process reliability cannot be guaranteed. To address this issue, this study focuses on optimizing the probability distribution of the QoI. The key idea is not only to increase the likelihood of achieving the ideal QoI but also to reduce its variance. This is achieved by uncertainty-aware modeling and optimization of the LPBF process using machine learning. Specifically, the problem is formulated as maximizing the posterior distribution of scan parameters given an ideal QoI sequence and historical manufacturing data, yielding a large-scale constrained optimization problem. A stochastic, distributed, gradient-based method is proposed to solve this problem, where a coarse-to-fine strategy plays a critical role in accelerating convergence. A case study is then conducted to stabilize the melt pool volume by optimizing laser powers. The solutions are verified in a calibrated finite element-based simulation environment, in which the variations of the melt pool volume are effectively reduced both within a single run and across multiple runs. The implementation of our method is available at https://github.com/qihangGH/uncertainty_aware_param_optim_for_AM. Note to Practitioners—This paper is motivated by the critical reliability issues in laser powder bed fusion (LPBF) additive manufacturing, requiring that the process is stable within a run and repeatable across runs. Parameter optimization is an effective way to control a certain quantity of interest (QoI) to regulate the LPBF process. Existing methods, however, often ignore the stochastic variations and neighboring effects in the LPBF process. The objective solely focuses on minimizing the error between the predicted QoI and ideal one, under the assumption that the environment is deterministic. The optimized parameters, therefore, do not guarantee reliability under the influences of random factors. This paper intends to improve the reliability through uncertainty-aware modeling and optimization of the LPBF process. It focuses on optimizing the probability distribution of the QoI, encouraging the ideal QoI not only more likely to appear but also appear with less fluctuations. By incorporating important neighboring effects and bounded and smooth input constraints, a large-scale constrained optimization problem is formulated. To solve this problem efficiently, a stochastic and distributed gradient-based optimization algorithm is proposed, and a coarse-to-fine strategy is employed to accelerate the optimization process. For validation, a case study is conducted to stabilize the melt pool volume by regulating laser powers. The optimized laser powers are verified in a finite element-based simulation environment. Quantitative and qualitative results show that the variations of the melt pool volume within a single run and across repeated runs are reduced, indicating the potential of the uncertainty-aware optimization for enhancing the reliability of the LPBF process.