Q-Learning Algorithm for Predicting Mechanical Properties of Inconel 718 Processed by Selective Laser Melting

This study introduces a model-free reinforcement learning framework based on Q-Learning (QLA) for the multi-objective optimization of Selective Laser Melting (SLM) process parameters for Inconel 718. To efficiently handle the limited experimental dataset, a tabular Q-Learning approach was implemented, in which each parameter combination was treated as a discrete state and every possible transition as an action. Four key process variables laser power (P), scan speed (S), layer thickness (T), and hatch spacing (H) were optimized for two output responses: relative density (RD) and Vickers hardness (VH). The Q-Learning agent iteratively explored various parameter combinations, observed the resulting material properties, and continuously updated its policy to converge toward optimal conditions. The optimal parameter set identified by the framework was P = 270 W, S = 800 mm/s, H = 0.1 mm, and T = 0.08 mm. Despite relying on only 16 experimental trials, the model achieved exceptionally low prediction errors of 0.0503% for RD and 0.0857% for VH, demonstrating substantial reductions in both experimental effort and material consumption. The results confirm that reinforcement learning can autonomously and effectively identify optimal SLM parameter settings, highlighting its strong potential to enhance precision, efficiency, and overall quality in the additive manufacturing of metallic components.