Multi-Model Collaborative Optimization of Inconel 690 Deposited Geometry in Laser-Directed Energy Deposition: Machine Learning Prediction and NSGA-II Decision Framework

The critical challenge of achieving precise geometric control in laser directed energy deposition (L-DED) of Inconel 690 for nuclear applications is addressed by this study. We established a data-driven optimization framework that reduces time-consuming trial-and-error experiments. A comprehensive process-geometry dataset was generated through full-factor experiments. Pearson correlation analysis revealed significant correlations: strong positive correlations between laser power and bead width (r = 0.82) and depth (r = 0.85), and between powder feed rate and height (r = 0.70). A hybrid machine learning model was subsequently developed. It used a Backpropagation Neural Network (BPNN) to achieve excellent prediction of width, height, and depth (R2 ≤ 0.962). It also generated 100 uniformly distributed Pareto optimal process parameter sets via the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Experimental validation confirmed the model’s high predictive accuracy—relative error ≤ 5% for width/depth, and a maximum relative error of 5.34% for height. This demonstrates the framework’s effectiveness for reliable multi-objective process optimization in high-precision deposition tasks. It also highlights its potential for use in nuclear component repair and other material systems.