Prediction and Optimization of Freeform Impeller Machining Parameters Using a Hybrid Taguchi-Artificial Neural Network Model with the Levenberg–Marquardt Algorithm

Abstract

Freeform machining of impellers involves extended cycle times, leading to high energy consumption and costs necessitating efficient process optimization. This study develops a CAD/CAM-integrated hybrid Taguchi-Artificial Neural Network (ANN) model to optimize machining parameters for a freeform impeller. Four controllable factors, namely cutting feed (Cf), feed Z (Fz), retract feed (Rf), and cutter diameter (CD), were investigated at five levels using an L25 orthogonal array, with machining time as the response. Taguchi analysis identified cutting feed as the most dominant factor, while retract feed was insignificant, and a first-order regression model yielded an R2 of 95.88%. A two-layer feedforward neural network with six hidden neurons achieved an R2 of 0.9999 and a mean absolute error of 0.0976 min. To rigorously validate generalization, leave-one-out cross-validation was employed, identifying three hidden neurons as optimal with a cross-validated R2 of 0.9823, RMSE of 0.5350 min, and MAE of 0.3429 min. The final model trained on all samples achieved an R2 of 0.9996. Comparison with a quadratic regression model on the same test set confirmed the superior predictive capability of the ANN (R2=0.9992 vs. 0.9983). Optimal parameters (Cf=12,000 mm/min, Fz=600 mm/min, Rf=4000 mm/min, CD=6 mm) were validated through simulation, yielding a machining time of 11.05 min, representing a 52.6% reduction from 23.32 min. The hybrid Taguchi–ANN framework effectively optimizes freeform impeller machining, significantly enhancing productivity while maintaining process reliability.