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Process parameter control is critical for reducing casting defects in ZTA2 alloy pump body investment casting. However, there exists a complex nonlinear relationship between parameters such as pouring temperature, pouring time, and shell preheating temperature, and defects including total defect volume, shrinkage porosity, and shrinkage cavities, posing significant challenges to accurate prediction and optimization. To address this issue, this study proposes an integrated strategy for defect prediction and process optimization that combines the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Backpropagation Neural Network (BP neural network). First, an L25(53) orthogonal experiment was designed, and a dataset consisting of 25 orthogonal samples and 97 random samples was constructed by combining ProCAST simulations, covering the entire parameter domain of pouring temperature, pouring time, and shell preheating temperature. Subsequently, the PSO algorithm was used to optimize the initial weights and thresholds of the BP neural network, and Bayesian regularization and 5-fold cross-validation were introduced to build a high-precision defect prediction model. The SHapley Additive exPlanations (SHAP) analysis was employed to clarify parameter sensitivity and interaction mechanisms, and the NSGA-II was combined to realize multi-objective process optimization. The results show that: compared with the traditional BP neural network, the optimized PSO-BP model improves the coefficient of determination (R2) of the test set for total defect volume prediction by 20.82% and reduces the root mean square error (RMSE) by 33.34%; for shrinkage porosity volume prediction, the R2 is increased by 7.93% and the RMSE is reduced by 22.71%, which effectively solves the problems of local optimization and weak generalization ability. Pouring time is the most sensitive parameter affecting defects, and the coupling effect between pouring temperature and pouring time is the strongest. Considering actual production conditions, the superior process parameters are determined as follows: pouring temperature of 1800 °C, pouring time of 4 s, and shell preheating temperature of 475 °C. Compared with the pre-optimization results, this parameter combination reduces the total defect volume by 38.92% and the shrinkage porosity volume by 51.62%. The intelligent optimization framework constructed in this study provides reliable technical support for the accurate control of defects in ZTA2 titanium alloy pump body investment casting, and has important engineering value for improving the quality of castings in industrial production and reducing costs.
HE et al. (Tue,) studied this question.