Advances in Production Engineering & Management
Volume 20 | Number 3 | September 2025 | pp 369–379
https://doi.org/10.14743/apem2025.3.546
Precision blade manufacturing: Small-sample prediction and optimization using improved meta-learning and Particle Swarm Optimization
Zhang, L.; Wang, Q.; Xia, Y.T.; Xia, Y.L.
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A B S T R A C T
Accurately predicting blade manufacturing deviations from limited experimental data remains challenging due to the complex nonlinear relationship between process parameters and resulting profile deviations in precision casting. To overcome the limitations inherent in traditional approaches and conventional machine learning methods, this study proposes a novel prediction and optimization framework specifically designed for small-sample scenarios, integrating enhanced meta-learning optimization with advanced Particle Swarm Optimization (PSO). We innovatively improve the model-agnostic meta-learning (MAML) algorithm by incorporating a dynamic loss function weighting strategy and a stochastic gradient descent with warm restarts (SGDR) learning rate mechanism, significantly mitigating overfitting and enhancing generalization performance. Additionally, we propose a process parameter optimization model utilizing an improved PSO algorithm with dynamic inertia and adaptive learning factors, designed to effectively navigate high-dimensional optimization landscapes. Experimental validation using orthogonal design data highlights pulling speed as the dominant factor influencing blade deviations (Pearson correlation coefficient (r = 0.67). The optimized parameters—low pulling speed (1.5 mm/min) and high pouring temperature (1530 °C)—achieve an 11.54 % reduction in blade deformation. The improved MAML-based prediction model demonstrates superior accuracy, achieving a mean absolute error (MAE) of 2.566 × 10−4 mm, representing a 21.7 % improvement over traditional Adam optimization methods, and exhibits robust predictive capability (R2 = 0.92) in small-sample contexts. This research not only delivers practical insights and precise parameter recommendations for complex blade manufacturing processes but also establishes a robust methodological framework applicable broadly to precision manufacturing domains characterized by limited data availability.
A R T I C L E I N F O
Keywords • Precise manufacturing; Optimization; Meta-learning optimization; Machine learning; Small sample learning; Particle Swarm Optimization (PSO)
Corresponding author • Xia, Y.T.
Article history • Received 7 April 2025, Revised 25 August 2025, Accepted 29 August 2025
Published on-line • 31 October 2025
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