20.500.12556/DKUM-96683 Precision blade manufacturing: small-sample prediction and optimization using improved meta-learning and Particle Swarm Optimization 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. precise manufacturing optimization meta-learning optimization machine learning small sample learning Particle Swarm Optimization PSO natančna izdelava strojno učenje optimizacija true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Članek v reviji 2026-01-23 08:32:48 2026-01-23 08:32:49 2026-03-11 11:43:04 0000-00-00 00:00:00 2025 0 0 str. 369-379 no. 3 Vol. 20 Sep. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-04-07 2025-08-29 2025-10-31 658.5 265833219 10.14743/apem2025.3.546 1854-6250 APEM20-3_369-379.pdf APEM20-3_369-379.pdf 1 F033F9C60541039F2905701F7DF25269 226c6135d88d6547f9a3bb448c5136b93e3491ac4b7d5a375547a732e2e6c523 ec8ad1e7-f82d-11f0-8c23-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202475 https://apem-journal.org/Archives/2025/Abstract-APEM20-3_369-379.html 0 f1e34384-f82d-11f0-8c23-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202476 Fakulteta za strojništvo Advances in production engineering & management 0 0 0