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Abstract: 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.
Keywords: precise manufacturing, optimization, meta-learning optimization, machine learning, small sample learning, Particle Swarm Optimization, PSO
Published in DKUM: 23.01.2026; Views: 0; Downloads: 0
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Abstract: Optimizing the multimodal transport route for vehicles is crucial for reducing costs, enhancing efficiency, and minimizing emissions in the vehicle logistics industry. This study addresses several operational challenges, including seasonal fluctuations in vehicle sales, the scheduling of transportation modes, and client-specific order timing requirements. This paper presents a 0-1 integer programming model under carbon trading policy considering the timetable limit, with the objective of minimizing the aggregate costs of transportation, transshipment, short-term storage, time-window penalties, and carbon emissions. A linear weight reduction technique is employed to formulate the Improved Particle Swarm Optimization (IPSO) algorithm with dynamic inertia weights for model resolution. The model and algorithm's efficacy are validated by a real-world case study of multi-modal transport in China. The results reveal that the IPSO algorithm reduced convergence times by 30.38 % and 17.78 % in off-season and peak season data, respectively, compared to the traditional PSO algorithm. Additionally, the optimized multimodal transport solution reduced unit costs by 19.3 % and 14.8 %, respectively. The findings indicate that transport time-liness significantly influences optimal route selection. Factors such as extended short-term storage duration, missed shipping schedules, and expedited orders compel multimodal transport to shift toward road transport. An increase in carbon trading prices effectively encourages a shift from road transport to multimodal transport; however, excessively high carbon trading prices fail to regulate this transition. Furthermore, as transport distance increases, the transport costs and carbon emission advantages associated with multimodal transport also increase correspondingly. This research advances multimodal logistics by integrating seasonal variations and carbon trading into a novel optimization framework.
Keywords: low-carbon multimodal transport, vehicle logistics, route optimization, timetable limit, particle swarm optimization
Published in DKUM: 22.01.2026; Views: 0; Downloads: 0
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Abstract: Efficient scheduling of emergency resources is of great practical significance to ensure the smooth progress of large-scale events and maintain public safety. Therefore, this paper, firstly, proposes a novel emergency rescue system after public emergencies in large-scale sports events. In order to allocate resources, this paper establishes a single-objective model that minimizes the maximum delivery time (the injured waiting in place and being transported to the hospital) on the basis of considering the rescue effect. This paper proposes an improved barebones particle swarm optimization algorithm (IBBPSO) to solve the model. Through case analysis, it is found that compared with the bare-bones particle swarm optimization algorithm (BBPSO) and RBBPSO, the average optimization effect of IBBPSO is 62 % higher than that of RBBPSO and 23 % higher than that of BBPSO. IBBPSO has better performance in solving the resource allocation problem proposed in this paper.
Keywords: emergency rescue system, rescue effect, Bare-Bones particle swarm optimization, emergency medical resource scheduling
Published in DKUM: 02.07.2025; Views: 0; Downloads: 11
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Abstract: This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model’s construction and validated via k-fold cross-validation. Optimization of the ANFIS’s external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error (test RMSE) = 0.2620, and test R$^2$ = 0.8585, proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen’s cooling ability, which has an impact on the surface characteristics of the machined parts.
Keywords: cryogenic machining, cooling impact, Inconel 718, machine learning, adaptive neuro-fuzzy inference system, particle swarm optimization
Published in DKUM: 14.07.2023; Views: 564; Downloads: 48
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Abstract: As manufacturing companies pursue higher-quality products, they spend much of their efforts monitoring and controlling dimensional accuracy. In the present work for dimensional deviation prediction of workpiece in turning 11SMn30 steel, the conventional deterministic approach, such as multiple linear regression and two artificial intelligence techniques, back-propagation feed-forward artificial neural network (ANN) and particle swarm optimization (PSO) have been used. Spindle speed, feed rate, depth of cut, pressure of cooling lubrication fluid and number of produced parts were taken as input parameters and dimensional deviation of workpiece as an output parameter. Significance of a single parameter and their interactive influences on dimensional deviation were statistically analysed and values predicted from regression, ANN and PSO models were compared with experimental results to estimate prediction accuracy. A predictive PSO based model showed better predictions than two remaining models. However, all three models can be used for the prediction of dimensional deviation in turning.
Keywords: artificial neural network, dimensional dviation, particle swarm optimization, regression
Published in DKUM: 12.07.2017; Views: 1255; Downloads: 194
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