基于BPNN与NSGA-Ⅱ的翼身整流罩复合材料壁板调形工艺参数优化方法

doi:10.19936/j.cnki.2096-8000.20260828.016

摘要/Abstract

摘要： 翼身整流罩复合材料壁板在制造后易产生外形偏差,影响飞机的气动性能,装配过程中需进行调形。针对其调形过程中优化求解效率低与调形受力难以有效控制的问题,提出一种基于反向传播神经网络(Back Propagation Neural Network,BPNN)与非支配排序遗传算法Ⅱ(Non-dominated Sorting Genetic Algorithm Ⅱ,NSGA-Ⅱ)的复合材料壁板调形工艺参数优化方法。建立了壁板变形与受力预测模型,并将其嵌入多目标优化算法,基于理想点法求解得到兼顾外形精度与调形受力的最优调形单元布局与驱动量。实验结果表明,所提方法在保证预测精度的同时提升了优化效率,为复合材料壁板的高质量装配调形提供了可靠技术路径。

关键词: 飞机装配, 复合材料壁板, 调形工艺参数, BPNN, 多目标优化

Abstract: Wing-body fairing composite panels are prone to shape deviations after manufacturing, which can degrade the aerodynamic performance of aircraft and therefore require shape adjustment during assembly. To address the low optimization efficiency and the difficulty in effectively controlling adjustment force during the shape adjustment process, an optimization method for shape adjustment process parameters based on back propagation neural network (BPNN) and non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was proposed. Deformation and force prediction models for the panel to be assembled were established and embedded into a multi-objective optimization framework. Using the ideal point method, the optimal layout and driving displacements of adjustment units were efficiently obtained by simultaneously considering contour accuracy and adjustment force. Experimental results show that the proposed method improves optimization efficiency while ensuring prediction accuracy, and provides a reliable technical route for the high-quality assembly and shape adjustment of composite panels in practice.

Key words: aircraft assembly, composite panels, shape adjustment process parameters, BPNN, multi-objective optimization

中图分类号:

TB332

李欣欣, 赵聪, 安鲁陵, 张学壮, 王琦. 基于BPNN与NSGA-Ⅱ的翼身整流罩复合材料壁板调形工艺参数优化方法[J]. 复合材料科学与工程, 2026, 0(8): 124-135.

LI Xinxin, ZHAO Cong, AN Luling, ZHANG Xuezhuang, WANG Qi. Optimization method of shape adjustment process parameters for composite panels of aircraft wing-body fairing based on BPNN and NSGA-Ⅱ[J]. COMPOSITES SCIENCE AND ENGINEERING, 2026, 0(8): 124-135.

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