多钉铆接作为飞机结构关键紧固工艺之一，对飞机结构整体承载能力具有重要影响。传统设计优化方法在高维数据处理、计算成本控制与工程实用性平衡方面存在局限。本文提出一种基于条件生成对抗网络（CGAN）的多钉铆接结构设计优化方法，通过将物理规律、轻量化目标与工程约束融入深度学习模型，实现高效协同优化。针对纯数据驱动模型易违背力学原理的缺陷，将多钉载荷分布与剪切破坏判定条件嵌入CGAN生成器损失函数；为实现结构轻量化设计，在构建生成器损失函数时考虑多钉结构质量最小化。此外，神经网络模型中加入了工程制造约束，确保生成的设计参数符合制造可行性要求。建立基于Johnson-Cook本构与失效准则的有限元模型，经试验结果校准后，形成“试验-仿真”融合数据集，覆盖更广泛的参数范围。引入Wasserstein距离防止梯度归零或爆炸，提高神经网络训练稳定性。训练结果显示，生成器与判别器可达成稳定对抗平衡，强度与重量损失函数均收敛，验证了模型对“强度-轻量化”多目标的协同优化能力。在多钉铆接结构拉伸工况下，通过自动优化铆钉直径、排布参数及被连接件厚度，实现满足强度设计要求的结构减重9%以上，并能够在300ms时间内生成优化设计方案。研究表明本文提出的CGAN优化方法可快速生成符合工程实际的结构优化设计方案，大幅缩短设计周期、降低研发成本，为飞机多钉铆接结构的高性能、轻量化设计提供全新的技术手段。

Multi-rivet fastening is one of the key technologies for aircraft structures, exerting a crucial influence on the overall load-bearing capacity of aircraft structures. Traditional optimization methods have limitations in balancing high-dimensional data processing, computational cost control and engineering applicability. This paper proposes a multi-rivet joint structural design optimization method based on Conditional Generative Adversarial Networks (CGAN). By integrating physical laws, lightweight objectives and engineering constraints into the deep learning model, efficient collaborative optimization is achieved. Aiming at the defect that pure data-driven models tend to violate mechanical principles, the multi-rivet load distribution and shear failure criteria are embedded into the loss function of the CGAN generator. To realize structural lightweight design, the minimization of the multi-rivet structure mass is considered when constructing the generator loss function. In addition, engineering manufacturing constraints are incorporated into the neural network model to ensure that the generated design parameters meet the requirements of manufacturing feasibility. A finite element model based on the Johnson-Cook constitutive model and failure criterion is established. After calibration with test results, a "test - simulation" integrated dataset is formed, covering a wider range of parameters. The Wasserstein distance is introduced to prevent gradient vanishing or explosion, thus improving the stability of neural network training. The training results show that the generator and the discriminator can achieve stable adversarial equilibrium, and both the strength and weight loss functions converge, which verifies the model's ability of collaborative optimization for the "strength-lightweight" multi-objective. Under the tensile condition of the multi-rivet joint structure, by automatically optimizing the rivet diameter, arrangement parameters and the thickness of connected components, the structural weight reduction of more than 9% is achieved while satisfying the strength design requirements, and the optimized design scheme can be generated within 300ms. The research shows that the CGAN-based optimization method proposed in this paper can rapidly generate structurally optimized design schemes that are in line with engineering practice, greatly shorten the design cycle, reduce the R&D cost, and provide a new technical means for the high-performance and lightweight design of aircraft multi-rivet joint structures.