摘要
高超声速飞行器热防护瓦胶接质量直接影响隔热性能和飞行安全。目前胶接工艺主要由人工严格遵循既定的工艺顺序完成,其动态复杂、严格时序的特点使得操作顺序错误与零件混装问题频发,亟需智能化的时序行为识别与管控手段。因此,本文在定义瓦块胶接工艺时序行为的基础上,通过将SimAM无参注意力机制融入到C3D网络中,构建了面向胶接工艺时序行为识别的SimA3D模型;引入余弦退火动态学习率策略配合自适应AdamW优化器,提高模型收敛稳定性;提出三重协同数据增强策略,扩充样本多样性和输入数据的复杂度,显著缓解时序行为小样本下的过拟合问题。试验结果表明,SimA3D模型取得了98.32%的胶接工艺行为识别准确率,准确率较基线C3D网络提升了19.9个百分点。
The gluing quality of thermal protection tile on hypersonic vehicles directly affects thermal insulation performance and flight safety.Current gluing process predominantly relies on manual operations strictly following established procedures.However,their dynamic complexity and strictly time-sequenced characteristics lead to frequent occurrences of operational sequence errors and component mis-assemblies,necessitating intelligent temporal behavior recognition and monitoring methods.To address these challenges,this study first defines the temporal behavioral characteristics of tile gluing process.Subsequently,we construct the SimA3D model for temporal behavior recognition by integrating the SimAM parameter-free attention mechanism into the C3D network architecture.A cosine annealing dynamic learning rate strategy is introduced in conjunction with an adaptive AdamW optimizer to enhance model convergence stability.Furthermore,a triple collaborative data augmentation strategy is proposed to expand sample diversity and input data complexity,effectively alleviating overfitting issues in small-sample temporal behavior recognition scenarios.Experimental results demonstrate that the SimA3D model achieves 98.32%recognition accuracy for gluing process behaviors,and the accuracy is improved by 19.9 percentage points over the baseline C3D network.
作者
郭城达
李泷杲
候国义
施嘉明
黄翔
GUO Chengda;LI Shuanggao;HOU Guoyi;SHI Jiaming;HUANG Xiang(College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《航空制造技术》
北大核心
2026年第5期150-160,共11页
Aeronautical Manufacturing Technology
关键词
热防护瓦
时序行为识别
胶接工艺
深度学习
飞行器装配
Thermal protection tile
Temporal behavior recognition
Gluing process
Deep learning
Aircraft assembly
