基于VIT网络的复合材料栓接结构挤压极限快速预测方法研究  

A Rapid Prediction Method for the Bearing Limit of Composite-bolted Joints Based on Vision Transformer Networks

作　　者：任宇[1] 刘禹铭 林清源 赵勇[1] 程辉 REN Yu;LIU Yuming;LIN Qingyuan;ZHAO Yong;CHENG Hui(Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures,Shanghai Jiao Tong University,Shanghai 200240,China;Shanghai Aerospace Equipment Manufacturing Co.,Ltd.,Shanghai 200245,China)

机构地区：[1]上海交通大学上海市复杂薄板结构数字化制造重点实验室,上海200240 [2]上海航天设备制造总厂有限公司,上海200245

出　　处：《上海航天(中英文)》2025年第2期121-134,共14页Aerospace Shanghai(Chinese&English)

基　　金：国家重点研发计划资助项目(2019YFA07090001);国家自然科学基金资助项目(51975349)。

摘　　要：针对复合材料栓接结构(CBJ)多源装配要素耦合作用下挤压极限预测难题及传统数值模拟效率低下等问题,提出一种基于Vision Transformer(VIT)框架的挤压极限快速预测方法。通过融合装配过程中几何形变参数与物理性能参数,构建多源装配参数空间与挤压极限的非线性映射模型。首先,针对复材栓接结构装配过程的几何参量和性能参量进行分析与建模;其次,创新开发基于VIT架构的CBJ-VIT深度学习网络,采用多头自注意力机制实现多模态装配数据特征融合;最后,以航天薄壁结构复合材料栓接装配体为研究对象进行了实例验证。实验表明:CBJ-VIT模型预测结果与有限元分析结果高度一致,单次预测耗时从传统数值模拟的12.0 h降至8.1 s。在定性和定量评价中,该模型相较传统非图像数据处理方法预测精度提升85.02%,较非VIT架构模型精度提高76.24%。In response to the challenges of predicting bearing limit of composite-bolted joints under the coupling effects of multi-source assembly factors,as well as the low efficiency of traditional numerical simulations,this paper proposes a rapid prediction method for the bearing limit based on the Vision Transformer(VIT)framework.By integrating the geometric deformation parameters and physical performance parameters during the assembly process,a nonlinear mapping model between the multi-source assembly parameter space and bearing limit is established.First,the geometric and performance parameters of the composite-bolted joint assembly process are analyzed and modeled.Then,an innovative CBJ-VIT deep learning network based on the VIT architecture is developed,utilizing a multi-head selfattention mechanism to achieve the feature fusion of multimodal assembly data.Finally,case studies are conducted with aerospace thin-walled composite-bolted assemblies for validation.The experimental results indicate that the predicted results from the CBJ-VIT model align closely with the finite element analysis results,reducing the time required for a single prediction from the traditional numerical simulation time of 12 h to just 8.1 s.In both qualitative and quantitative evaluations,the model shows an 85.02%improvement in the prediction accuracy compared with traditional non-image data processing methods and a 76.24%accuracy increase compared with non-VIT architecture models.

关 键 词：复合材料 栓接结构 多源因素耦合 形性融合 Vision Transformer(VIT) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TH113[自动化与计算机技术—计算机科学与技术]