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轴向挤压接头连接强度性能的模拟与优化

Simulation and Optimization of Connection-Strength Performance of Axial Extrusion Joint.

作者信息

Wu Jianguo, Zhai Jingyu, Yan Yangyang, Lin Hongwei, Chen Siquan, Luo Jianping

机构信息

School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.

School of Mechanical Engineering, Weifang University of Science and Technology, Weifang 261000, China.

出版信息

Materials (Basel). 2022 Mar 25;15(7):2433. doi: 10.3390/ma15072433.

Abstract

Axial extrusion-connection technology is one of the important connection technologies for hydraulic piping systems, with high sealing performance and mechanical strength. In this paper, the finite-element-modeling method is used to simulate the experimental process of the connection strength of the axial extrusion joint. The generation mechanism and calculation method of the connection strength are analyzed. To optimize the joint strength, orthogonal testing and grey correlation analysis are used to analyze the influencing factors of joint strength. The key factors affecting joint strength are obtained as the friction coefficient μ1, μ2 between joint components and the groove angle θ1 of the fittings body. The back-propagation (BP) neural-network algorithm is used to establish the connection-strength model of the joint and the genetic algorithm is used to optimize it. The optimal connection strength is 8.237 kN and the optimal combination of influencing factors is 0.2, 0.4 and 76.8°. Compared with the prediction results of the neural-network genetic algorithm, the relative error of the finite-element results is 3.9%, indicating that the method has high accuracy. The results show that the extrusion-based joining process offers significant advantages in the manufacture of high-strength titanium tubular joints.

摘要

轴向挤压连接技术是液压管道系统重要的连接技术之一,具有高密封性能和机械强度。本文采用有限元建模方法模拟轴向挤压接头连接强度的试验过程,分析了连接强度的产生机理和计算方法。为优化接头强度,采用正交试验和灰色关联分析对接头强度的影响因素进行分析,得出影响接头强度的关键因素为接头部件间的摩擦系数μ1、μ2和管件本体的槽角θ1。利用反向传播(BP)神经网络算法建立接头连接强度模型,并采用遗传算法对其进行优化。优化后的连接强度为8.237 kN,影响因素的最优组合为0.2、0.4和76.8°。与神经网络遗传算法的预测结果相比,有限元结果的相对误差为3.9%,表明该方法具有较高的精度。结果表明,基于挤压的连接工艺在制造高强度钛管接头方面具有显著优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa99/8999510/7a5f67209ac0/materials-15-02433-g001.jpg

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