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徐加俊, 黄禹, 王璐, 荣佑民. 基于粒子群算法确定热源参数的EH40/316L高功率激光焊接温度场模拟[J]. 焊接学报, 2024, 45(1): 31-39. DOI: 10.12073/j.hjxb.20230202002
引用本文: 徐加俊, 黄禹, 王璐, 荣佑民. 基于粒子群算法确定热源参数的EH40/316L高功率激光焊接温度场模拟[J]. 焊接学报, 2024, 45(1): 31-39. DOI: 10.12073/j.hjxb.20230202002
XU Jiajun, HUANG Yu, WANG Lu, RONG Youmin. Determining heat source parameters based on particle swarm optimization for temperature field simulation of EH40/316L high power laser welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(1): 31-39. DOI: 10.12073/j.hjxb.20230202002
Citation: XU Jiajun, HUANG Yu, WANG Lu, RONG Youmin. Determining heat source parameters based on particle swarm optimization for temperature field simulation of EH40/316L high power laser welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(1): 31-39. DOI: 10.12073/j.hjxb.20230202002

基于粒子群算法确定热源参数的EH40/316L高功率激光焊接温度场模拟

Determining heat source parameters based on particle swarm optimization for temperature field simulation of EH40/316L high power laser welding

  • 摘要: 焊接瞬态温度场对焊接残余应力与变形模拟结果具有重要的影响,为提高EH40/316L高功率激光焊接的温度场模拟精度,开发了用于热源模型经验参数优化的智能计算方法.该方法利用MATLAB软件随机生成热源模型的经验参数,调用ANSYS软件执行焊接温度场模拟的APDL命令,并在计算完成后返回结果数据,然后通过借助粒子群算法的群体智能和进化智能的优点,根据预测结果生成新的热源经验参数,进行迭代计算,直至预测结果为最优结果;同时,为提高收敛速度,将有限元几何模型简化为原模型的1/5.基于该方法,开展了10个热源模型经验参数优化案例的计算,并根据优化结果分析了热源模型的参数敏感性.结果表明,该方法实现了温度场的准确预测,其中10个优化案例的平均计算时间为30.3 h,最大、最小和平均预测误差分别为2.84%,2.06%和2.16%;同时,由预测误差和热源经验参数的响应曲面可知,两者之间具有复杂的非线性关系,其数学函数为多谷函数.

     

    Abstract: The welding transient temperature field has an important influence on the simulation results of welding residual stress and deformation. To improve the accuracy of the temperature field simulation in EH40/316L high power laser welding, an intelligent calculation method is developed to optimize the empirical parameters of heat source model. This method uses MATLAB software to randomly generate the empirical parameters, calls ANSYS software to execute the APDL command of the welding temperature field simulation, and returns the result data when the calculation is completed. Then, based on the swarm intelligence and evolutionary intelligence of particle swarm optimization algorithm, new empirical parameters of the heat source are generated according to the prediction results. The iterative calculation is carried out until the prediction results are the optimal results. 10 cases of empirical parameter optimization are performed, and the parameter sensitivity of heat source model is analyzed according to the optimization results. The results show that the average calculation time of the 10 cases is 30.3 hours, and the maximum, minimum and average prediction errors are 2.84%, 2.06% and 2.16% respectively. This indicates a great improvement in the simulation accuracy of welding temperature field. Meanwhile, it can be seen from the response surface of the prediction error and the empirical parameters, that the mathematical function between them is a multi-valley function and has a complex nonlinear relationship.

     

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