基于BP和GA的激光焊接热源模型参数优化
发布时间：2018-09-03 06:17
【摘要】：通过BP神经网络与遗传算法GA对激光焊接有限元模拟中的热源模型参数进行优化,实现了对激光焊接温度场的精确模拟。选取面体热源模型对激光焊接温度场进行了有限元模拟,将模拟中难以确定并且对结果影响较大的热源有效功率系数、热能分配系数和热源作用半径作为输入量,以有限元模拟结果的误差作为输出量对BP神经网络进行训练,得到具有一定预测能力的神经网络,并形成结合神经网络和遗传算法的参数优化方法。结果表明,经过参数优化后的激光焊接有限元模拟具有较高的精度。
[Abstract]:The heat source model parameters in laser welding finite element simulation are optimized by BP neural network and genetic algorithm GA, and the accurate simulation of laser welding temperature field is realized. The surface heat source model is selected to simulate the temperature field of laser welding by finite element method. The effective power coefficient, thermal energy distribution coefficient and the radius of action of heat source, which are difficult to determine in the simulation and have a great influence on the results, are taken as the input quantity. The BP neural network is trained with the error of the finite element simulation result as the output, and the neural network with certain predictive ability is obtained, and a parameter optimization method combining the neural network and genetic algorithm is formed. The results show that the finite element simulation of laser welding with optimized parameters has high accuracy.
【作者单位】： 武汉理工大学现代汽车零部件技术湖北省重点实验室;武汉理工大学汽车零部件技术湖北省协同创新中心;武汉理工大学汽车工程学院;
【基金】：国家自然科学基金资助项目(51305317) 中国汽车产业创新发展联合基金(U1564202) 湖北省自然科学基金重点项目(ZRS2014000009)
【分类号】：TG456.7
本文编号：2219136
[Abstract]:The heat source model parameters in laser welding finite element simulation are optimized by BP neural network and genetic algorithm GA, and the accurate simulation of laser welding temperature field is realized. The surface heat source model is selected to simulate the temperature field of laser welding by finite element method. The effective power coefficient, thermal energy distribution coefficient and the radius of action of heat source, which are difficult to determine in the simulation and have a great influence on the results, are taken as the input quantity. The BP neural network is trained with the error of the finite element simulation result as the output, and the neural network with certain predictive ability is obtained, and a parameter optimization method combining the neural network and genetic algorithm is formed. The results show that the finite element simulation of laser welding with optimized parameters has high accuracy.
【作者单位】： 武汉理工大学现代汽车零部件技术湖北省重点实验室;武汉理工大学汽车零部件技术湖北省协同创新中心;武汉理工大学汽车工程学院;
【基金】：国家自然科学基金资助项目(51305317) 中国汽车产业创新发展联合基金(U1564202) 湖北省自然科学基金重点项目(ZRS2014000009)
【分类号】：TG456.7
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