摘要
采用尖部半螺纹搅拌针进行6151铝合金搅拌摩擦搭接焊试验,并基于径向基神经网络与蚁群算法相结合的方法优化工艺参数,达到改善搭接界面结构特征,进而实现最大化接头承载能力的目的。研究结果表明,当转速、焊速和下压量分别为1504 r/min、207 mm/min和0.12 mm时接头的拉剪载荷最大达5.06 kN/mm,比优化前的最大拉剪载荷提高了6.08%。径向基神经网络与蚁群优化算法相结合的智能方法为优化焊接工艺参数,进一步提高铝合金搅拌摩擦搭接接头强度提供了一种有效途径。
Friction stir lap welding of 6151 aluminum alloy was carried out by using the tip-half-thread pin,and the process parameters were optimized by combining radial basis function neural network(RBFNN)and ant colony optimization(ACO)algorithm,to improve the characteristics of lap interface and maximize the bearing capacity of the joint.The result showed when the rotational velocity,welding speed and plunge depth were 1504 r/min,207 mm/min and 0.12 mm,respectively,the highest tensile shear load of the joint reached 5.06 kN/mm,which was increased by 6.08%than the highest tensile shear load before optimization.The RBFNN combining with ACO provides an effective way to optimize the welding processing parameters and further enhance the strength of aluminum friction stir lap welding joint.
作者
岳玉梅
韩松
郭芮秀
姬书得
YUE Yumei;HAN Song;GUO Ruixiu;JI Shude(Shenyang Aerospace University,Shenyang 110136,China)
出处
《航空制造技术》
CSCD
北大核心
2022年第21期54-61,共8页
Aeronautical Manufacturing Technology
基金
国家自然科学基金(52074184)。
