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.