The joint surface of the 1060 aluminum and AZ31 magnesium alloy was prepared through friction stir lap welding (FSLW) under different welding process parameters. The joint surface was characterized three-dimensionally using a three-dimensional (3D) optical profiler, and the coordinate data were obtained. The fractal dimension of the joint surface was calculated by the box-width transformation method using a MATLAB program. Furthermore, the influence of the welding process parameters on the fractal dimension of the joint surface was studied. The response surface model was established based on the principle of central composite design (CCD), and analysis of variance (ANOVA) was carried out to test the accuracy of the response surface. The results showed that the joint surface morphology had fractal characteristics, and the fractal dimension could be used as an index to characterize the quality of the joint surface. The change of the welding process parameters had a great impact on the fractal dimension of the joint surface, the interaction between the parameters was small, and the fitting accuracy of the response surface model was high. The fractal dimension of the joint surface decreased with the increase in the welding and rotational speeds and the effect of the rotational speed was more significant.

The spindle characteristic signal (forces and vibrations) at different friction stir lap welding (FSLW) parameters were studied. The result indicated that the spindle force and vibration have different trends with the change of welding parameters. For further study, the spindle dynamic performance evaluation model by means of the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS method) was established. The model was used to calculate the relative approach degree B under different welding process parameters. The correlation between the characteristic signal and the joint properties was obtained. The model was validated by mechanical performance testing and microscopic observation. The results showed that the model evaluations were consistent with the experimental results.