The deformation analysis, prediction, and experiment verification for thin-wall part assembly based on the fractal theory model with WNNM

Abstract

Under the load conditions of clamping force and riveting force, the part deformation problems are induced in the assembly process. For the component deformation problems of the thin-wall part assembly, in this paper, from the viewpoint of micro-aspect, the fractal theory model is proposed to analyze the relationship between the part deformation, loads, and contact area for rivet-joint parts; the micro-deformation values for different stages are solved and obtained through theoretical analysis. According to the self-similarity of fractal theory model, the change of the micro-convex body for contact surface will be researched to reflect the macro-deformation properties. Through the measuring experiment for thin-wall parts, the actual three-dimensional coordinate value of each measuring point is obtained before and after riveting assembly, and then, the wavelet neural network method (WNNM) is adopted to predict experiment deformation value based on the obtained deformation value after processing the coordinate value through fitting surface method. Hereby, according to the deformation value for every thin-wall part after processing experiment data, the fractal dimension value will be determined based on the fractal statistic method to analyze the deformation status of different stages. Through analysis and comparison, the results show that the variation regularity of deformation value based on two or three fractal deformation stages is well conformed to and similar to the experimental value and prediction value, which also verify the correctness and effectiveness of the theoretical model, and provide a theoretical basis for further research on thin-wall part assembly deformation from a micro-perspective and improving assembly accuracy quality in future.