Status Quo of Numerical Simulation of Friction Stir Welding After Deformation under Big Data Analysis

Abstract

Friction stir spot welding is a new type of connection technology developed on the basis of the friction stir welding process. It uses a high-speed rotating stirring head to generate heat by friction with the plate to make the welding material reach a thermoplastic softened state, forming a lap joint similar to resistance spot welding. The purpose of this article is to analyze and analyze the current situation of numerical simulation of friction stir welding after deformation under big data analysis. Algorithm mining commonly used for analysis of big data. This paper proposes to use the classic algorithm FP-growth algorithm. The FP-growth algorithm has the widest application range. It compresses the transaction database into a FP tree for processing. It also uses the Apriori algorithm, which eliminates the need to generate candidate frequent itemsets, which improves the efficiency of use. In addition, this article uses data mining technology and data fusion technology commonly used in big data analysis, and their application strengthens the analysis of the current situation of numerical simulation after friction stir welding deformation under big data analysis. Experimental research shows that the dynamic FSSW technology used in this paper simulates the numerical change after friction stir welding deformation, and the simulated data effect is better than the traditional GPC algorithm by about 20%.