A fuzzy finite element model based on the eigenstrain method to evaluate the welding distortion of T-joint fillet welded structures. (May 2022) - Cai, Shupeng; Zhang, Yongkang; Wu, Jianxin; Wu, Fengmin
Abstract: T-joint fillet welded structure is one of the most commonly welded structures in the assembly of vessels, so it is crucial to accurately predict the welding distortion of T-joint fillet as fast as possible for in-process quality controls in ship building industry. Eigenstrain distribution which forms after welding accounts for the appearance of welding distortion, the results obtained from experiment and thermal elastoplastic finite element (FE) analysis indicate that it is closely related with the formation of heat-affected zone (HAZ), however, a quantitative description needs to be determined based on limited experimental data. To deal with this problem, a fuzzy finite element model (fFEM) is proposed to evaluate welding distortion in T-joint fillet welding using the eigenstrain method. A set of coefficients which determines the eigenstrain distribution is introduced as the optimization parameters in fFEM. The optimized coefficients of eigenstrains can be found by introducing the genetic algorithm (GA) and combining it with the commercial FE software ANSYS. The eigenstrain distribution calculated by the optimized coefficients can predict the welding distortion with high accuracy and efficiency, the same optimized coefficients and corresponding eigenstrain distribution can be applied to various dimensions of T-joint welded structures if the welding parameters remain unchanged. Graphical abstract: Unlabelled Image Highlights: A fuzzy finite element model (fFEM) is proposed to evaluate welding distortion in T-joint fillet welding using the eigenstrain method. By combining the genetic algorithm (GA) with ANSYS, the optimized eigenstrain distribution in T-joint fillet welding can be found. The approach can predict the welding distortion of various dimensions of T-joint welded structures with accuracy and efficiency.