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Indexed by:期刊论文

Date of Publication:2024-06-05

Journal:Tensile strength prediction and process parameters optimization of FSW thick AA2219-T8 based on ANN-GA

Volume:46

Issue:7

ISSN No.:1678-5878

Key Words:AA2219 thick plate; ANN-GA; FSW; Optimization; Tensile strength prediction

Abstract:This paper presents the modeling of tensile properties of friction stir welding (FSW) AA2219-T8 aluminum alloy thick plate for launch vehicle tanks using the artificial neural network (ANN) and genetic algorithm (GA), specifically developed for this work. FSW experiments of 18 mm thick AA2219-T8 plate and subsequent tensile tests were conducted. A single-layer feedforward neural network model was developed based on rotational speed and welding speed as inputs and the joint tensile strength as output, with a network topology of 2-5-1. Correlation performance of considered ANN model with network topology expressed in terms of mean absolute percent error was found to be 0.52%, with a correlation coefficient value of 0.96101. The objective function from ANN was taken by GA to determine the combination of process parameters that yields an optimal tensile strength. The feasible optimal process parameter of a combined ANN-GA was identified as a rotational speed of 312 r/min and welding speed of 132 mm/min with an average joint efficiency of 83.17%. Effective validation experiments were conducted, and the experimental results agreed with the ANN-GA optimization results with an acceptable error of only 1.13%. The weld, subjected to the confirmation test, was investigated by means of microstructure analysis and fracture analysis. Evaluation of the developed model proved to be efficient enough for the development of FSW thick AA2219-T8 with required tensile strength.