Analysis and prediction of the joint strength of friction stir welded Aluminium 5754 to polyamide using response surface methodology and artificial neural network

Abstract

Lightweight hybrid structures are developing these days due to increased demand for fuel economy and lower emissions in the automotive and aerospace industries. This study aims to analyse and optimise the influence of friction stir welding (FSW) process parameters on the tensile shear strength of the aluminium-polyamide hybrid joint. The study on the influence of each parameter on the joint strength helps define the bonding mechanism while joining aluminium-polymer hybrid structures. Optical microscopy and scanning electron microscopy (SEM) were used for microstructural examination. A SEM image of the weld’s cross-sectional area shows micro and macro mechanical interlocks with a small interfacial gap which indicates better joint strength. An elemental area mapping investigation of the weld zone reveals fine polymer and aluminium mixing along the interaction region. In addition, FSW parameters have been optimized to maximize the tensile shear strength of aluminium-polyamide hybrid joints. A mathematical model for tensile shear strength in terms of FSW parameters is developed using response surface methodology (RSM). A predictive model was developed using an Artificial Neural Network (ANN) to validate RSM predicted results. The analysis of variance (ANOVA) shows that the actual and predicted values have a satisfactory correlation. ANN methods are better than regression models in predicting tensile shear strength within input welding parameter ranges. The process variables were optimised using the desirability function analysis. The maximum joint tensile shear strength of about 19.74 MPa and attained at optimal FSW parameters, i.e. rotational tool speed of 1421 r/min, welding speed of 27 mm/min, and tool tilt angle of 1°. The regression coefficient for the ANN model was 0.988 for the test data set, indicating that the developed model is appropriate for predicting tensile shear strength.