Artificial neural network prediction and grey relational grade optimisation of friction stir processing

Abstract

AbstractThe study predicts friction stir process (FSP) parameters using an artificial neural network (ANN) model. A design of experiment (DoE) approach is used to conduct experiments on FSP and to evaluate the best operating parameters of FSP. ANN uses 30 neurons, 1 hidden layer, and an input and output layer in the Matlab® environment. ANN predicts the output parameters with R values of 0.999, 0.995, and 0.992 for training, validation, and test datasets, respectively, while the overall R-value is 0.997. GRA is used to optimize and rank the parameters of the processes and revealed that the rotational tool speed should be 1180 rpm, traverse feed rate 38 mm/min, and tool tilt angle 1° for best results. The optimized values obtained are 380 MPa, 3.8 µm, 138 HV, and 14% for tensile strength, grain size, microhardness, and elongation, respectively. According to the analysis of variance (ANOVA) and grey relational analysis, the order of influencing parameters on output factors was rotational speed (80%), followed by transverse feed (19%) and tool tilt angle (1%). An ANN is further used to predict using the two most significant parameters—rotational speed and traverse feed. The modified ANN has nearly the same R values as the original ANN. Thus modified ANN may be used for prediction.

Keywords

• ANN
• grey relational analysis
• 7075-T6 aluminum alloy
• ANOVA
• Friction stir processing