Optimisation of friction-stir welding process using vibro-acoustic signal analysis

Abstract

This paper presents a mathematical model to predict the tensile strength (TS) of friction-stir welded (FSW) AA1050 aluminium alloy joint. This model has been obtained from the optimisation of the parameters of models developed from vibro-acoustic signals produced during the process. The multiple response method was used to obtain the optimisation model. During experimental development, the process parameters selected to be assessed were rotation speed (RS), travel speed, and tool profile (TP). The TS of the welded joint and the root mean square (RMS) value of the vibro-acoustic signal in the frequency range of 3?2-6?4 kHz were analysed as response variables. In order to validate the model, the optimal values of the factors and responses are presented, showing their agreement with the experimental values obtained. This model can be used to develop and optimise the parameters of the automatic control of the FSW process, based on the vibro-acoustic signal generated.

Tribological Behavior of AA1050H24-Graphene Nanocomposite Obtained by Friction Stir Processing

“…Therefore, apart from the expected disorder induced by the friction stir process, our Raman spectra reveal a successful GNS/AA1050 mixture. Similar Raman results were reported in References [17,30].…”

“…disorder induced by the friction stir process, our Raman spectra reveal a successful GNS/AA1050 mixture. Similar Raman results were reported in References [17,30]. It was observed that the tool rotation speed was the most influential factor in the variation of the morphology and dispersion of the GNS within the metallic matrix.…”

Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN-Based Model

“…The evaluation of the ANN using the test data shows that a practically lineal relationship exists between the FSW parameters and the parameters calculated with the ANN by (1). Figure 8(c) shows the comparative analysis between the tensile strength obtained from the ANN-based model and the one determined using the mathematical model developed in previous works [23]. In this case, the value of the adjusted coefficient of determination 2 was 91.4% for mathematical model, indicating that less than 9% of the total variations are not explained by the model [22].…”