In the current study, a novel approach that integrated in situ and ex situ reinforcement techniques was used to develop a hybrid AA7475 aluminium metal matrix composite (MMC). The primary objective of this study was to explore the viability of producing in situ TiB2 particles and adding ex situ B4C particles as reinforcements in the same melt of the aluminium alloy AA7475, with the intention of using these ceramic fillers to improve the mechanical properties of bare AA7475. For in situ particle synthesis, salts such as K2TiF6 and KBF4 were used and for ex situ, B4C particles are utilized. The aluminium MMC was prepared using stir casting method and characterized using ASTM standards. For comparison, composite with only in situ TiB2 particles and composite with only ex situ B4C particles were also fabricated. According to the results, the highest tensile strength of 156.57 MPa was achieved for the hybrid composite made using 5wt% of B4C and TiB2. Similarly, the highest hardness of 144.67 HV was observed for the same. However, the presence of both TiB2 and B4C improved the hardness together. The microstructure shows highly refined phases of the matrix and also the formation of precipitates and reinforcements clearly. The EDAX and XRD confirm the intermetallics formation and dispersion of particles within the matrix material. This aluminium composite with improved properties could be used in automotive, aircraft and in defense sectors where lightweight and high strength materials are required.

Purpose This study aims to predict the mechanical properties such as equivalent tensile strength and micro-hardness of friction-stir-welded dissimilar aluminium alloy plates AA 6063-O and AA 2014-T6, using artificial neural network (ANN). Design/methodology/approach The ANN model used for the experiment was developed through back propagation algorithm. The input parameter of the model consisted of tool rotational speed and weld-traverse speed whereas the output of the model consisted of mechanical properties (tensile strength and hardness) of the joint formed by friction-stir welding (FSW) process. The ANN was trained for 60% of the experimental data. In addition, the impact of the process parameters (tool rotational speed and weld-traverse speed) on the mechanical properties of the joint was determined by Taguchi Grey relational analysis. Findings Subsequently, testing and validation of the ANN were done using experimental data, which were not used for training the network. From the experiment, it was inferred that the outcomes of the ANN are in good agreement with the experimental data. The result of the analyses showed that the tool rotational speed has more impact than the weld-traverse speed. Originality/value The developed neural network can be used to predict the mechanical properties of the weld. Results indicate that the network prediction is similar to the experiment results. Overall regression value computed for training, validation and testing is greater than 0.9900 for both tensile strength and microhardness. In addition, the percentage error between experimental and predicted values was found to be minimal for the mechanical properties of the weldments. Therefore, it can be concluded that ANN is a potential tool for predicting the mechanical properties of the weld formed by FSW process. Similarly, the results of Taguchi Grey relational analysis can be used to optimize the process parameters of the weld process and it can be applied extensively to ascertain the most prominent factor. The results of which indicates that rotational speed of 1,270 rpm and traverse speed of 30 mm/min are to be the optimized process parameters. The result also shows that tool rotational speed has more impact on the mechanical properties of the weld than that of traverse speed.