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Al7075/10 wt% TiO 2 alloy composite has been fabricated by the stir casting method to investigate the influence of wear parameters. The microstructure of the produced composite was analyzed by scanning electronic microscopy. A pin-on-disc rig was used to carry out the wear test on produced composite under dry sliding conditions. The three wear control parameters such as load, sliding velocity and sliding distance were chosen to study the effects on the wear rate and coefficient of friction of composite. The experiments were planned as per L9 orthogonal array based on Taguchi's design of experiments. The signal-to-noise ratio and analysis of variance techniques were employed to determine the optimum combination of parameters and the significant contribution of each parameter on the responses. The results exposed that the load was the most predominant factor affecting the wear rate and coefficient of friction followed by the sliding distance and sliding velocity. Finally, the regression equations were developed to predict the responses. The worn surface morphology of composite noticed that TiO 2 reinforcement protects the matrix alloy from removal of material at all conditions. This investigation revealed that the wear resistance of the Al7075 alloy can be enhanced by the incorporation of TiO 2 particles.

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The Friction stir processing benefits of aluminium composites contain advanced exploration in the region of aluminium alloy Friction Stir Welding - FSW. The modern advancements in Friction Stir Welding are concentrated on the optimization of welding parameters for multi response attributes. The investigations were carried out with the tool pin profiles, tool rotational speed and traverse speed as predictable process parameters for multi response optimization in Friction Stir Welding of 5052 aluminium alloy. GRG (grey relational grade) was obtained by the grey relational analysis of the friction stir welding process through different qualities, particularly, UTS-ultimate tensile strength and micro hardness. The significant process variables on GRG and most substantial parameters traverse speed and tool pin profiles are examined by ANOVA. Excluding tool rotational speed, tool pin profiles and traverse speed were likewise observed to be significant. To approve the investigation, verification of tests was completed at optimal parameters arrangement and predicted outcomes were observed to be in great concurrence with test values.

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This work aims to apply data-driven approach [decision tree (DT) algorithm] to analyse the wear rate (WR) of ZnO-filled AA7075 composites. The results of model-based analysis was compared with Taguchi analysis. Stir casting was used to produce the composite samples. Characterization studies were conducted to analyse the composition and morphology. The scanning electron microscopy results indicated the even dispersion of ZnO in the AA7075. The energy-dispersive x-ray spectroscopy pattern ensures the presence of matrix elements and the inclusion of reinforcement particles into the proposed composites. To minimize the number of experimentation, L27 Orthogonal array is used for finding WR. The 'DuCom' Pin-on-Disc apparatus were used to prepare WR data for the set of the proposed composites. Taguchi technique reveals the optimum level factors for obtaining the minimum 'WR' is reinforcement content of 10 wt.%, applied load (P) at 10 N, sliding velocity (V) at 1 m s −1 and sliding distance (D) of 1000 m. The experiments results from DT algorithm, and analysis of variance and signal-to-noise ratio analysis from Taguchi-based approach confirmed that reinforcement is the primary element for affecting wear of the composites. The reason for applying DT algorithm is that, the low-level knowledge could be converted into high-level knowledge (If-then-else rules), which can be effortlessly explicable by semiskilled personnel.