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In this investigations, sliding wear performance of Al-25Zn based novel hybrid composites added with fixed weight percentage of graphite (3 wt.%) and varying weight percentage of silicon carbide (10, 20 and 30 wt.%) was investigated for various process factors such as specimen temperature, applied load, sliding speed and sliding distance using a pin on disc with EN24 disc as per Taguchi L16 array. For similar test conditions, the composite with 10 wt.% of silicon carbide shows the highest wear resistance and tensile strength; whereas the composite with 20 wt.% of SiC shows highest hardness. The specimen temperature is recognized as the dominating parameter for the sliding wear performance of the materials. Artificial Neural network and Regression model developed was found competent for the forecasting of wear performance. Confirmation experiment conducted with the optimum parameter combination also confirmed the accuracy of developed model. The observed wear mechanism is abrasion and adhesion. The major mechanisms of abrasive wear are recognized as ploughing, micro cutting and delamination.

This investigation studied the effect of Al2O3 reinforcement on sliding wear (dry) characteristics of Al–25Zn/Al2O3 composites at various temperatures, speeds, and loads applied for 1400 m sliding distances as per the Taguchi L16 orthogonal array using a tribometer with an Emergency Number24 shaft steel disk. Al–25Zn alloy-based composites reinforced with 10, 15, and 20 wt% of Al2O3 particles were fabricated by the stir casting technique. The results show a significant alteration in mechanical and tribological properties with reinforcement of Al2O3 content. The optimum mechanical and sliding wear properties are seen for the composite with 10 wt% of Al2O3. The material removal in the matrix alloy is due to adhesion and in composites mainly due to abrasion and delamination. The developed regression model and the artificial neural network model can forecast the composite's wear behavior with good precision.

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