AI Enhanced Design and Synthesis of Al7075/TiC/Gr Hybrid Composites: Predicting Mechanical Performance

DOI: https://doie.org/10.10399/JBSE.2026791488

V. Jayaprakash, N. KishoreNath, A. Krishnaiah

Keywords:

Al7075, AI tools, Optimization, RSM, Mechanical properties.

Abstract:

Modern aerospace structures require the development of lightweight materials with exceptional mechanical performance. In this work, two-step stir casting was used to create hybrid aluminum metal matrix composites based on Al7075 reinforced with titanium carbide (TiC) and graphite (Gr). To examine their impact on the mechanical behaviour of the composites, the reinforcing content was changed between 0 to 5 wt. % TiC and 0 to 5 wt. % Gr. To assess the impact of hybrid reinforcement on microstructural evolution and mechanical properties, optical microscopy, Vickers micro hardness testing, and tensile testing were used to characterize the produced samples. The results showed that while graphite functioned as a solid lubricant improving interfacial behaviour and lowering frictional stresses, the addition of TiC greatly increased hardness and strength due to grain refinement and load-bearing effects.With a maximum hardness of 88 HV and increased tensile strength above the unreinforced alloy, the Al7075/3TiC/3Gr wt. % hybrid composite demonstrated the greatest overall performance among the compositions under investigation. Response surface methodology (RSM), analysis of variance (ANOVA), and machine learning models were used for predictive modelling and optimization in order to better comprehend the connection between processing parameters and mechanical responses. High prediction accuracy (R2>0.94) between experimental and anticipated results was shown by the generated models. The improved composite's homogeneous dispersion of reinforcing particles and notable grain refinement were verified by microstructural investigation. The results show that an efficient method for creating highperformance Al7075 composites appropriate for lightweight aerospace applications is to combine hybrid reinforcement with AIbased predictive modelling.

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