This study investigates the comparison of the microstructural and mechanical properties of a novel ternary reinforced AA7075 hybrid metal matrix composite. Four samples, including AA7075 (base alloy), AA7075-5wt %SiC (MMC), AA7075-5wt %SiC-3wt %RHA (s-HMMC), and AA7075-5wt %SiC-3wt %RHA-1wt %CES (n-HMMC) were developed using the stir casting liquid metallurgy route, followed by the heat treatment. The experimental densities corresponded with the theoretical values, confirming the successful fabrication of the samples. A minimum density of 2714 kg/m³ was recorded for the n-HMMC. In addition, the highest porosity of 3.11% was found for n-HMMC. Furthermore, an increase of 24.4% in ultimate tensile strength and 32.8% in hardness of the n-HMMC was recorded compared to the base alloy. However, its ductility and impact strength was compromised with the lower values of 5.98% and 1.5 J, respectively. This was confirmed by microstructural analysis, which reveals that n-HMMC has mixing issues and forms agglomerates in the matrix, which served as the potential sites of stress concentration leading to low impact strength and ductility. Nevertheless, the hybrid composites showed superior mechanical properties over the MMC and its base alloy.

Accurate prediction of the mechanical performance of polymer components fabricated by fused deposition modelling (FDM) remains challenging owing to the complex nonlinear relationships between process parameters and material properties, limiting reliable process planning and broader industrial adoption of polymer additive manufacturing. This study develops and experimentally validates a heterogeneous multi-output stacked ensemble learning framework for the simultaneous prediction of tensile strength, flexural strength, compressive strength, Rockwell hardness, and Charpy impact strength of acrylonitrile styrene acrylate (ASA), a high-performance engineering thermoplastic with excellent weatherability and ultraviolet resistance that remains comparatively underexplored in data-driven FDM research. A Definitive Screening Design (DSD) was employed to investigate eight critical process parameters: extrusion temperature (ET), bed temperature (BT), infill density (ID), layer height (LH), print speed (PS), raster angle (RA), build orientation (BO), and cooling fan speed (CFS). Multiple supervised learning algorithms were systematically benchmarked, and the highest-performing complementary models were integrated into a heterogeneous stacked ensemble for simultaneous multi-output prediction. The proposed framework achieved an overall R² of 0.9943 with an overall RMSE of 0.9758, while the individual prediction models attained R² values ranging from 0.9898 to 0.9967. Beyond improving predictive accuracy, the proposed AI-assisted framework provides a data-driven basis for mechanical-property prediction and establishes a surrogate modelling framework that may subsequently be coupled with dedicated optimization or decision-making methods.

This study investigates the influence of Zn and Sn interlayers on the microstructure and mechanical properties of dissimilar friction stir welds between AA7075-T651 aluminum alloy and AZ31B magnesium alloy. Joints produced without an interlayer exhibited poor material mixing, interfacial cracking, and root defects. The use of a Sn interlayer produced a homogeneous lamellar stir zone with fine, discontinuous Mg ₂ Sn intermetallic layer (thickness <1.2 μm), effectively suppressing continuous brittle Al–Mg intermetallic phases. This resulted in the highest tensile strength (76.4 MPa, ∼29.6% joint efficiency), highest peak hardness (154.4 HV), and improved ductility, with fracture shifting to the stir zone (SZ) / thermomechanically affected zone (TMAZ) interface region on the Mg side. In contrast, the Zn interlayer offered only limited improvement (53.81 MPa) due to incomplete dispersion, residual defects, and localized MgZn ₂ formation. No continuous intermetallic layers were observed in any joints, and fracture surfaces predominantly showed brittle morphology associated with intermixing defects and localized intermetallics. The findings demonstrate that interlayer effectiveness in Al–Mg dissimilar friction stir welding (FSW) is governed primarily by material flow compatibility rather than chemical barrier effects, providing new directions for interlayer selection and design in dissimilar friction stir welding.