Evaluating Microstructural Characteristics of Aluminum–Silicon Carbide Nanomixtures: Application of Spatio-Temporal Graph Convolutional Neural Networks for Enhanced Analysis

Nanomixes of aluminum and silicon carbide (Al–SiC) are sophisticated composite materials with better mechanical qualities, such as increased stiffness and strength. The microstructural properties of an aluminum matrix, such as grain size, phase distribution, and interfacial bonding, can be greatly influenced by the addition of silicon carbide nanoparticles. Achieving homogeneous dispersion of nanoparticles and preserving robust interface bonding while striking a balance between mechanical strength and ductility present challenges. This paper proposes a hybrid approach for evaluating the microstructural characteristics of Aluminum–Silicon Carbide Nanomixtures. The proposed hybrid method combines a Spatio-Temporal Graph Convolutional Neural Network (STGCNN) and Ali Baba and Forty Thieves Optimization (AFTO) and is usually referred to as the AFTO-STGCNN method. The main objective of the proposed method indicates the smallest aluminum matrix crystallite size and the maximum lattice strain can be achieved by modifying the process parameters. The AFTO approach is utilized to optimize the process parameters producing the minimum crystallite size and the maximum lattice strain of the Al matrix. The STGCNN technique predicts the characteristics of the Al/SiC nanocomposite. The proposed AFTO-STGCNN technique runs in the MATLAB platform and is compared to perform with the various existing methods. By this, the proposed technique achieves an error of 0.1%. But, the existing techniques, like Artificial Neural Network (ANN), Growth Optimizer Algorithm (GOA), and Multi-objective Grasshopper Optimization Algorithm (MOGOA) attain the error of 0.3%, 0.2%, and 0.6%, respectively. Finally, this work demonstrates that the proposed strategy for minimizing error and enhancing performance in evaluating the microstructural properties of aluminum–silicon carbide nanomixtures is feasible.