Optimization and prediction of the hardness behaviour of LM4 + Si3N4 composites using RSM and ANN: A comparative study

Abstract

In the present work, LM4 + Si3N4 (1, 2, and 3 wt.%) composites were fabricated using the two-stage stir casting method. Precipitation hardening treatment was carried out on the cast composites and hardness results were compared with as-cast specimens. Microstructural analysis was performed using Scanning Electron Microscope (SEM) images to validate the existence and homogenous distribution of reinforcement in the matrix. LM4 + 3 wt.% Si3N4 composite with multistage solution heat treatment (MSHT) and aging at 100°C showed higher hardness viz., 124% improvement when compared to as-cast LM4 due to the uniform distribution of Si3N4 and precipitation of metastable phases during the heat treatment process. The microhardness values of the fabricated composites was investigated using Artificial Neural Network (ANN) and Response Surface Methodology (RSM). Both RSM and ANN models predicted hardness values close to experimental values with minimum error, and the prominence of aging temperature in the improvement of hardness was observed. The data obtained illustrate that the proposed regression model can accurately predict hardness values within the constraints of the factors under consideration. Based on the error values it can be concluded that the ANN model can deliver results with higher accuracy than the RSM model.