Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites

Abstract Aluminium casting alloy LM4 (EN 1706 AC-45200) composites with TiB2 (1, 2, and 3 wt.%) as reinforcements were produced using the two-stage stir casting method. OM and SEM study shows uniform and homogeneous reinforcement distribution in LM4 + TiB2 composites. As-cast composites were subjected to single-stage solution treatment at 520°C for 2 h and multistage solution treatment at 495 and 520°C for 2 and 4 h, followed by hot water quenching at 60°C and aging at 100 and 200°C for different time intervals. The hardness of as-cast and artificially aged composites were compared in both conditions. Compared to as-cast LM4 alloy, 20-45% improvement in hardness was observed for LM4 + TiB2 as-cast composites. 60-150% improvement in hardness was observed in artificially aged LM4 + 3 wt.% TiB2 composites when aged at 100 and 200°C during peak aged conditions. TEM images confirmed the presence of primary strengthening solute-rich phases after age hardening treatment such as θ’-Al2Cu and θ”-Al3Cu, which are responsible for hardness increment. An artificial neural network (ANN) model was created to predict the hardness trend of these composite samples using MATLAB R2021b, and results proved that the ANN model developed can be utilized as an effective tool to predict the hardness of treated composite samples.