Utilizing hybrid BIGRU-LIGHTGBM model for mechanical characteristics prediction in aluminium alloys

Abstract

The growing requirement for lightweight yet high-performance materials has resulted in a major progress in aluminium (Al) alloys, especially in optimizing their mechanical properties. Conventional predictive models struggle to capture the intricate relationships among alloy composition, processing conditions, and mechanical properties. To address this challenge, this research proposes a new hybrid model combining a Bidirectional Gated Recurrent Unit (BiGRU) and a Light Gradient Boosting Machine (LightGBM), optimized using AdamW-based Bayesian Optimization (AdamW-BO). The model is trained and tested on experimental data sets, accurately predicting key mechanical properties such as yield strength (YS), tensile strength (TS), and elongation. The proposed BiGRU-LightGBM model with and without AdamW-BO achieves superior performance, with an R² value of 0.979 and 0.96, respectively and less errors, demonstrating its efficacy in accurately forecasting mechanical characteristics. The findings suggest that the hybrid approach can serve as a robust predictive tool for material design and quality control in Al alloy manufacturing.