Characterization of Cr-TiB2 composite coating on AISI 1045 steel using hybrid deep neural network-based sandpiper optimization algorithm

Abstract

The scope of this study is to enhance the performance of AISI 1045 steel for demanding marine applications like connecting rods, axles, and wear-resistant components. Traditional heat treatment methods, including boriding, surface induction hardening, and tempering, improve the steel’s surface hardness. Additionally, the Tungsten Inert Gas (TIG) coating process develops a novel Cr- TiB2 composite coating on the heat-treated AISI 1045 stainless steel. The investigation explores the influence of varying TiB2 content (10%, 20%, and 30%) on the mechanical properties of AISI 1045 steel. Microhardness and tensile characteristics are analyzed, considering the impact of TiB2 percentage. Surface morphology and interfacial bonding are examined using Scanning Electron Microscope (SEM) and Energy Dispersive X-Ray Analysis (EDAX). Results reveal notable improvements, including a 2.65 times increase in microhardness compared to untreated AISI 1045 steel, uniform TiB2 distribution, and a crack-free coating. The addition of TiB2 particles in the Cr- TiB2 composite coating enhances microhardness. Improved mechanical properties are attributed to surface treatments, incorporation of hard and wear-resistant materials (Cr and TiB2) in the composite coating, microstructure modifications, and crack prevention, collectively resulting in increased hardness, wear resistance, and durability. Furthermore, this study introduces a novel hybrid Deep Neural Network-based Sandpiper Optimization Algorithm (DNN-SOA) for predicting experimental outcomes with superior accuracy. This research not only enhances the understanding of the coated steel’s mechanical properties but also presents an innovative predictive model for future material engineering applications, demonstrating the significance of advanced heat treatment and a novel composite coating in improving AISI 1045 steel performance.