Analysis of Tensile Damage of Titanium Alloy in Seawater Environment Based on Deep Learning

With the continuous expansion of the global ocean economy, the demand for marine engineering is increasing. However, the presence of chemicals such as chloride ions in the marine environment poses a significant corrosion risk to metal materials, thereby compromising the safety and reliability of marine engineering projects. This study focused on investigating the corrosion treatment and mechanical performance of titanium alloy materials. Simulated seawater corrosion experiments and integrated acoustic emission systems were utilized to gather mechanical performance and acoustic emission data from 48 samples after corrosion treatment. The study applied consistency treatment to the acoustic emission-remaining useful life (AE-RUL) data and established a correlation between acoustic emission signals and the remaining life of the titanium alloy during the yield stage. Principal component analysis was employed to reduce the initial 11-dimensional data to 5 dimensions, improving the quality of the dataset. The Long Short Term Memory (LSTM) network was enhanced using interpolation optimization methods and multi-time-series batch input techniques. The predictions and reliability analysis for the tensile yield life of titanium alloy achieved an accuracy rate of 94.102%, which was further improved to 95.782% with the use of spline interpolation methods. Reliability analysis demonstrated a good fit with a normal distribution through parameter estimation and hypothesis testing within a 95% confidence interval.