Sensitivity-driven deep learning model for tribological prediction in Al7075/B4C nanocomposites

Abstract The development of lightweight, durable composites for industrial use is constrained by traditional tribological evaluation methods that are costly, time-consuming, and inadequate for capturing nonlinear interactions between material and operational parameters. This study proposes an integrated framework combining Global Sensitivity Analysis (GSA) and Machine Learning (ML) to predict the coefficient of friction (COF) and wear rate in Al7075/B4C nanocomposites. Four GSA techniques - Sobol indices, delta index, PAWN index, and mutual information - were employed to rank the significance of input parameters, including applied load, B4C reinforcement percentage, time, sliding velocity, and sliding distance. Using 10,800 experimental records from pin-on-disc tests, a Deep Residual Regression Network (DRRN) was developed to model tribological behavior. The Al7075 matrix was reinforced with boron carbide (B4C) particles at weight fractions of 0%, 4%, 8%, and 12%. Results show that B4C reinforcement significantly enhances wear resistance, with the 12% B4C composite reducing wear rate by 77% under severe conditions. The proposed framework achieved high predictive accuracy (R² = 0.93 for COF, 0.99 for wear rate).