AI-Driven Simulations of the Car Components Fatigue Life, for Automotive Predictive Maintenance Applications

This paper presents a new Integrated Framework for AI predictive Maintenance (AMIFAIM—Andrei Muresan Integrated Framework for AI predictive Maintenance), which allows online monitoring of the main components’ fatigue life. A Random Forest regressor, trained on 15,000 synthetic loading scenarios generated by physics-based superposition of seven finite element load cases, serves as a near-real-time surrogate for structural simulation. The framework is demonstrated on a lower control arm made from S420MC steel (EN 10149-2) in a Dacia Logan front suspension. The trained model predicts Von Mises stress from six strain-gauge channels with R2 = 0.9943, MAE = 28.76 MPa, and RMSE = 37.58 MPa on unseen test data, while providing uncertainty estimates per prediction from the variance in the ensemble. Leave-One-Load-Case-Out validation confirms generalisation to unseen load combinations (mean R2 = 0.950). Cumulative fatigue damage is tracked in real time using the Palmgren–Miner rule with an S-N curve calibrated for hot-spot stress conditions. Simulated drive scenarios confirm that the system correctly distinguishes between benign service (near-zero fatigue damage under highway loading) and abusive conditions (5–8% life consumption in 30 s under repeated impact loading), consistent with the expected behaviour of a properly designed suspension link. The entire pipeline—data augmentation, training, prediction, fatigue tracking, and visualisation—is driven by a single configuration file. Applying it to a new component requires only finite element results and material properties; no software changes are needed. The framework is designed to be reconfigured for components for which representative FEA load cases and material-fatigue inputs are available. The current implementation operates on FEA-derived virtual sensor data, with the architecture designed for direct replacement by physical strain-gauge hardware in future experimental validation.