Enhanced fatigue SN curve generation for gear bending fatigue life prediction using multi-objective optimization algorithms

Abstract

This study presents a comprehensive methodology for fatigue life prediction and SN curve generation in gear systems, integrating specimen and product-level testing, simulations, and multi-objective optimization. The approach addresses the discrepancy between specimen-based testing and actual product performance through a novel multi-fidelity framework. To solve the proposed multi-objective optimization problem, several optimization algorithms were compared and analyzed, demonstrating the effectiveness of each approach in balancing different objectives. A key innovation is the development of an empirical formulation-based surrogate model, enabling efficient optimization while significantly reducing computational demands. The methodology covers both specimen and product-level testing along with simulations, offering a holistic solution for accurate fatigue life prediction. Extensive validation through experiments and comparison with commercial software confirms the reliability and practical applicability of the method. This approach provides a sustainable and efficient solution for SN curve generation and fatigue life prediction in automotive gear train development, allowing for continuous improvement as new data becomes available. Its practicality and accuracy make it particularly valuable for early-stage design optimization and large-scale applications in the automotive industry.