Fatigue Failure Prediction in Mechanical Components
Fatigue failure is considered to be one of the most critical forms of failure in mechanically loaded associated components that are characterized by cyclic loading. It takes almost 80-90 percent of the failures of engineering structures and machine elements. Fatigue life estimation is a tricky and tough task due to the unpredictability of the fatigue crack initiation and propagation. In this paper, a detailed exploration of fatigue failure prediction of mechanical components has been developed in terms of experimental, analytical and computational as well. A review and a comparison of traditional stress-life (S ⁸ N), strain-life (ε N), as well as fracture mechanisms-based methods, are discussed, and contrasted with the recent data-driven and machine learning-based methods. The paper focuses on combination of finite element analysis (FEA), material characterization and probabilistic modeling in terms of better fatigue life prediction. It suggests a methodology based on a systematic approach to the problem with the analysis of stress, the model of damage accumulation, and verification with experimental results. To prove the efficiency of the proposed method, simulated case studies of common mechanical elements like shaft, gears, and welded contacts are provided. Findings show that cross-validation models that incorporate physics-based and data-driven methods are less inaccurate and reliable. The paper will be used as a framework of reference by the researcher and practicing engineers involved in fatigue study and structural durability evaluation.