*Advances in Fatigue Prediction Techniques DOI: http://dx.doi.org/10.5772/intechopen.99361*

of analyzing fatigue behavior of structures and weld sections can be understood by the fact that several national and international organizations such as the American Society of Mechanical Engineers (ASME), the International Institute of Welding (IIW), and the Society of Automotive Engineers (SAE), etc. are working round the clock to establish standard procedures and guidelines for determining the fatigue properties of weld joints. Various design guidelines and multiple updates have been proposed by the IIW on "recommendations for the fatigue design of welded components and structures". Various standard procedures are available for determining the fatigue properties of weld sections as per the assessment criterion and requirement of stress –strain data (**Figure 2**).

Previously, several models such as stress and strain-based models [21, 22], Critical plane models [23, 24], Enclosed surface models [25], and Integral type models [26], etc. have been used to predict fatigue properties of structural

**Figure 2.** *Variables for fatigue life prediction.*

components, however, could not estimate the fatigue properties accurately. The fatigue life predictions based on the application of empirical models (Basquin law, Miner's rule, Goodman diagram) taking into account the macroscopic mechanical fields are structure-oriented methods and therefore material-specific; thus, they do not account for the stochastic fatigue behavior due to the microstructural variabilities. Recently a methodology to predict the fatigue property of metal structures is gaining increasing acceptance and is known as CPFEM. The CPFEM can provide insights, and eventually, predictions of the fatigue behavior and variability of metallic materials become easier. CPFEM is also helpful to accelerate the development of new alloys with improved fatigue performances. While using the CPFEM, the advantage is that the fatigue predictions are much closer to the actual failure phenomenon and thus realistic. On the other hand, they require significant computational time and material intrinsic parameters that are difficult to measure experimentally. The accuracy of such simulations is strongly dependent on the synthetic microstructures generated through mathematical scripts or EBSD data. The CPFEM can provides insights and eventually predictions of the fatigue behavior and variability of metallic materials becomes easier. CPFEM is also helpful to accelerate the development of new alloys with improved fatigue performances.
