ABSTRACT
Since the mid-19th century, the study of fatigue in metals has become a major field of interest for materials scientists and structural engineers because fatigue represents one of the principal failure mechanisms in metallic engineering structures. Initially based on empirical approaches such as the stress amplitude–life (S–N) curves derived from Wöhler’s pioneering studies, fatigue prediction capabilities have evolved significantly, leading to sophisticated numerical models capable of predicting not only fatigue life but also the progressive degradation of material properties associated with the fatigue phenomenon, including crack initiation and propagation up to the complete failure of the component.Despite these advances, the systematic incorporation of manufacturing process effects —such as machining, forming, trimming, and punching— into fatigue formulations remains limited. Most existing approaches still rely on simple modifications of S–N curves through reduction factors, restricting their applicability mainly to coupon-level specimens and preventing the accurate prediction of crack initiation and propagation patterns.In this context, the present thesis proposes a constitutive framework to incorporate manufacturing-induced effects into the fatigue response of metals in the high-cycle regime. The model is formulated in a general manner, allowing different manufacturing processes to be considered through suitable specializations based on relevant process-related data, such as residual stresses, surface roughness, porosity, and defect volume fraction.To develop this framework, several fatigue modeling approaches are reviewed, with particular emphasis on fracture mechanics, continuum damage mechanics, and phase-field formulations, commonly implemented using numerical methods such as the finite element method and the extended finite element method. Among them, a continuum damage mechanics-based high-cycle fatigue model is selected as the baseline framework due to its high computational efficiency and its ability to model complex stress–strain states throughout the material degradation process, from the intact condition to complete failure.The constitutive framework is first enhanced through the introduction of a novel hardening–softening stress–strain curve for damage. In addition, a methodology to convert average-based S–N data obtained from standardized fatigue tests into local material-point information is proposed to ensure a consistent calibration of the fatigue parameters with the local nature of the formulation. The enhanced model is validated against experimental data from stiffness-based rapid fatigue tests and durability tests performed on real automotive components, demonstrating strong predictive capabilities at both coupon and component levels while maintaining high computational efficiency.Finally, the framework is extended to explicitly account for manufacturing-induced effects. A residual stress relaxation model and a formulation to account for notch effects associated with manufacturing operations are introduced. The resulting model is specialized for shear-cutting operations, such as trimming and punching, using residual stresses obtained from process simulations and measured surface roughness as process-related inputs. The formulation is validated against uniaxial fatigue tests performed on trimmed and punched complex-phase steel specimens, demonstrating its ability to accurately predict fatigue life and crack initiation locations.
PhD Advisors:
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- Dr. Lucía G. Barbu
- Prof. Sergio H. Oller
CANDIDATE
Luis Antônio Gonçalves Junior is a structural engineer specialising in computational mechanics and advanced fatigue modelling. At CIMNE’s CAMMS Group (Part of the Aeronautical, Marine, Automotive and Energy Engineering Research Cluster) he contributed to the development of high‑fidelity numerical frameworks for fatigue and damage tolerance, applying finite element and fracture‑based methods to lightweight multifunctional structures. His work spans automotive and aerospace applications, with a strong focus on component‑level virtual testing and material‑driven design optimisation.