Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: multiaxial fatigue
Description
Two fatigue life prediction methods using the energy-based approach have been proposed. A number of approaches have been developed in the past five decades. This study reviews some common models and discusses the model that is most suitable for each different condition, no matter whether the model is designed to solve uniaxial, multiaxial, or biaxial loading paths in fatigue prediction. In addition, different loading cases such as various loading and constant loading are also discussed. These models are suitable for one or two conditions in fatigue prediction. While most of the existing models can only solve single cases, the proposed new energy-based approach not only can deal with different loading paths but is applicable for various loading cases. The first energy-based model using the linear cumulative rule is developed to calculate random loading cases. The method is developed by combining Miner’s rule and the rainflow-counting algorithm. For the second energy-based method, I propose an alternative method and develop an approach to avert the rainflow-counting algorithm. Specifically, I propose to use an energy-based model by directly using the time integration concept. In this study, first, the equivalent energy concept that can transform three-dimensional loading into an equivalent loading will be discussed. Second, the new damage propagation method modified by fatigue crack growth will be introduced to deal with cycle-based fatigue prediction. Third, the time-based concept will be implemented to determine fatigue damage under every cycle in the random loading case. The formulation will also be explained in detail. Through this new model, the fatigue life can be calculated properly in different loading cases. In addition, the proposed model is verified with experimental datasets from several published studies. The data include both uniaxial and multiaxial loading paths under constant loading and random loading cases. Finally, the discussion and conclusion based on the results, are included. Additional loading cases such as the spectrum including both elastic and plastic regions will be explored in future research.
ContributorsTien, Shih-Chuan (Author) / Liu, Yongming (Thesis advisor) / Nian, Qiong (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Extensive efforts have been devoted to understanding material failure in the last several decades. A suitable numerical method and specific failure criteria are required for failure simulation. The finite element method (FEM) is the most widely used approach for material mechanical modelling. Since FEM is based on partial differential equations, it is hard to solve problems involving spatial discontinuities, such as fracture and material interface. Due to their intrinsic characteristics of integro-differential governing equations, discontinuous approaches are more suitable for problems involving spatial discontinuities, such as lattice spring method, discrete element method, and peridynamics. A recently proposed lattice particle method is shown to have no restriction of Poisson’s ratio, which is very common in discontinuous methods. In this study, the lattice particle method is adopted to study failure problems. In addition of numerical method, failure criterion is essential for failure simulations. In this study, multiaxial fatigue failure is investigated and then applied to the adopted method. Another critical issue of failure simulation is that the simulation process is time-consuming. To reduce computational cost, the lattice particle method can be partly replaced by neural network model.First, the development of a nonlocal maximum distortion energy criterion in the framework of a Lattice Particle Model (LPM) is presented for modeling of elastoplastic materials. The basic idea is to decompose the energy of a discrete material point into dilatational and distortional components, and plastic yielding of bonds associated with this material point is assumed to occur only when the distortional component reaches a critical value. Then, two multiaxial fatigue models are proposed for random loading and biaxial tension-tension loading, respectively. Following this, fatigue cracking in homogeneous and composite materials is studied using the lattice particle method and the proposed multiaxial fatigue model. Bi-phase material fatigue crack simulation is performed. Next, an integration of an efficient deep learning model and the lattice particle method is presented to predict fracture pattern for arbitrary microstructure and loading conditions. With this integration, computational accuracy and efficiency are both considered. Finally, some conclusion and discussion based on this study are drawn.
ContributorsWei, Haoyang (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Jiang, Hanqing (Committee member) / Jiao, Yang (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2021