No abstract

No abstract

This paper proposes an approach to predicting fatigue crack growth under mixed-mode loading based on improved Gaussian process. In terms of analyzing the theoretical background for fatigue crack growth, a corresponding finite element model is built to generate sufficient simulation data, which is utilized to obtain the key parameters (e.g., stress intensity factor) for the fatigue crack growth process. And then, a Gaussian process model is achieved to meet the condition that the stress intensity factor is a nonlinear continuous change in crack growth, especially for mixed-mode loading. Following, an idea of local sample densification method is implemented to improve Gaussian sample generation process according to the simplified model of the crack growth path. Based on the above investigation, a fatigue crack growth prediction model using the improved Gaussian process is finished, which is subsequently verified through the test data of lower bainite steel (SCM435) material. The results show that the proposed approach has better computational accuracy and efficiency than the traditional finite element method in predicting fatigue crack growth under mixed-mode loading.INDEX TERMS Fatigue crack growth, mixed-mode, improved Gaussian process, sample space, local sample densification.

No abstract

Well-dispersed α-Fe2O3 nanocrystalline was prepared by a convenient self-propagation combustion method using low-toxic glycine as fuel, low-cost Fe (NO3)2·9H2O as raw materials. The XRD results indicate that the glycine doses have an important effect on the control of the average size and the order of the crystalline phase. The UV-vis results indicate that the α-Fe2O3 nanocrystals prepared by this method have a broad absorption peak whose center is at about 221 nm. TEM images revealed that the products were composed of well-dispersed particles with an average size about 35 nm.