Research on Fatigue Curves for Pre-corroded Aircraft Structures

To quantitatively evaluate the effects of corrosion during grounding onfatigue life of aircraft structures, a new power equation is proposed using two-variable linearregression method. That the slope is a constant and the logarithmic intercept is a linear functionof pre-corrosion time makes this equation advantageous: it has a simple form, its parameters haveunambiguous technical and geometrical meanings, and it facilitates engineering applications.Three-parameter equations after pre-corrosion are obtained from back-calculation of fatigue limits,which have been successfully used to predict safe life of aircraft structures in corrosiveenvironment.

Vibration Fatigue Probabilistic Life Prediction Model and Method for Blade

Vibration fatigue is one of the main failure modes of blade.The vibration fatigue life of blade is scattered caused by manufacture error,material property dispersion and external excitation randomness.A new vibration fatigue probabilistic life prediction model(VFPLPM)and a prediction method are proposed in this paper.Firstly,as one-dimensional volumetric method(ODVM)only considers the principle calculation direction,a three-dimensional space vector volumetric method(TSVVM)is proposed to improve fatigue life prediction accuracy for actual threedimensional engineering structure.Secondly,based on the two volumetric methods(ODVM and TSVVM),the material C-P-S-N fatigue curve model(CFCM)and the maximum entropy quantile function model(MEQFM),VFPLPM is established to predict the vibration fatigue probabilistic life of blade.The VFPLPM is combined with maximum stress method(MSM),ODVM and TSVVM to estimate vibration fatigue probabilistic life of blade simulator by finite element simulation,and is verified by vibration fatigue test.The results show that all of the three methods can predict the vibration fatigue probabilistic life of blade simulator well.VFPLPM &TSVVM method has the highest computational accuracy for considering stress gradient effect not only in the principle calculation direction but also in other space vector directions.

Gigacycle fatigue behaviors of two SNCM439 steels with different tensile strengthes

Gigacycle fatigue behaviors of two SNCM439 steels with different tensile strengthes were experimentally studied by rotating bending tests,to investigate the effects of the tensile strength obtained by different heat treatment processes on very high cycle fatigue failure mechanisms.The material with higher tensile strength of 1 710 MPa exhibited typical gigacycle fatigue failure characteristics,whereas one with lower tensile strength of 1 010 MPa showed only traditional fatigue limit during the tests and no gigacycle failure could be found even when the specimen ran up to more than 10 8 cycles.Metallographic and fractographic analysis were carried out by an optical microscope （OM） and scanning electron microscope （SEM）.It showed two different crack initiation mechanisms that for the specimen with lower tensile strength the crack prefers surface initiation and for that with higher strength the crack initiates from subsurface inclusions revealed by a fish-eye like microstructure.

Effect of weld reinforcement on fatigue properties of A6N01 aluminum alloy welded joint

Fatigue properties of smooth and reinforcement A6N01 aluminum alloy welded joints were characterized in this paper. Based on measured S-lgN curves and fatigue fracture morphologies, effect of weld reinforcement on the fatigue property of the welded joint was studied. Results show that the weld toe is the weakness region of the reinforcement welded joint due to the stress concentration in this area, thus the fatigue fracture occurred at the weld toe for all the reinforcement welded joints; while the fatigue property of the smooth welded joint was improved due to remove of the weld reinforcement, and the welding defect was the key factor of the fatigue fracture, thus its fracture zones mainly located at welding zone and fusion line.

Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

The stress-life curve(S–N)and low-cycle strain-life curve(E–N)are the two primary representations used to characterize the fatigue behavior of a material.These material fatigue curves are essential for structural fatigue analysis.However,conducting material fatigue tests is expensive and time-intensive.To address the challenge of data limitations on ferrous metal materials,we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S–N and E–N curves of ferrous materials.In addition,a data-augmentation framework is introduced using a conditional generative adversarial network(cGAN)to overcome data deficiencies.By incorporating the cGAN-generated data,the accuracy(R2)of the Random Forest Algorithm-trained model is improved by 0.3–0.6.It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.

Frequency-dependent random fatigue of panel-type structures made of ceramic matrix composites

The panel-type structures used in aerospace engineering can be subjected to severe highfrequency acoustic loadings in service. This paper evaluates the frequency-dependent random fatigue of panel-type structures made of ceramic matrix composites（CMCs） under acoustic loadings. Firstly, the high-frequency random responses from the broadband random excitation will result in more stress cycles in a deinite period of time. The probability density distributions of stress amplitudes will be different in different frequency bandwidths, though the peak stress estimations are identical. Secondly, the fatigue properties of CMCs can be highly frequency-dependent. The fatigue evaluation method for the random vibration case is adopted to evaluate the fatigue damage of a representative stiffened panel structure. The frequency effect through S-N curves on random fatigue damage is numerically veriied. Finally, a parameter is demonstrated to characterize the mean vibration frequency of a random process, and hence this parameter can further be considered as a reasonable loading frequency in the fatigue tests of CMCs to obtain more reliable S-N curves.Therefore, the inluence of vibration frequency can be incorporated in the random fatigue model from the two perspectives.