• 한국해양공학회지
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• 제10권1호
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• pp.65-74
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• 1996

Up to now, the existing crack growth modelling has used a mathematical approximation but an assumed function have a great influence on this method. Especially, crack growth behavior that shows very strong nonlinearity needed complicated function which has difficulty in setting parameter of it. The main characteristics of neural network modelling to engineering field are simple calculations and absence of assumed function. In this paper, after discussing learning and generalization of neural networks, we performed crack growth modelling on the basis of above learning algorithms. J'-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).

• 대한기계학회논문집A
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• 제20권4호
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• pp.2752-2759
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• 1996

This paper presents crack growth analysis approach on the basis of neural networks, a branch of cognitive science to high temperature low cycle fatigue that shows strong nonlinearity in material behavior. As the number of data patterns on crack growth increase, pattern classification occurs well and two point representation scheme with gradient of crack growth curve simulates crack growth rate better than one point representation scheme. Optimal number of learning data exists and excessive number of learning data increases estimated mean error with remarkable learning time J-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).

• Structural Engineering and Mechanics
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• 제5권6호
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• pp.817-837
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• 1997

In the present contribution an interface crack model is introduced which is capable of modelling crack initialisation and growth in aluminium as well as in Fibre Metal Laminates. Interface elements are inserted in a finite element mesh with a yield function which bounds all states of stress in the interface. Hardening occurs after a state of stress exceeds the yield stress of the material. The hardening branch is bounded by the ultimate stress of the material. Thereafter, the state of stress is reduced to zero while the inelastic deformations grow. The energy dissipated by the inelastic deformations in this process equals the fracture energy of the material. The model is applied to calculate the onset and growth of cracking in centre cracked plates made of aluminium and GLARE$^{(R)}$. The impact of the model parameters on the performance of the crack model is studied by comparisons of the numerical results with experimental data.

• 대한기계학회논문집A
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• 제24권3호
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• pp.634-644
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• 2000

Fatigue crack growth and life is estimated by various fracture mechanical parameters but affected by load, material and environment. Fatigue character of component without surface notch cannot be e valuated by above-mentioned parameters due to microstructure of in-service material. Single fracture mechanical parameter or nondestructive parameter cannot predict fatigue damage in arbitrary boundary condition but multiple fracture mechanical parameters or nondestructive parameters can Fatigue crack growth modelling with three point representation scheme uses this merit but has limit on real-time monitoring. Therefore, this study shows fatigue damage model using backpropagatior. neural networks on the basis of X-ray half breadth ratio B/$B_o$ fractal dimension $D_f$ and fracture mechanical parameters can predict fatigue crack growth rate da/dN and cycle ratioN/$N_f$ at the same time within engineering estimated mean error(5%).

• 한국자동차공학회논문집
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• 제7권6호
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• pp.258-269
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• 1999

It is important to evaluate fatigue damage of in-service material in respect to assure safety and remaining fatigue life in structure and mechanical components under cyclic load . Fatigue damage is represented by mathematical modelling with crack growth rate da/dN and cycle ration N/Nf and is detected by X-ray diffraction and ultrasonic wave method etc. But this is estimated generally by single parameter but influenced by many test conditions The characteristics of it indicates fatigue damage has complex fracture mechanism. Therefore, in this study we propose that back-propagation neural networks on the basis of ration of X-ray half-value breath B/Bo, fractal dimension Df and fracture mechanical parameters can construct artificial intelligent networks estimating crack growth rate da/dN and cycle ratio N/Nf without regard to stress amplitude Δ $\sigma$.

• 한국정밀공학회지
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• 제16권2호통권95호
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• pp.225-233
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• 1999

This paper describes a versatile finite element technique which has been used to investigate of wide range of structural defects of practical importance. The procedure automatically remeshes the three-dimensional finite element model during the stages of crack growth. Problems analyzed to date include the surface cracks in leak-before-break situations, the development of quarter-elliptical corner defects, planar semi-elliptical surface defects and the fatigue growth of defects.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.703-712
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• 2017

The composite blades of offshore wind turbines accumulate structural damage such as fatigue cracking due to harsh operation environments during their service time, leading to premature structural failures. This paper investigates various fatigue crack models for reproducing crack development in composite blades and proposes a stochastic approach to predict fatigue crack evolution and to analyse failure probability for the composite blades. Three typical fatigue models for the propagation of fatigue cracks, i.e., Miner model, Paris model and Reifsnider model, are discussed to reproduce the fatigue crack evolution in composite blades subjected to cyclical loadings. The lifetime probability of fatigue failure of the composite blades is estimated by stochastic deterioration modelling such as gamma process. Based on time-dependent reliability analysis and lifecycle cost analysis, an optimised maintenance policy is determined to make the optimal decision for the composite blades during the service time. A numerical example is employed to investigate the effectiveness of predicting fatigue crack growth, estimating the probability of fatigue failure and evaluating an optimal maintenance policy. The results from the numerical study show that the stochastic gamma process together with the proper fatigue models can provide a useful tool for remaining useful life predictions and optimum maintenance strategies of the composite blades of offshore wind turbines.

• Structural Engineering and Mechanics
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• 제34권3호
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• pp.377-390
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• 2010

This paper studies fracture initiation direction of two parallel non-coplanar cracks of equal length. Using the dislocation pile-up modelling, singular integral equations for two parallel cracks subjected to mixed-mode loading are derived and the crack-tip field including singular and non-singular terms is obtained. The kinking angle is determined by using the maximum hoop stress criterion, or the ${\sigma}_{\theta}$-criterion. Results are presented for simple uniaxial tension and biaxial loading. The biaxiality ratio has a noticeable influence on crack growth direction. For the case of biaxial tension, when neglecting the T-stress the crack branching angle is overestimated for small crack inclination angles relative to the largest applied principal stress direction, and underestimated for large crack inclination angles.

• Smart Structures and Systems
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• 제18권3호
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• pp.569-584
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• 2016

This paper presents a method for stochastic modelling of fatigue crack growth and optimising inspection and maintenance strategy for the structural members of steel bridges. The fatigue crack evolution is considered as a stochastic process with uncertainties, and the Gamma process is adopted to simulate the propagation of fatigue crack in steel bridge members. From the stochastic modelling for fatigue crack growth, the probability of failure caused by fatigue is predicted over the service life of steel bridge members. The remaining fatigue life of steel bridge members is determined by comparing the fatigue crack length with its predetermined threshold. Furthermore, the probability of detection is adopted to consider the uncertainties in detecting fatigue crack by using existing damage detection techniques. A multi-objective optimisation problem is proposed and solved by a genetic algorithm to determine the optimised inspection and maintenance strategy for the fatigue affected steel bridge members. The optimised strategy is achieved by minimizing the life-cycle cost, including the inspection, maintenance and failure costs, and maximizing the service life after necessary intervention. The number of intervention during the service life is also taken into account to investigate the relationship between the service life and the cost for maintenance. The results from numerical examples show that the proposed method can provide a useful approach for cost-effective inspection and maintenance strategy for fatigue affected steel bridges.

• International Journal of Railway
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• 제1권3호
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• pp.106-112
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• 2008

Rolling-sliding, cyclic contact of wheel and rail progressively alters the microstructure of the contacting steels, eventually leading to micro-scale crack initiation, wear and macro-scale crack growth in the railhead. Relating the microstructural changes to subsequent wear and cracking is being accomplished through modelling at three spatial scales: (i) bulk material (ii) multi-grain and (iii) sub-grain. The models incorporate detailed information from metallurgical examinations of used rails and tested rail material. The initial 2-dimensional models representing the rail material are being further developed into 3-dimensional models. Modelling is taking account of thermal effects, and traffic patterns to which the rails are exposed.