Advances in Fracture and Damage Mechanics VII

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Authors: Li Juan Cao, Shou Ju Li, Zi Chang Shangguan
Abstract: The inverse problem of structure damage detection is formulated as an optimization problem, which is then solved by using artificial neural networks. Based on the hybrid optimization strategy, the parameter identification algorithm was presented according to the measured data of vibrating frequency and mode shapes in the damaged structure. The proposed algorithm combines the local optimum method having fast convergence ability with the neural networks having global optimum ability. By doing this, the local minimization problem of the local optimum method can be solved, and the convergence speed of the global optimum method can be improved. The investigation shows that to identify the location and magnitude of the damaged structure by using an artificial neural network is feasible and a well trained artificial neural network by Levenberg-Marquardt algorithm reveals an extremely fast convergence and a high degree of accuracy.
Authors: Jing Wang, Xiao Wei Gao, Chuan Zeng Zhang
Abstract: This paper presents an elastostatic crack analysis in three-dimensional (3D), isotropic, functionally graded and linear elastic solids. A boundary element method (BEM) based on boundary-domain integral equations is applied. A multi-domain technique and discontinuous elements at the crack-front are adopted. To show the effects of the materials gradients on the crackopening- displacements (CODs) and the stress intensity factors (SIFs), numerical results for a pennyshaped crack are presented and discussed.
Authors: M.H. Aliabadi, A. Apicella, Albert Sanqirgo-Rodriguez
Abstract: In this paper the application of the finite element method is presented to modeling piezoelectric sensors and actuators for use in structural health monitoring of composite panels. It this demonstrated that FEM can be used to simulate sensorised composite panels and investigate the damage detection capability of sensors.
Authors: Min Jae Choi, In Geun Kang, Kwang Ho Kim, Dong Il Kwon
Abstract: The instrumented indentation technique (IIT) is a powerful method for evaluating mechanical properties of materials such as elastic modulus, tensile strength, fracture toughness and residual stress. Especially, IIT is a promising alternative to conventional methods of residual stress measurement such as hole drilling, saw cutting, X-ray/neutron diffraction, and ultrasonic methods because of its various advantages of nondestructive specimen preparation, easy process, characterization of material properties on local scales and measurement of in-service structures. Evaluation of residual stress using IIT is based on the key concepts that the deviatoric-stress part of the residual stress affects the indentation load-depth curve and that the quantitative residual stress in a target region can be evaluated by analyzing the difference between the residual stress-induced indentation curve and residual stress-free curve. To verify the applicability of the suggested technique, indentation tests were performed on the welded zone.
Authors: Kyung Woo Lee, Hyun Uk Kim, Sang Wook Park, Jung Suk Lee, Kwang Ho Kim, Dong Il Kwon
Abstract: This study focused on the determination of fracture toughness by instrumented indentation technique. A theoretical model to estimate the fracture toughness of ductile materials is proposed and used to verify those results. Modeling of IIT to evaluate fracture toughness is based on two main ideas; the energy input up to characteristic fracture initiation point during indentation was correlated with material’s resistance to crack initiation and growth, and this characteristic fracture initiation point was determined by concepts of continuum damage mechanics. The estimated fracture toughness values obtained from the indentation technique showed good agreement with those from conventional fracture toughness tests based on CTOD. In addition, we confirmed that the proposed model can be also applied in the brittle material through modification of void volume fraction.

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