Guilherme Ferreira Gomes

Design Optimization of Composite Prosthetic Tubes Using GA-ANN Algorithm Considering Tsai-Wu Failure Criteria

The investigation of possible failures in composite materials is a matter of very great importance, and the Tsai-Wu criterion is an effective criterion for analyzing those flaws in anisotropic materials and defining whether the material at a given load will or will not suffer structural failure. In this study, an optimization procedure is proposed to minimize the maximum value of Tsai-Wu of laminated composite tubes subject to axial loading. Artificial neural networks and genetic algorithms are chosen as optimization tools. The results of this study show that the developed algorithm converges faster. Then, the maximum Tsai-Wu value is used as the objective function and the fiber orientations are the constraints in the optimization process. The results yielded by them are compared and discussed. Optimal results are compared with respect to the usual initial design. The design approach is recommended for structures where composites are the key load-carrying members such as orthopedic prosthesis.

A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates

The need for global damage detection methods that can be applied in complex structures has led to the development of methods that examine the structural dynamic behavior. The damage detection problem can be considered as a inverse problem with minimization of a objective function. For those reasons, a new nature-inspired optimization method based on sunflowers’ motion is introduced. The proposed sunflower optimization algorithm (SFO) technique is a population-based iterative heuristic global optimization algorithm for multi-modal problems. Compared to traditional algorithms, SFO employs terms as root velocity and pollination providing robustness. The new method is then applied in an inverse problem of structural damage detection in composite laminated plates.

A multiobjective sensor placement optimization for SHM systems considering Fisher information matrix and mode shape interpolation

Sensor placement optimization plays a key role in structural health monitoring (SHM) of large mechanical structures. Given the existence of an effective damage identification procedure, the problem arises as to how the acquisition points should be placed for optimal efficiency of the detection system. The global multiobjective optimization of sensor locations for structural health monitoring systems is studied in this paper. First, a laminated composite plate is modelled using Finite Element Method (FEM) and put into modal analysis. Then, multiobjective genetic algorithms (GAs) are adopted to search for the optimal locations of sensors. Numerical issues arising in the selection of the optimal sensor configuration in structural dynamics are addressed. A method of multiobjective sensor locations optimization using the collected information by Fisher Information Matrix (FIM) and mode shape interpolation is presented in this paper. The sensor locations are prioritized according to their ability to localize structural damage based on the eigenvector sensitivity method. The proposed method presented in this paper allows to distribute the points of acquisition on a structure in the best possible way so as to obtain both data of greater modal information and data for better modal reconstruction from a minimum point interpolation. Numerical example and test results show that the proposed method is effective to distribute a reduced number of sensors on a structure and at the same time guarantee the quality of information obtained. The results still indicate that the modal configuration obtained by multiobjective optimization does not become trivial when a set of modes is used in the construction of the objective function. This strategy is an advantage in experimental modal analysis tests, since it is only necessary to acquire signals in a limited number of points, saving time and operational costs.