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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.

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.

Since the early development, the sectors where the composite materials are being used have been growing gradually. Nowadays, these materials have extensive application in the structural components in the aerospace, defense, transportation, civil, and energy industries. The composites, especially carbon fabric/epoxy resin, have excellent in-plane properties. However, the susceptibility to delamination from out-of-plane loads, due to the lack of fibers oriented through the thickness, is still one of the weaknesses of this kind of materials. The introduction of reinforcement through the thickness has the potential to increase out-of-plane properties of composite materials. This can be accomplished by different reinforcement methods, such as Z-pinning, stitching, and tufting. This work aimed to develop and implement a simplified method of through-the-thickness reinforcement, based on tufting reinforcement. The experimental methodology was developed in a CNC router machine where glass fiber and polyamide were used as the main reinforcement materials in different square patterns (5 × 5 and 7 × 7) and applied in laminated carbon/epoxy composites manufactured by vacuum resin transfer molding process. Then, efficiency of the reinforcement was evaluated by means of mechanical testing, i.e., tensile and end-notched flexure testing in order to evaluate the ability to improve the interlaminar fracture strength. Results showed that the presence of the reinforcements provided around 27% increase in the delamination resistance compared to the non-reinforced composites in the thickness direction.

With the increase in the use of composite materials, especially in the aeronautical industry, it is essential that a complete evaluation of the mechanical performance of such structures be undertaken, especially with regard to structural integrity. To assist in this task, structural health monitoring methodologies are employed in order to minimize time and maintenance costs, and errors arising principally from human factors, and which can occasionally result from the failure to properly inspect the aircrafts. This study addresses the use of an inverse method for delamination identification in carbon fiber reinforced polymers plates. First, the direct problem was modeled via a finite element method in order to obtain a faithful model that represented the real case studied. The inverse problem was solved by minimizing an objective function through genetic algorithms. Modal responses of delaminated plates are able to identify the possible location of multiple delaminations in laminated plates since the structural matrices are changed as a function of the induced damage. Numerical and experimental results showed excellent identification of small delaminations, reducing the initial search area by up to 96%, which can lead to savings in time and costs for the aeronautical industry.

From the beginnings of humanity, natural or unnatural misfortunes such as illnesses, wars, automobile accidents cause loss of body limbs like teeth, arms, legs, etc. The solution found for the replacement of these missing limbs is in the use of prostheses. Lower limbs tubes or pylons are prosthetics components that are claimed to support loads during walking and other daily tasks activities. Commonly, prosthetic tubes are manufactured using metal materials such as stainless steel, aluminum and titanium. The mass of these tubes is generally high compared to tubes made of carbon fiber reinforced polymer matrix (CFRP) composite. Therefore, this work has the objective of design, manufacturing and analyzing the feasibility of a new tube concept, made of composite material, which makes use of lattice structure and inner layer. Until the present moment, lower limb prosthesis tubes using lattice structure and ineer layer have never been studied and/or tested to date. It can be stated that the tube of rigid ribs with inner layer and angle of 40° is more efficient than those of 26° and 30°. The proposed design allows a structural weight reduction in high performance prostheses from 120 g to 40 g.

The use of composite materials in the most varied industrial sectors has increased considerably in recent years, especially those made of carbon fiber/epoxy resin. For this reason, the use of these materials need a better understanding of their mechanical behavior, especially when submitted to cyclical load requests, especially about compression, which is the object of study of this work. Residual strength degradation in CFRP is evaluated by numerical models and experimental tests relating the residual strength to the applied fatigue cycles and the maximum stress. The present work proposes to characterize the static and fatigue mechanical properties in compression of a carbon fiber/epoxy resin composite, used in the aeronautical industry, to present a mathematical model to determine the residual strength. The experimental methodology for strain limit evaluation has involved the residual strength after the fatigue test limited to 120,000 or 240,000 cycles, considering frequency of 12 Hz and stress ratio R =10 in different strain levels. This study establishes that the strain limit corresponds to deformation in which the residual strength becomes equal to the design stress (stress reduced statically to the B − basis value). Lastly, the adjusted mathematical model for determining the residual strength and the finite element simulations showed consistent results when compared with experimental tests. This study shows the relevance of the allowable levels determined from static strength to the onset of abrupt failure as a function of increasing maximum fatigue stress level that is a significant finding for the fatigue design community.

Delamination is the principal mode of failure of laminated composites. It is caused by the rupture of the fiber–matrix interface and results in the separation of the layers. This failure is induced by interlaminate tension and shear that is developed due to a variety of factors such as fatigue. Composites are known for excellent structural performance, which can be significantly affected by delamination. Such damages are not always visible on the surface and can lead to sudden catastrophic failures. To ensure a structural performance and integrity, accurate methods to monitor damages are required. This work presents a methodology for damage detection and identification on laminated composite plates using artificial neural networks fed with modal data obtained by finite element analysis. The proposed neural network to quantify damage severity achieved up to 95% success rate. A comparative study was done to evaluate the effect of boundary conditions on damage location. With the comparative study results, another neural network was proposed to locate damage position, achieving excellent results by successfully locating or by significantly reducing the search area. Both proposed ANNs use only frequency variation values as inputs, an easily obtainable quantity that requires few equipment to be acquired. The obtained results from these numerical examples indicate that the proposed approach can detect true damage locations and estimate damage magnitudes with satisfactory accuracy for this particular geometry, even under high measurement noise.

The Structural Health Monitoring (SHM) technique is today the principle approach to manage the discovery and recognizable proof of damage in the most various designing areas. The need to monitor structural behavior is increasing every day but due to the development of new materials and increasingly complex structures. This leads to the development of increasingly robust and sensitive SHM methodologies and techniques. Damage Identification by means of intelligent signal processing and optimization algorithms based in vibration metrics are particularly emphasized in this paper. The methods discussed here are mainly elaborated by the evaluation of vibrational and modal data due to the great potential (and relatively easy to apply) of application. This article discusses the use of optimization algorithms and Artificial Neural Networks (ANN) for structural monitoring in the form of a brief review. This paper can be seen as a starting point of developing SHM systems and data analysis. The content of this paper aims to help engineers and researchers find a better alternative to their specific structural monitoring problems.

Competition in the industrial environment is increasingly intense, so it is of utmost importance that organizations keep their assets in operation as much as possible (in order to produce more). In this context, there is a need for predictive maintenance, a technique that detects the health of assets in real time, allowing failures to be diagnosed before they can interrupt the operation of the assets, avoiding high financial losses. This study uses a sixteen-motor experimental setup with four different known operating conditions. The vibration signal of these motors, through signal analysis, both in time and frequency domains, is performed to evaluate the types and severities of the defects. An artificial neural network (ANN) is used to classify these defects. Considering the vibration analysis, mechanical faults can be identified quickly and conveniently. For the development of the ANN, it was necessary to perform a preprocessing of the vibration signal (response in time) due to the data size, which overwhelms the network. Thus, statistical data were used to extract key information from the vibration signal. Finally, the neural network created based on this study’s methodology presents extremely reliable results, allowing a quick and robust diagnosis of the motor operating condition.

Purpose Electric motors are present in most industries today, being the main source of power. Thus, detection of faults is very important to rise reliability, reduce the production cost, improving uptime and safety. Vibration analysis for condition-based maintenance is a mature technique in view of these objectives. Design/methodology/approach This paper shows a methodology to analyze the vibration signal of electric rotating motors and diagnosis the health of the motor using time and frequency domain responses. The analysis lies in the fact that all rotating motor has a stable vibration pattern on health conditions. If the motor becomes faulty, the vibration pattern gets changed. Findings Results showed that through the vibration analysis using the frequency domain response it is possible to detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar and bearing fault condition. Originality/value The proposed methodology is verified through a real experimental setup.