Mazdak Ghajari

Determination of impact location on composite stiffened panels

In this work a methodology for impact identification on composite stiffened panels using piezoceramic sensors has been presented. A large number of impacts covering a wide range of energies (corresponding to small and large mass impacts) at various locations of a composite stiffened panel have been simulated using the finite element (FE) method. To predict the impact location, artificial neural networks have been established using the data generated from FE analyses. A number of sensor signal features have been examined as inputs to the neural network and the effect of noise on the predictions has been investigated. The results of the study show that the trained network is capable of locating impacts with different energies at different locations (e.g. in the bay, over/under the stringer and on the foot of the stringer) in a complicated structure such as a composite stiffened panel.

Identification of impact force for smart composite stiffened panels

In this work, a new methodology is presented for reconstruction of the impact force history using artificial neural networks (ANNs) and spectral components of sensor data recorded by piezoceramic sensors. A large set of data, required for training the ANNs, was generated by using an efficient nonlinear finite element (FE) model of a sensorised composite stiffened panel. Impact experiments were performed on a composite plate equipped with surface-mounted piezoceramic sensors to validate the numerical modelling approach. Using the FE model of the panel, data were generated for impacts that are likely to occur during the life-time of an aircraft, consisting of large mass (e.g. dropping tool) and small mass (e.g. debris) impacts at various locations, i.e. in the bay, on the foot of a stringer and over/under a stringer. Even though the panel undergoes large deformation during impact (nonlinear response), the established networks predict the impact force history and its peak with reasonable accuracy.

Computational modelling of traumatic brain injury predicts the location of chronic traumatic encephalopathy pathology

Traumatic brain injury can lead to the neurodegenerative disease chronic traumatic encephalopathy. This condition has a clear neuropathological definition but the relationship between the initial head impact and the pattern of progressive brain pathology is poorly understood. We test the hypothesis that mechanical strain and strain rate are greatest in sulci, where neuropathology is prominently seen in chronic traumatic encephalopathy, and whether human neuroimaging observations converge with computational predictions. Three distinct types of injury were simulated. Chronic traumatic encephalopathy can occur after sporting injuries, so we studied a helmet-to-helmet impact in an American football game. In addition, we investigated an occipital head impact due to a fall from ground level and a helmeted head impact in a road traffic accident involving a motorcycle and a car. A high fidelity 3D computational model of brain injury biomechanics was developed and the contours of strain and strain rate at the grey matter–white matter boundary were mapped. Diffusion tensor imaging abnormalities in a cohort of 97 traumatic brain injury patients were also mapped at the grey matter–white matter boundary. Fifty-one healthy subjects served as controls. The computational models predicted large strain most prominent at the depths of sulci. The volume fraction of sulcal regions exceeding brain injury thresholds were significantly larger than that of gyral regions. Strain and strain rates were highest for the road traffic accident and sporting injury. Strain was greater in the sulci for all injury types, but strain rate was greater only in the road traffic and sporting injuries. Diffusion tensor imaging showed converging imaging abnormalities within sulcal regions with a significant decrease in fractional anisotropy in the patient group compared to controls within the sulci. Our results show that brain tissue deformation induced by head impact loading is greatest in sulcal locations, where pathology in cases of chronic traumatic encephalopathy is observed. In addition, the nature of initial head loading can have a significant influence on the magnitude and pattern of injury. Clarifying this relationship is key to understanding the long-term effects of head impacts and improving protective strategies, such as helmet design.

Impact damage detection in composite plates using a self-diagnostic electro-mechanical impedance-based structural health monitoring system

In this work, application of the electro-mechanical impedance (EMI) method in structural health monitoring as a damage detection technique has been investigated. A damage metric based on the real and imaginary parts of the impedance measures is introduced. Numerical and experimental tests are carried out to investigate the applicability of the method for various types of damage, such as debonding between the transducers and the plate, faulty sensors and impact damage in composite plates. The effect of several parameters, such as environmental effects, frequency sweep, severity of damage, location of damage, etc., on the damage metric has been reported.

Effects of the presence of the body in helmet oblique impacts

The oblique impact methods of motorcycle helmet standards prescribe using an isolated headform. However, in accidents the presence of the body may influence impact responses of the head and helmet. In this study, the effects of the presence of the body, in helmet oblique impacts, are investigated. Using the Finite Element method, oblique impacts of a commercially available helmet, coupled with a model of the human body, are simulated. A comparison between full-body impacts and those performed with an isolated headform show that the presence of the body modifies the peak head rotational acceleration by up to 40%. In addition, it has a significant effect on head linear acceleration and the crushing distance of the helmets liner. To include the effect of the body on head rotational acceleration in headform impacts, modifying inertial properties of the headform is proposed. The modified inertial properties are determined for a severe and frequent impact configuration. The results of helmet impacts obtained by using the modified headform are in very good agreement with those of full-body impacts; this verifies the accuracy of the proposed method.

Design Methodology for Magnetic Field-Based Soft Tri-Axis Tactile Sensors.

Tactile sensors are essential if robots are to safely interact with the external world and to dexterously manipulate objects. Current tactile sensors have limitations restricting their use, notably being too fragile or having limited performance. Magnetic field-based soft tactile sensors offer a potential improvement, being durable, low cost, accurate and high bandwidth, but they are relatively undeveloped because of the complexities involved in design and calibration. This paper presents a general design methodology for magnetic field-based three-axis soft tactile sensors, enabling researchers to easily develop specific tactile sensors for a variety of applications. All aspects (design, fabrication, calibration and evaluation) of the development of tri-axis soft tactile sensors are presented and discussed. A moving least square approach is used to decouple and convert the magnetic field signal to force output to eliminate non-linearity and cross-talk effects. A case study of a tactile sensor prototype, MagOne, was developed. This achieved a resolution of 1.42 mN in normal force measurement (0.71 mN in shear force), good output repeatability and has a maximum hysteresis error of 3.4%. These results outperform comparable sensors reported previously, highlighting the efficacy of our methodology for sensor design.

Influence of the body on the response of the helmeted head during impact

The most frequent type of injury that causes death or disability in motorcycle accidents is head injury. The only item of personal protective equipment that protects a motorcyclists head in real-world accidents is the safety helmet. The protective capability of a helmet is assessed, according to international standards, through the impact of a headform fitted with the helmet onto an anvil. The purpose of the present work was to study the influence of the presence of the body on the impact response of the helmeted head. Full-body and detached-head impacts were simulated using the finite element (FE) method. As a consequence of the presence of the body, the crushing distance of the helmet liner was drastically increased. This evidence indicated that the effect of the body should be included in impact absorption tests in order to provide conditions that are more realistic and stringent. The solution to an analytical model of the helmeted headform impact revealed that increasing the headform mass has the same influence on impact outputs, particularly the liners crushing distance, as including the whole body in impact tests. The added mass was calculated by using a helmeted Hybrid III dummy for an impact configuration that frequently occurred in real-world accidents.

Impact Detection Using Artificial Neural Networks

In this work, a number of impacts on a composite stiffened panel fitted with piezoceramic sensors were simulated with the finite element (FE) method. During impacts, the contact force history and strains at the sensors were recorded. These data were used to train, validate and test two artificial neural networks (ANN) for the prediction of the impact position and the peak of the impact force. The performance of the network for location detection has been promising but the other network should be further improved to provide acceptable predictions about the peak force.

Assessing motorcycle crash-related head injuries using finite element simulations

Motorcycle crash-related fatalities and injuries have a relatively increasing tendency compared to other vehicles. The new development of safety devices and technologies for prediction of their behaviour are therefore also increasingly important. Motorcycles have the least amount of protective devices amongst vehicles. A small disturbance in the motion of motorcycles can expose the riders to severe impacts leading to injuries especially in the appendicular part of the body, but the severest injury is usually to the head. Head injuries are the most common cause of death amongst motorcyclists (approximately 45 %). Thus, naturally, the main protective equipment preventing motorcyclists from fatal injuries is the helmet. In this study, detailed finite element models of helmet and human head are used to simulate and analyse the impacts on a protected and unprotected head in a scenario typical for motorcycle-related collisions.

SMART Platform for Structural Health Monitoring of Sensorised Stiffened Composite Panels

A SMART Platform is developed based on sensor readings for Structural Health Monitoring of a stiffened composite panel. The platforms main function is divided into three categories: Passive sensing, Active sensing and Optimal sensor positioning. The platform has self-diagnostic capabilities, i.e. prior to its application the health of the sensors and their connection will be checked to avoid any false alarm. Passive sensing results in impact location and force magnitude detection. Active sensing is performed for damage detection. It results in detecting the damage location and severity. Finally the optimal sensor location can be provided given the number of sensors and probability of detection value. This platform is the first step in applying the developed SHM methodologies to real size structures in service load conditions.