Hassan Elahi

Control of an oil film thickness in a hydrostatic journal bearing under different dynamic conditions

In order to meet the requirements of supporting heavy loads, high stiffness, and precise movement, the hydrostatic journal bearings are getting popularity from day to day. The efficiency of hydrostatic bearing for supporting the external load, depends upon the external source of pressure, which is responsible for supplying fluid at a certain pressure. Normally pump is used to supply fluid at a certain pressure, which has poor performance and low efficiency. This paper presents servo valve with a feedback control algorithm to achieve uniform oil thickness for positioning of a shaft in a hydrostatic journal bearing. To check the effectiveness of a proposed strategy, a number of experiments have been done in Matlab/Simulink using different conditions of external load and viscosity. It is found that proposed strategy not only has good results under the different value of viscosity but also has a linear relationship between external load and change in oil film thickness under a wide range of external load.

Trajectory based motion synchronization in a dissimilar redundant actuation system for a large civil aircraft

With the passage of time, the aircraft industry is moving towards more electric aircraft. This creates an opportunity to introduce dissimilar redundant actuation system in the primary flight control system. The problem arises, such as a force fighting (force difference between the outputs of actuators) in using dissimilar redundant actuation system. This paper presents a strategy to reduce force fighting for redundant actuation system of hydraulic actuator (HA) and electro hydrostatic actuator (EHA). A trajectory is designed on the basis of dynamic characteristics of the EHA. The feedback controller is used to improve load rejection performance and the two feed forward controllers to remove the initial force fighting between HA and EHA.

Effect of Drilling Parameters on Hole Quality of Ti-6Al-4V Titanium Alloy in Dry Drilling

In past few decades, a lot of research has been done in the field of machining to improve the quality of machined surface. Out of these machining operations, drilling is widely used in the areas of marine and aerospace for assembly requirements. Titanium alloy Ti-6Al-4V, owing to its vast applications, is regarded as an important material for these industries. Ti-6Al-4V is categorized as difficult to machine material. Based on above stated facts, an experimental study was conducted on the dry drilling of Ti-6Al-4V. The objective of this study was to establish the correlations between drilling parameters such as feed rate and spindle speed, and quality of machined surface which is evaluated in terms of drill hole diameter deviation, exit burr height and surface roughness.

Electromechanical Degradation of Piezoelectric Patches

Piezoelectric materials (PZT) are widely used as smart structure in various aerospace applications because of their sensing, actuating and energy harvesting abilities. In this research work, the degradation of the electromechanical properties of a PZT material after various mechanical and thermal shocking conditions is experimentally studied. In particular, the relationship between resistance and peak to peak voltage of Lead Zirconate Titanate (PZT-5A4E) to the degradation factor at variable frequencies and thermo-mechanical shocking conditions is considered. This research provides novel mechanism for characterizing smart structures using Mechanical Quality Factor.

A new approach to classification of upper limb and wrist movements using EEG signals

Brain computer interface (BCI) systems have ushered a new era of neural engineering research. At the core of BCIi research is development of data acquisition, filtration and classification techniques that can accurately decode the brain activity that occurs while performing a motor task. In this study, we investigate the classification accuracy of lda, QDA, Naïve Bayes, quadratic SVM and RBF SVM classifiers for classifying the flexion/extension of forearm and wrist. Moreover, hjorth parameters and PSD are employed as feature extraction techniques to derive four different feature vectors that are later used to train our classifiers. At the culmination of this study, it is shown that QDA classifier trained with PSD feature vector has the highest classification accuracy at 77.37% followed by q-SVM trained with activity feature vector at 73.97%. Apart from enhancing accuracy of classifying the four fundamental upper limb movements, this study will eventually contribute towards developing better controllers for neuro-prosthetic devices. The study has been performed experimentally with Emotiv headsets equipped with 14 electrodes to acquire EEG data from two human test subjects in synchronous mode. Classification and data analysis has been performed offline however in future the study will be extended to an online BCI system.