Moh’d Sami Ashhab

Brief Paper: Dynamical analysis and control of microcantilevers

In this paper, we study the dynamical behavior of a microcantilever-sample system that forms the basis for the operation of atomic force microscopes (AFM). We model the microcantilever by a single mode approximation and the interaction between the sample and cantilever by a van der Waals (vdW) potential. The cantilever is vibrated by a sinusoidal input, and its deflection is detected optically. We analyze the forced dynamics using Melnikov method, which reveals the region in the space of physical parameters where chaotic motion is possible. In addition, using a proportional and derivative controller we compute the Melnikov function in terms of the parameters of the controller. Using this relation it is possible to design controllers that will remove the possibility of chaos.

Melnikov-Based Dynamical Analysis of Microcantilevers in Scanning Probe Microscopy

We study the dynamical behavior of a microcantilever-sample system that forms the basis for the operation of atomic force microscopes (AFM). We model the microcantilever by a single mode approximation. The interaction between the sample and the cantilever is modeled by a Lennard--Jones potential which consists of a short-range repulsive potential and a long-range van der Waals (vdW) attractive potential. We analyze the dynamics of the cantilever sample system when the cantilever is subjected to a sinusoidal forcing. Using the Melnikov method, the region in the space of physical parameters where chaotic motion is present is determined. In addition, using a proportional and derivative controller, we compute the Melnikov function in terms of the parameters of the controller. Using this relation, controllers can be designed to selectively change the regime of dynamical interaction.

Control of chaos in atomic force microscopes

We study the dynamical behaviour of a microcantilever-sample system that forms the basis for the operation of atomic force microscopes. We model the micro-cantilever by a single mode approximation and the interaction between the sample and cantilever by a van der Waals potential. The cantilever is vibrated by a sinusoidal input, and its deflection is detected optically. We analyze the forced dynamics using the Melnikov method, which reveals the region in the space of physical parameters where chaotic motion is possible. In addition, using a proportional and derivative controller we compute the Melnikov function in terms of the parameters of the controller. Using this relation it is possible to design controllers that will remove the possibility of chaos.

Neural network based modeling and optimization of deep drawing --- extrusion combined process

A combined deep drawing–extrusion process is modeled with artificial neural networks (ANN’s). The process is used for manufacturing synchronizer rings and it combines sheet and bulk metal forming processes. Input–output data relevant to the process was collected. The inputs represent geometrical parameters of the synchronizer ring and the outputs are the total equivalent plastic strain (TEPS), contact ratio and forming force. This data is used to train the ANN which approximates the input-output relation well and therefore can be relied on in predicting the process input parameters that will result in desired outputs provided by the designer. The complex method constrained optimization is applied to the ANN model to find the inputs or geometrical parameters that will produce the desired or optimum values of TEPS, contact ratio and forming force. This information will be very hard to obtain by just looking at the available historical input–output data. Therefore, the presented technique is very useful for selection of process design parameters to obtain desired product properties.

THE EFFECT OF SUCTION BOUNDARY CONDITION ON THE LOCAL AND AVERAGE NUSSELT NUMBERS FOR A FREE CONVECTION FLOW REGIME

We study the effect of suction on local and average Nusselt number around a cylinder surface subjected to natural convection. The complete Navier-Stokes and energy equations are formulated in terms of stream function and vorticity. They are solved using the finite difference technique. The Rayleigh number is ranged between 1 x 10 3 to 1 x 10 5 in the current simulations. An increase in the overall Nusselt number with an increase in the suction flow rate for the three simulated Rayleigh numbers is reported. For the lowest simulated flow rate, i.e. Q = 5, the average Nusslet number difference between the three Ralyeigh number modeled cases is relatively significant. However for the maximum simulated suction flow rate, i.e. Q =40, the difference is relatively small