Mohammed Shalaby

Robust Design of RF-MEMS Cantilever Switches Using Contact Physics Modeling

This paper presents the robust design optimization of an RF-MEMS direct contact cantilever switch for minimum actuation voltage and opening time, and maximum power handling capability. The design variables are the length and thickness of the entire cantilever, the widths of the sections of the cantilever, and the dimple size. The actuation voltage is obtained using a 3-D structural-electrostatic finite-element method (FEM) model, and the opening time is obtained using the same FEM model and the experimental model of adhesion at the contact surfaces developed in our previous work. The model accounts for an unpredictable variance in the contact resistance resulting from the micromachining process for the estimation of the power handling. This is achieved by taking the ratio of the root mean square power of the RF current (ldquosignalrdquo) passing through the switch to the contact temperature (ldquonoiserdquo) resulting from the possible range of the contact resistance. The resulting robust optimization problem is solved using a Strength Pareto Evolutionary Algorithm, to obtain design alternatives exhibiting different tradeoffs among the three objectives. The results show that there exists substantial room for improved designs of RF-MEMS direct-contact switches. It also provides a better understanding of the key factors contributing to the performances of RF-MEMS switches. Most importantly, it provides guidance for further improvements of RF-MEMS switches that exploit complex multiphysics phenomena.

Design for Disassembly With High-Stiffness Heat-Reversible Locator-Snap Systems

Recent legislative and social pressures have driven manufacturers to consider effective part reuse and material recycling at the end of product life at the design stage. One of the key considerations is to design and use joints that can disengage with minimum labor, part damage, and material contamination. This paper presents a unified method to design a high-stiffness reversible locator-snap system that can disengage nondestructively with localized heat, and its application to external product enclosures of electrical appliances. The design problem is posed as an optimization problem to find the locations, numbers, and orientations of locators and snaps as well as the number, locations, and sizes of heating areas, which realize the release of snaps with minimum heating area and maximum stiffness while satisfying any motion and structural requirements. The screw theory is utilized to precalculate a set of feasible orientations of locators and snaps, which are examined during optimization. The optimization problem is solved using the multi-objective genetic algorithm coupled with the structural and thermal finite element analysis. The method is applied to a two-piece enclosure of a DVD player with a T-shaped mating line. The resulting Pareto-optimal solutions exhibit alternative designs with different trade-offs between the structural stiffness during snap engagement and the area of heating for snap disengagement. Some results require the heating of two areas at the same time, demonstrating the idea of a lock-and-key.

Design of Spring Coupling for High Q, High Frequency MEMS Filters

This paper presents the design optimization of the coupling beam of wine glass (WG) mode micromechanical disk filters using the simulated annealing algorithm. The filter under consideration consists of two identical wine-glass mode disk resonators, mechanically coupled by a flexural mode beam. Such coupled two-resonator system exhibits two mechanical resonance modes with closely spaced frequencies that define the filter passband. The frequencies of the constituent resonators determine the center frequency of the filter, while the bandwidth is determined by the stiffness and location of attachment of the coupling beam. The goal is to design a filter with a commonly used bandwidth, namely 100 kHz. The design variables that control the bandwidth value are the beam length, the beam width, and the location of attachment of the coupling beam from the center. The simulated annealing algorithm is used to solve the optimization problem, since the governing dynamic equations of the resonator-coupling system are highly nonlinear. The resulting optimum design is simulated using the finite element method, which confirms the achievement of the desired center frequency and bandwidth.Copyright

Design Optimization of Reverse Osmosis Water Desalination Systems via Genetic Algorithms

This paper explores the application of genetic algorithms (GA) for optimal design of reverse osmosis (RO) water desalination systems. While RO desalination is among the most cost and energy efficient methods for water desalination, optimal design of such systems is rarely an easy task. In these systems, salty water is made to flow at high pressure through vessels that contain semi-permeable membrane modules. The membranes can allow water to flow through, but prohibit the passage of salt ions. When the pressure is sufficiently high, water molecules will flow through the membranes leaving the salt ions behind and are collected in a fresh water stream. Typical system design variables for RO systems include the number and layout of the vessels and membrane modules, as well as the operating pressure and flow rate. This paper explores models for single and two-stage RO pressure vessel configurations. The number and layout of the vessels and membrane modules are regarded as discrete variables, while the operating pressures and flow rate are regarded as continuous variables. GA is applied to optimize the models for minimum overall cost of unit produced fresh water. Case studies are considered for four different water salinity concentration levels. In each of the studies, three different types of crossover are explored in the GA. While all the studied crossover types yielded satisfactory results, the crossover types that attempt to exploit design variable continuity performed slightly better, even for the discrete variables of this problem.Copyright

Studies on the design of reverse osmosis water desalination systems for cost and energy efficiency

This paper explores optimal design of reverse osmosis (RO) systems for water desalination. In these systems, salty water flows at high pressure through vessels containing semi-permeable membrane modules. The membranes can allow water to flow through, but prohibit the passage of salt ions. When the pressure is sufficiently high, water molecules will flow through the membranes leaving the salt ions behind, and are collected in a fresh water stream. Typical system design variables include the number and layout of the vessels and membrane modules, as well as the operating pressure and flow rate. This paper presents models for single and two-stage pressure vessel configurations. The models are used to explore the various design scenarios in order to minimize the cost and energy required per unit volume of produced fresh water. Multi-objective genetic algorithm (GA) is used to generate the Pareto-optimal design scenarios for the systems. Case studies are considered for four different water salinity concentration levels. Results of the studies indicate that even though the energy required to drive the RO system is a major contributor to the cost of fresh water production, there exists a tradeoff between minimum energy and minimum cost. An additional parametric study on the unit cost of energy is performed in order to explore future trends. The parametric study demonstrates how an increase in the unit cost of energy may shift the minimum cost designs to shift to more energy-efficient design scenarios.Copyright

Optimal heat-reversible snap joints for frame-panel assembly in aluminium space frame automotive bodies

This paper presents a method for designing heat-reversible snap joints, locator-snap systems that detach non-destructively by heating a certain location of parts. It is expected to dramatically improve the recyclability of aluminium space frame (ASF) bodies by enabling clean separation of frames and body panels. Extending our previous work on the sequential design of the locators and heating area (Shalaby and Saitou, 2005), the method simultaneously optimises locators, heating area and snaps for ensuring joint detachment with minimum heat and avoiding resonance due to vehicle vibration. A multi-objective genetic algorithm is utilised to search for Pareto optimal design alternatives.

DESIGN OF HEAT-REVERSIBLE SNAP JOINTS FOR SPACE FRAME BODIES

This paper presents the design of new joints, heat-reversible snaps, which allow easy, non-destructive, and clean detaching between internal frames and external panels in automotive bodies. It is expected to dramatically reduce the end-of-life environmental impacts of the aluminum space frame bodies, which currently suffer from poor material recyclability. While the assembly process is analogous to normal locator-snap systems, the heat-reversible snaps can be unlocked non-destructively upon heating the panel at a certain location, via the non-uniform thermal deformation of the panel. The optimum number and locations of the locators on the given panel are found based on the equivalent springs that represent the stiffness of the locator. Then, the locations of snaps and heating that ensure unlocking upon heating of the minimum area on the panel are obtained. Finally, a case study on an automotive fender panel assembly is discussed.Copyright

Convex Estimators for Optimization of Kriging Model Problems

This paper presents a framework for identification of the global optimum of Kriging models that have been tuned to approximate the response of some generic objective function and constraints. The framework is based on a branch and bound scheme for subdivision of the search space into hypercubes while constructing convex underestimators of the Kriging models. The convex underestimators, which are the key development in this paper, provide a relaxation of the original problem. The relaxed problem has two main features: (i) convex optimization algorithms such as sequential quadratic programming (SQP) are guaranteed to find the global optimum of the relaxed problem and (ii) objective value of the relaxed problem is a lower bound within a hypercube for the original (Kriging model) problem. As accuracy of the convex estimators improves with subdivision of a hypercube, termination of a branch happens when either: (i) solution of the relaxed problem within the hypercube is no better than current best solution of the original problem or (ii) best solution of the original problem and that of the relaxed problem are within tolerance limits. To assess the significance of the proposed framework, comparison studies against genetic algorithm (GA), particle swarm optimization (PSO), random multistart sequential quadratic programming (mSQP), and DIRECT are conducted. The studies include four standard nonlinear test functions and two design application problems of water desalination and vehicle crashworthiness. The studies show the proposed framework deterministically finding the optimum for all the test problems. Among the tested stochastic search techniques (GA, PSO, mSQP), mSQP had the best performance as it consistently found the optimum in less computational time than the proposed approach except on the water desalination problem. DIRECT deterministically found the optima for the nonlinear test functions, but completely failed to find it for the water desalination and vehicle crashworthiness problems.

Selection of Precision Machining Cutting Parameters via a Modified Efficient Global Optimization Approach

The primary objective in precision machining is usually to attain excellent dimensional accuracy and surface finish. In addition, complimentary objectives such as cost and production rate are also important. Proper selection of cutting parameters can profoundly affect both primary and secondary machining performance objectives. While simplified and/or empirical models exist for machining processes, none of those models provides accurate prediction of the dynamic cutting forces, which in turn govern the obtainable quality of the machined surfaces. Finite element analysis (FEA) via ABAQUS/Explicit is adopted in this paper for predicting the machining dynamic cutting forces. Rake and clearance angles, as well as cutting speed are set as the design variables for optimization. Since the machining model requires significant computational resources, economizing the number of FEA runs is desirable. The optimization approach adopted is based off Efficient Global Optimization (EGO), where Kriging models are trained to predict the underlying behavior of the machining process via a finite set of sample points. New sample points are then generated via a multi-objective genetic algorithm that seeks locations of optima and/or high uncertainty in the Kriging models. Machining performance of the new samples is then evaluated via FEA, the Kriging models are re-trained and the process is repeated until one of termination criteria is met. The application study presented is an orthogonal cutting test for ultra-precision micro-cutting using diamond tools.Copyright

A Framework for Parallel Sampling of Design Space With Application to Vehicle Crashworthiness Optimization

This paper presents a framework for simulation-based design optimization of computationally-expensive problems, where economizing the generation of sample designs is highly desirable. Various meta-modeling schemes are used in practice in order to approximate the input-output relationships in the designed system and suggest candidate locations in the design space where high quality designs are likely to be found. One such popular approach is known as Efficient Global Optimization (EGO), where an initial set of design samples is used to construct a Kriging model, which approximates the system output and provides a prediction of the uncertainty in the approximations. Variations of EGO suggest new sample designs according to various infill criteria that seek to maximize the chance of finding high quality designs. The new samples are then used to update the Kriging model and the process is iterated. This paper attempts to address one of the limitations of EGO, which is the generation of the infill samples often becoming a difficult optimization problem in its own right for a larger number of design variables. This is done by adapting a previously developed approach for locating the optimum of a Kriging model to a modified EGO infill sampling criterion. The new implementation also allows the generation of multiple new samples at a time in order to take advantage of parallel computing. After testing on analytical functions, the algorithm is applied to vehicle crashworthiness design of a full vehicle model of a Geo Metro subject to frontal crash conditions.Copyright