An optimization strategy for the design of composite shells is investigated. This study differs from previous work in that an advanced analysis package is utilized to provide buckling information on potential designs. The Structural Analysis of General Shells (STAGS) finite element code is used to provide linear buckling calculations for a minimum buckling load constraint. A response surface, spanning the design space, is generated from a set of design points and corresponding buckling load data. This response surface is incorporated into a genetic algorithm for optimization of composite cylinders. Laminate designs are limited to those that are balanced and symmetric. Three load cases and four different variable formulations are examined. In the first approach, designs are limited to those whose normalized in-plane and out-of-plane stiffness parameters would be feasible with laminates consisting of two independent fiber orientation angles. The second approach increases the design space to include those that are bordered by those in the first approach. The third and fourth approaches utilize stacking sequence designs for optimization, with continuous and discrete fiber orientation angle variation, respectively. For each load case and different variable formulation, additional runs are made to account for inaccuracies inherent in the response surface model. This study concluded that this strategy was effective at reducing the computational cost of optimizing the composite cylinders. / Master of Science

"Concepts in manufacturing such as CIMS (Computer Integrated Manufacturing Systems), JIT (Just In Time), Lean Production, Virtual Manufacturing, and Flexible Fixturing have been proposed to meet the fundamental requirements of manufacturing - decrease the cost and satisfy the needs of customers. Fast fixture generation and fixture reusability are essential in the current manufacturing environment. The dissertation focuses on the models, methods, and algorithms for fixture synthesis and variation that satisfy the functional requirements specified by on-site industrial engineers. With the reusability of a fixture base combined with variation of other fixture components, fixture configuration can be rapidly adapted and accommodated to the new workpiece. The dissertation presents methods and algorithms for fixture base synthesis, which directly result in fixture reusability. Optimization functions are derived based on engineering requirements due to the mass production nature of automotive parts. Specific optimization algorithms are developed and their complexities, compared to other alternatives, are comprehensively evaluated according to different optimization functions. The fixture variation and reusability provide an engineering tool to rapidly generate and validate fixtures in production planning stage. It applies scientific reasoning methodology in combination with best knowledge of fixture designs, which heavily relies on designers' manufacturing knowledge and experience. It also provides means to bridge the gap between CAD and CAM integration and therefore reduces the new product and production development cycle time and cost while maintaining the quality of fixtures."

Researchers have applied genetic algorithms (GAs) and other heuristic optimization methods to perform truss optimization in recent years. Although a substantial amount of research has been performed on the optimization of truss member sizes, nodal coordinates, and member connections, research that seeks to simultaneously optimize the topology, geometry, and member sizes of trusses is still uncommon. In addition, most of the previous research is focused on the problem domains that are limited to a structured domain, which is defined by a fixed number of nodes, members, load locations, and load magnitudes. The objective of this research is to develop a computational method that can design efficient roof truss systems. This method provides an engineer with a set of near-optimal trusses for a specific unstructured problem domain. The unstructured domain only prescribes the magnitude of loading and the support locations. No other structural information concerning the number or locations of nodes and the connectivity of members is defined. An implicit redundant representation (IRR) GA (Raich 1999) is used in this research to evolve a diverse set of near-optimal truss designs within the specified domain that have varying topology, geometry, and sizes. IRR GA allows a Pareto-optimal set to be identified within a single trial. These truss designs reflect the tradeoffs that occur between the multiple objectives optimized. Finally, the obtained Pareto-optimal curve will be used to provide design engineers with a range of highly fit conceptual designs from which they can select their final design. The quality of the designs obtained by the proposed multi-objective IRR GA method will be evaluated by comparing the trusses evolved with trusses that were optimized using local perturbation methods and by trusses designed by engineers using a trial and error approach. The results presented show that the method developed is very effective in simultaneously optimizing the topology, geometry, and size of trusses for multiple objectives.

The automated timetabling and scheduling is one of the hardest problem areas. This isbecause of constraints and satisfying those constraints to get the feasible and optimizedschedule, and it is already proved as an NP Complete (1) [1]. The basic idea behind this studyis to investigate the performance of Genetic Algorithm on general scheduling problem underpredefined constraints and check the validity of results, and then having comparative analysiswith other available approaches like Tabu search, simulated annealing, direct and indirectheuristics [2] and expert system. It is observed that Genetic Algorithm is good solutiontechnique for solving such problems and later analysis will prove this argument. The programis written in C++ and analysis is done by using variation in various parameters.

Researchers have applied genetic algorithms (GAs) and other heuristic optimization methods to perform truss optimization in recent years. Although a substantial amount of research has been performed on the optimization of truss member sizes, nodal coordinates, and member connections, research that seeks to simultaneously optimize the topology, geometry, and member sizes of trusses is still uncommon. In addition, most of the previous research is focused on the problem domains that are limited to a structured domain, which is defined by a fixed number of nodes, members, load locations, and load magnitudes. The objective of this research is to develop a computational method that can design efficient roof truss systems. This method provides an engineer with a set of near-optimal trusses for a specific unstructured problem domain. The unstructured domain only prescribes the magnitude of loading and the support locations. No other structural information concerning the number or locations of nodes and the connectivity of members is defined. An implicit redundant representation (IRR) GA (Raich 1999) is used in this research to evolve a diverse set of near-optimal truss designs within the specified domain that have varying topology, geometry, and sizes. IRR GA allows a Pareto-optimal set to be identified within a single trial. These truss designs reflect the tradeoffs that occur between the multiple objectives optimized. Finally, the obtained Pareto-optimal curve will be used to provide design engineers with a range of highly fit conceptual designs from which they can select their final design. The quality of the designs obtained by the proposed multi-objective IRR GA method will be evaluated by comparing the trusses evolved with trusses that were optimized using local perturbation methods and by trusses designed by engineers using a trial and error approach. The results presented show that the method developed is very effective in simultaneously optimizing the topology, geometry, and size of trusses for multiple objectives.

Vision-based grasp planning can be approached as an optimization problem, where a hand configuration that indicates a stable grasp needs to be located in a large search space. In this thesis, we proposed applying genetic algorithm (GA) to grasp planning of 3D object in arbitrary shapes and any robot hand. Details are given on the selection of operators and parameters of GA. GraspIt! simulator [2] is used for implementing the proposed algorithm and as the test environment. A quantitative analysis including the comparison with simple random algorithm and simulated annealing (SA) method is carried out to evaluate the performance of the GA based planner. Both GA and SA grasp planner are tested on different sets of hand-object. And two different quality metrics are used in the planning. Given the same amount of time, GA is shown to be capable of finding a force-closure grasp with higher stability than SA.

Magnesium is known as one of the lowest density metals. With the increasing importance of fuel economy and the need to reduce weight, magnesium has proven to be a very important structural material used in transportation industry. However, the use of magnesium alloys have been limited by its tendency to corrode, creep at high temperature, and higher cost compare to aluminium alloys and steels. Polycrystal plasticity models such as VPSC and EVPSC were used to study deformation mechanisms of magnesium alloys. However, current polycrystal plasticity models with slip and twinning involve a large number of material parameters, which may not be uniquely determined. Furthermore, determining material parameters using traditional trial-and-error approach is very time consuming. Therefore, a genetic algorithm approach is developed in this thesis to optimize these material parameters. The genetic algorithm approach is evaluated by studying large strain behavior of magnesium alloys under different deformation processes. The material parameters for those models are determined by material numerical simulations based on the polycrystal model to the corresponding experimental data. Then the material parameters are used to make prediction of other deformation behaviours (stress strain curves, R values, texture evolution and lattice strain), and the performance is judged by how well the prediction match the actual experimental data. The results show that the genetic algorithm approach works well on determining parameters, it can get reliable results within a relatively short period of time. / Thesis / Master of Applied Science (MASc)

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