"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."

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.

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)

No description available.

No description available.

No description available.

Natural gas plants can have multiple owners for raw natural gas streams and processing facilities as well as for multiple products. Therefore, a proper cost allocation method is necessary for taxation of the profits from natural gas and crude oil as well as for cost sharing among gas producers. However, cost allocation methods most often used in accounting, such as the sales value method and the physical units method, may produce unacceptable or even illogical results when applied to natural gas processes. Wright and Hall (1998) proposed a new approach called the design benefit method (DBM), based upon engineering principles, and Wright et al. (2001) illustrated the potential of the DBM for reliable cost allocation for natural gas processes by applying it to a natural gas process. In the present research, a rigorous modeling technique for the DBM has been developed based upon a Taylor series approximation. Also, we have investigated a cost allocation framework that determines the virtual flows, models the equipment, and evaluates cost allocation for applying the design benefit method to other scenarios, particularly those found in the petroleum and gas industries. By implementing these individual procedures on a computer, the proposed framework easily can be developed as a software package, and its application can be extended to large-scale processes. To implement the proposed cost allocation framework, we have investigated an optimization methodology specifically geared toward economic optimization problems encountered in natural gas plants. Optimization framework can provide co-producers who share raw natural gas streams and processing plants not only with optimal operating conditions but also with valuable information that can help evaluate their contracts. This information can be a reasonable source for deciding new contracts for co-producers. For the optimization framework, we have developed a genetic-quadratic search algorithm (GQSA) consisting of a general genetic algorithm and a quadratic search that is a suitable technique for solving optimization problems including process flowsheet optimization. The GQSA inherits the advantages of both genetic algorithms and quadratic search techniques, and it can find the global optimum with high probability for discontinuous as well as non-convex optimization problems much faster than general genetic algorithms.

The all-terrain vehicle (ATV) has been popular due to its simplicity in controlling, high recreational value, and sports utility, Furthermore, seating comfort and control aptness are affected mainly by the suspension and steering mechanism. Therefore, this research is directed towards the ATV¡¦s front suspension and steering mechanism, bearing in mind a set of systematized design procedures as the design and development basis of the front suspension and steering mechanism. First, we will investigate the papers on the front suspension and steering mechanism in order to make an induction on its characteristics and demand. Then, the theories of kinematic analysis of the front suspension and steering mechanism are established. The aided analyzing computer program we edit using the theories, moreover could be the design basis. And, to carry out the creative design of the front suspension and steering mechanism using the systematized procedure of creative mechanism design. In this investigation, we have successfully constructed atlas of the creative mechanism, and the result supplies the following design to select; Finally, we complete the kinematic design of the front suspension and steering mechanism based on the Genetic algorithm (GA), and we can obtain the better mechanism dimensions than Yamaha 660 ATV.

In recent years, microstrip antennas are suitable for applications in wireless communication systems because they have the characteristics of compact size, light weight, low cost and easy to manufacture. So, they play an important role in the navigation equipment of the rocket, space shutter, personal communication, etc. However, in the design and synthesis of antennas, there are a large number of design variables that affect the antenna performance. In early stages, some researchers did not use any optimization tool in parameter tuning of antennas design. The one utilized most is the ¡§trial and error¡¨ method, which is very time-consuming in order to find a suitable solution to verify the possibilities of the antenna structure. Genetic algorithms have been shown to be effective in the design of broadband microstrip antenna. However, their effectiveness with various degrees depends on the skills of the different genetic algorithms. In this dissertation, we propose a Genetic Algorithm (GA)-based refined method to enhance the effectiveness and to solve the gap-coupled microstrip antenna design problem (largest impedance bandwidth). The refined method with optimization process improves the computing performance comparing with the conventional genetic algorithm. By the refined GA method, bandwidth can be widened up to 3.84 times that of a single excited patch. Furthermore, we present a new design for Ultra Wideband (UWB) antenna. In the new research topic, it is expected that the genetic algorithm can find out a range of feasible (range-based) solutions instead of a few of solutions. As a result, the manufacturing process will have more convenience and practicability. Finally, we propose a new method to overcome the problem of signal interference with the UWB system operations. A band notched characteristic is achieved for the antenna to restrain the interference bandwidth. The disclosed antenna and the circuitry for the antenna system are easily integrated. With the simple structure, the fabrication cost for the antenna is also reduced.