Application of Genetic Algorithm in Worm Gear Mechanism (eBook, ePUB)
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Master's Thesis from the year 2010 in the subject Mathematics - Applied Mathematics, grade: 85%, Priyadarshini College of Engineering, Nagpur, course: M-TECH., language: English, abstract: In this study, a foundation and solution technique using Genetic Algorithm (GA) for design optimization of worm gear mechanism is presented for the minimization of power-loss of worm gear mechanism with respect to specified set of constraints. Number of gear tooth and helix (thread) angle of worm are used as design variables and linear pressure, bending strength of tooth and deformation of worm are set as co...
Master's Thesis from the year 2010 in the subject Mathematics - Applied Mathematics, grade: 85%, Priyadarshini College of Engineering, Nagpur, course: M-TECH., language: English, abstract: In this study, a foundation and solution technique using Genetic Algorithm (GA) for design optimization of worm gear mechanism is presented for the minimization of power-loss of worm gear mechanism with respect to specified set of constraints. Number of gear tooth and helix (thread) angle of worm are used as design variables and linear pressure, bending strength of tooth and deformation of worm are set as constraints. The GA in Non-Traditional method is useful and applicable for optimization of mechanical component design. The GA is an efficient search method which is inspired from natural genetics selection process to explore a given search space. In this work, GA is applied to minimize the power loss of worm gear which is subjected to constraints linear pressure, bending strength of tooth and deformation of worm. Up to now, many numerical optimization algorithms such as GA, Simulated Annealing, Ant-Colony Optimization, Neural Network have been developed and used for design optimization of engineering problems to find optimum design. Solving engineering problems can be complex and a time consuming process when there are large numbers of design variables and constraints. Hence, there is a need for more efficient and reliable algorithms that solve such problems. The improvement of faster computer has given chance for more robust and efficient optimization methods. Genetic algorithm is one of these methods. The genetic algorithm is a search technique based on the idea of natural selection and genetics.
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