Application of Genetic Algorithm in Worm Gear Mechanism

Masterarbeit , 2010 , 58 Seiten , Note: 85%

Mathematik - Angewandte Mathematik

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.

CONTENTS

01 OVERVIEW
1.1 Preamble
1.2 Background of the work 1.2.2 Genetic Algorithm Techniques
1.2.3 Performance
1.2.4 Features of GA
1.2.5 Representation
1.26 Working Principles
1.2.7 Coding
1.2.8 Fitness Function
1.29 GA Operators
1.2.10 Selection : Roulette Wheel
1.3 Overview of present work
1.3.2 GA Operators
1.3.3 Tournament Selection
1.3.4 Specification of Problem
1.3.5 Mathematical model for Analysis

1.3.6 Objectiv Funktion
1.3.7 Design Variables
1.3.8 Constraints
1.3.9 The Basic GA

02 LITERATURE REVIEW
2.1 Flight Trajectory Optimization using GA 2.2 Optimal Pump Operation of water distribution systems using GA
2.3 Penalty function methods for constrained optimization with GAs
2.4 A Real Coded GA for optimization of cutting parameters in turning
2.5 Optimization of production planning in a Real world manufacturing environment

03 DEVELOPMENT OF NON-TRADITIONAL SEARCH
3.1 Introduction
3.2 Brief history of non-traditional optimization methods
3.2.1 Ant-Colony Optimization
3.2.2 Neural Network
3.2.3 Simulated Annealing
3.3 Proposed Non-Traditional Optimization Search Technique
3.3.1 Introduction
3.3.2 Benefits of GAs
3.3.3 Applications of Gas

04 FORMULATION OF WORK
4.1 Objective function
4.2 Design Variable
4.3 Binary Coding
4.5 Evaluation & Reproduction
4.6 Keys for solving problem
4.7 Crossover & Mutation
4.8 Flow chart of GA

05 RESULTS AND DISCUSSION
f(x) Vs Zg after evaluation & reproduction
f(x) Vs γn after evaluation & reproduction
f(x) Vs Zg after crossover & mutation
f(x) Vs γn after crossover & mutation

06 References

• Hochschule: Priyadarshini College of Engineering, Nagpur
• Veranstaltung: M-TECH.
• Note: 85%
• Autor: Dr. Durgesh Verma (Autor:in)
• Erscheinungsjahr: 2010
• Seiten: 58
• Katalognummer: V208547
• ISBN (eBook): 9783656359241
• ISBN (Buch): 9783656359555
• Sprache: Englisch
• Schlagworte: application, genetic, algorithm, worm, gear, mechanism