M Mohammadali - PhD Student
A Moslemi Pak - Undergraduate Student
H Ahmadian - Professor ,Modal Analysis Research Laboratory, Department of Mechanical Engineering, Iran University of Science & Technology, Narmak, ۱۶۸۴۶۱۳۱۱۴, Tehran, Iran,

Finding the best arrangement of turbine blades to minimize residual unbalance is known as turbine balancing problem (TBP). In the turbine-blade manufacturing industry, turbine-blades are manufactured by various methods such as machining. Then the turbine-blades are assembled around the periphery of fan to form a cylindrical roll of blades. Manufacturing inaccuracies causes weight differences for each turbine-blade. The weight differences cause unbalancing and reducing the performance and fatigue life of turbine engine. TBP is known as combinatorial optimization and NP-Hard problem in turbine manufacturing industry and maintenance. An exact solution method for solving TBP as a NP-Hard problem in reasonable time is not appropriate. A recent method is presented in this article for solving this problem. Applying genetic algorithms in solving TBP will provide the nearest results to the optimum result more quickly. Genetic algorithms is based on the nature evolutionary laws as a subset of evolutionary computations and known as a recent method for solving combinatorial optimization problems. Reducing the static unbalance produced in each stage of a turbine engine rotor, is the main objective of using genetic algorithm in this article. Improving the results of algorithm as much as possible was obtained by heuristics crossover and mutation by considering the main objective of the problem. Heuristics methods presented for crossover and mutation in this article makes the results of algorithm unique. At the end, the results of geneticalgorithms was compared by an experimental results used for manufacturing a turbine engine in industrial corporations.

Turbine balancing, combinatorial optimization, Genetic algorithm, Crossover, Mutation