Optimization of turning parameters using genetic algorithm method
This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). This method was demonstrated for the optimization of machining parameters for turning operation using conventional lathe machines. Currently, everybody has start realizing the importance of this...
| Main Author: | |
|---|---|
| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2008
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/258/ http://umpir.ump.edu.my/id/eprint/258/ http://umpir.ump.edu.my/id/eprint/258/1/shah_izwandi.pdf |
| Summary: | This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). This method was demonstrated for the optimization of machining parameters for turning operation using conventional lathe machines. Currently, everybody has start realizing the importance of this new manufacturing optimization in order to improve the performance and its efficiency. The purpose of this project is to find the optimum parameters values for turning operations that will benefit such as reduces the machining time, improves their quality and productivity and also minimize the unit cost of the product. GA can be used in optimization problems such as scheduling, materials engineering , optimal control, and so forth. This approach has led to the important following discoveries such as GA has robustness, the balance between efficiency and performances for survival in many different environments. The machining parameters that been consider in this thesis are cutting speed, feed rate and depth of cut. The GA simulation are been develop to achieve the objective. The MATLAB software will be use to develop the GA simulation. An example to apply the Genetic Algorithm to the problem has been presented at the end of this paper to give more understanding picture from the application of the system and how its work. The result obtained from this simulation shown GA has a potential for improvements in order to optimize the turning parameters and minimize the unit production cost. The simulation based on Genetic Algorithm are successful develop and the optimum parameters values are obtained from the simulation. |
|---|