Application of Genetic Algorithm Technique for Machining Parameters Optimization in Drilling of Stainless Steel
| Autor: | B. Suresh Kumar, M. Umar, T. Deepan Bharathi Kannan, Mohammad Chand Khan, G. Rajesh kannan |
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| Rok vydání: | 2019 |
| Předmět: |
030507 speech-language pathology & audiology
0209 industrial biotechnology 03 medical and health sciences 020901 industrial engineering & automation Machining Artificial neural network Computer science Genetic algorithm Mechanical engineering Drilling 02 engineering and technology 0305 other medical science |
| Zdroj: | Mechanics and Mechanical Engineering. 23:271-276 |
| ISSN: | 2354-0192 |
| DOI: | 10.2478/mme-2019-0036 |
| Popis: | This work is aimed at developing relations between the pertinent variables that affect drilling process of stainless steel using artificial neural network. The experiments were conducted on vertical CNC machining centre. The parameters used were spindle speed and feed rate. The effect of machining parameters on entry burr height, exit burr height and surface roughness was experimentally evaluated for different spindle speeds and feed rates. A model was established between the drilling parameters and experimentally obtained data using ANN. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the burr height and surface roughness in drilling of stainless steel. Genetic algorithm (GA) technique was used in this work to identify the optimized drilling parameters. Confirmation test was conducted with the optimized parameters and it was found that confirmation test results were similar to that of GA-predicted output values. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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