Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining
The machining operation can be generally classified into two types which are traditional machine and non-traditional (modem) machine. There are two types of machining employed in this research, end milling (traditional machining) and abrasive waterjet machining (non-traditional machining). Optimizin...
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| Format: | Thesis |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/29267/5/NorfadzlanYusupMFSKSM2012.pdf |
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| Summary: | The machining operation can be generally classified into two types which are traditional machine and non-traditional (modem) machine. There are two types of machining employed in this research, end milling (traditional machining) and abrasive waterjet machining (non-traditional machining). Optimizing the process parameters is essential in order to provide a better quality and economics machining. This research develops an optimization algorithm using artificial bee colony (ABC) algorithm to optimize the process parameters that will lead to minimum surface roughness (Ra) value for both end milling and abrasive waterjet machining. In end milling, three process parameters that need to be optimized are the cutting speed, feed rate and radial rake angle. For abrasive waterjet, five process parameters that need to be optimized are the traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. These machining process parameters significantly impact on the cost, productivity and quality of machining parts. The ABC simulations are developed to achieve the minimum Ra value in both end milling and abrasive waterjet machining. The results obtained from the simulation are compared with experimental, regression modelling, Genetic Algorithm (GA) and Simulated Annealing (SA). In end milling, ABC reduced the Ra by 10% and 8% compared to experimental and regression. In abrasive waterjet, the performance was much better where the Ra value decreased by 28%, 42%, 2% and 0.9% compared to experimental, regression, GA and SA respectively. |
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