Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA
In this study, Artificial Neural Network (ANN) and Simulated Annealing (SA) techniques were integrated labeled as integrated ANN-SA to estimate optimal process parameters in abrasive waterjet (AWJ) machining operation. The considered process parameters include traverse speed, waterjet pressure, stan...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
Elsevier Limited
2011
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/29065/ http://dx.doi.org/10.1016/j.eswa.2011.01.019 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | In this study, Artificial Neural Network (ANN) and Simulated Annealing (SA) techniques were integrated labeled as integrated ANN-SA to estimate optimal process parameters in abrasive waterjet (AWJ) machining operation. The considered process parameters include traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. The quality of the cutting of machined-material is assessed by looking to the roughness average value (Ra). The optimal values of the process parameters are targeted for giving a minimum value of Ra. It was evidence that integrated ANN-SA is capable of giving much lower value of Ra at the recommended optimal process parameters compared to the result of experimental and ANN single-based modeling. The number of iterations for the optimal solutions is also decreased compared to the result of SA single-based optimization. |
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