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- W2048920321 abstract "Surface roughness is an important outcome in the machining process and it plays a major role in the manufacturing system. Prediction of surface roughness has been a challenge to researchers because it is impacted by different machining parameters and the inherent uncertainties in the machining process. Prediction of surface roughness will benefit the manufacturing process to be more productive and competitive at the same time to reduce any pre-processing of the machined workpiece in order to meet the technical specifications. In this study, a hybrid GA-LM ANN is proposed for the prediction of surface roughness during roughing process in turning operation. To verify the performance of the proposed approach, the results are compared with the results obtained by training an ANN using GA or LM. The results have shown that the hybrid ANN outperformed the other two algorithms." @default.
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- W2048920321 date "2014-09-01" @default.
- W2048920321 modified "2023-09-26" @default.
- W2048920321 title "A Hybrid Neural Network for Prediction of Surface Roughness in Machining" @default.
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- W2048920321 doi "https://doi.org/10.4028/www.scientific.net/amm.633-634.579" @default.
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