Roughing Rolling Performance Improvement and Energy Optimization by ANN- DOE Modeling
Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 12
Abstract
The rolling manufacturing system performance and output results are controlled by shape and size obtained at different sequences at roughing mill. Roughing rolling mill plays key role for sound and forging quality grades. In presence of too many parameters, the output performance variations of the different grades in rolling are a serious problem. Influence of different rolling variables is known but parameter which affects the process most is important. Artificial neural network (ANN) was constructed to predict symptoms of rolling mill with signal to noise (S/N) ratio as performance characteristics obtained by DOE in a rolling plant. The developed ANN model is useful to predict the performance of the rolling mill by most influential variable only within the range of experimental values. The new model indicates the rolling process energy optimization with help of most energy intensive single parameter for hot rolling.
Authors and Affiliations
Atul Modi, D. A. Hindoliya
Keywords
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- EP ID EP393209
- DOI 10.9790/9622-0712050110.
- Views 81
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