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• W2612425001 abstract "This paper addresses the development of a neural network (NN) model assisted with nonlinear data transformation for flow stress prediction of an aluminium alloy. The stress-strain data were collected from a plane strain compression (PSC) test machine, which is capable of testing under a wide range of strain rates. A straightforward NN modelling failed to give satisfactory stress predictions across the whole range of the input conditions. Careful analysis of the error behaviour led to the finding that the poor performance was caused by the large variation of the strain rates. To improve the model performance, a nonlinear data transformation was adopted for the strain rate, and the flow stress prediction was improved significantly." @default.
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• W2612425001 date "2004-09-01" @default.
• W2612425001 modified "2023-09-23" @default.
• W2612425001 title "Nonlinear Data Transfonnation to Improve Flow Stress Prediction Using Neural Networks" @default.
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• W2612425001 doi "https://doi.org/10.1016/s1474-6670(17)31052-2" @default.
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