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• W2901661567 abstract "Motivated to predict the properties of new material belonging to the 7000 aluminum alloys (7000-Al-alloy), in this contribution, we develop a combined model based on the artificial neural network approaches to predict the class of the Al-alloy and compute its mechanical properties. The adopted neural model is able to predict the type of alloys whether “hard and brittle” or “hard and ductile”. The concentrations of aluminum and the various additive elements (Al, Cr, Cu, Fe, Mg, Mn, Si, Ti, Zn) are used to train the neural network model. The results show a high performance in predicting and optimizing the mechanical behavior of the 7000-Al-alloys. For a better validation of our model, the 7005 (T6), 7475 (T65), 7475 (T7351), 7005 (T53), 7075 (T6), 7175 (T66), 7075 (T76), and 7075 (O) alloys are used as testing materials to predict the hardness (H), density (D), elongation (A), compressibility modulus (B), shear modulus (G) and the ratio (B/G)." @default.
• W2901661567 created "2018-11-29" @default.
• W2901661567 creator A5062225145 @default.
• W2901661567 creator A5076332412 @default.
• W2901661567 date "2019-02-01" @default.
• W2901661567 modified "2023-10-10" @default.
• W2901661567 title "Neural network model for 7000 (Al-Z) alloys: Classification and prediction of mechanical properties" @default.
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• W2901661567 doi "https://doi.org/10.1016/j.physb.2018.11.012" @default.
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