Hardness Prediction of 7003 Aluminum Alloy by Gradient Descent Algorithm in BP Artificial Neural Networks

J.P. Ren; R.G. Song

doi:10.4028/www.scientific.net/AMR.217-218.1458

Paper Titles

Mechanical Modal and Numerical Simulation of Fracture Process of Block Wall
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The Establish and Analysis of the Quenching Residual Stress’ Function of the Club-Shaped Metal
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Thermokinetic Propertices of CTAB on Quartz Surface and its Effects on Surface
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Burnt Microstructure and Properties of 7003 Aluminum Alloy
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Hardness Prediction of 7003 Aluminum Alloy by Gradient Descent Algorithm in BP Artificial Neural Networks
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Solid-Phase Synthesis and Photocatalytic Property of Cobalt-Doped TiO₂ Mesoporous Materials
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Simultaneous Determination of p-Nitrochlorobenzene and o-Nitrophenol in Mixture by Single-Sweep Oscillopolarography Based on Cascade Neural Network
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Study of Space Big Aperture New Materials Mirror and its Technology
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Air Compressor Plate Drum’s Reliability Analysis
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Hardness Prediction of 7003 Aluminum Alloy by Gradient Descent Algorithm in BP Artificial Neural Networks

Abstract:

In order to shorten the fussy experimental process in heat treatment of 7003 aluminum alloy, back-propagation (BP) artificial neural network control of scheme has been proposed. The network of arithmetic has been deduced by using gradient descent algorithms. A BP neural network has been established between the heat treatment technique and the hardness. The results indicated that the predicted results are closed to the test results. The weakness that the nonlinear and time variation relationship between heat treatment and the hardness could be approached more accurately, effectively by using single-factor-experiment method has been overcome. Hence providing a effective, economical，rapid way for the heat treatment optimization of nonferrous metals and ferrous metal.