Accuracy modelling of powder metallurgy process using multilayer neural network

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19 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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Accuracy modelling of powder metallurgy process using
multilayer neural network
D. Drndarević, B. Reljin
University of Belgrade
School of Electrical Engineering
http://www.etf.bg.ac.rs
POWDER METALLURGY, Vol. 43, No. 1, pp. 25-29, Jan, 2000
References:
Abstract:
In the present paper a neural network approach to accurate modelling of the PM process,
particularly the production of self-lubricating bearings, is derived. The model is based on a three
layer neural network with a backpropagation learning algorithm. In applying the derived model, the
deviations in sintered part dimensions are decreased, thus eliminating the need for additional
operations to achieve the required accuracy of the final parts. The simulated results demonstrated
that the neural network model is more accurate than the standard design procedure based on the
statistical processing of experimental data. Also, the neural network exhibits the very useful feature
that the same algorithm (and/or configuration) can be used for resolving different tasks (only new
training set should be applied).
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