Neural network model for predicting strain hardening and densification constants of sintered aluminium preforms

Journal of Materials Processing Technology

2008

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Section: Adoption Of Nn Approachmentioning

confidence: 99%

An analysis of formability of aluminium preforms using neural network

Journal of Materials Processing Technology

2008

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“…Statistical methods such as linear regression are limited in their ability to predict the resulting process outcomes. 18 Also, it may be possible to use a number of alternative input permutations for producing a P/M part.…”

Section: Theoretical Analysismentioning

confidence: 99%

Constitutive analysis on formability of aluminium iron composite preforms using artificial neural network

2011

Powder Metallurgy

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Cold upsetting experiments were carried out on sintered aluminium iron composite preforms in order to analyse the formability. A Neural Network (NN) model has been developed with a radial basis neural network algorithm to find the effect of particle size and iron content on formability.The training data were collected by the experimental setup in the laboratory for the sintered aluminium iron composite preforms with various preform densities, particle size, iron content and aspect ratios. The network is trained to predict the deformation characteristics such as formability index b, instantaneous strain hardening coefficients k i and n i and instantaneous density coefficients B i and C i . It is found that formability index increases rapidly at the earlier stages of deformation and followed by a gradual increase with further increase in true axial strain. It is also observed that coarse particle size and higher addition of iron content exhibits improved formability index values. In addition, instantaneous density coefficients B i and C i of the aluminium iron composite preforms were mathematically evaluated and simulated. Regression analysis has confirmed a well coincidence between predicted and experimental data with more accuracy. Hence, this approach helps to facilitate a knowledge base in order to generate advice for the designer at the earlier stages of design by forecasting and preventing the surface defects at the computer design stage itself. It also avoids the cost of prototyping or trialing.

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“…The effects of these coefficients are non-linear and are usually interrelated. Statistical methods such as linear regression methods are limited in their ability to predict the resulting process outcomes [29]. Also, it may be possible to use a number of alternative input permutations for producing a PM part.…”

Section: The Reason For Selecting Five Parameters As the Output For Pm Materialsmentioning

confidence: 99%

Neural network approach for the selection of processing parameters of aluminium—iron composite preforms during cold upsetting

Proceedings of the Institution of Mechanical Engineers, Part B:

2009