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Research Papers

Production of Open Cell Aluminum Foams by Using the Dissolution and Sintering Process (DSP)

[+] Author and Article Information
M. Barletta, S. Guarino, G. Rubino

Department of Mechanical Engineering, University of Rome Tor Vergata, Via del Politecnico, 1-00133 Rome, Italy

A. Gisario

Department of Mechanic and Aeronautic, La Sapienza University of Rome, Via Eudossiana, 18-00184 Rome, Italy

J. Manuf. Sci. Eng 131(4), 041009 (Jul 13, 2009) (10 pages) doi:10.1115/1.3159044 History: Received May 07, 2008; Revised December 07, 2008; Published July 13, 2009

The manufacture of open cell metal foams by dissolution and sintering process (DSP) is the matter of the present work. Aluminum foams were produced by mixing together carbamide particles with different mesh sizes (i.e., space-holder) and very fine aluminum powders. Attention was first paid at understanding the leading phenomena of the different stages the manufacturing process gets through: Compaction of the main constituents, space-holder dissolution, and aluminum powders sintering. Then, experimental tests were performed to analyze the influence of several process parameters, namely, carbamide grain size, carbamide wt%, compaction pressure, and compaction speed on the overall mechanical performance of the aluminum foams. Meaningfulness of each operational parameter was assessed by analysis of variance. Metal foams were found to be particularly sensitive to changes in compaction pressure, exhibiting their best performances for values not higher than 400 MPa. Neural network solutions were used to model the DSP. Radial basis function (RBF) neural network trained with back propagation algorithm was found to be the fittest model. Genetic algorithm (GA) was developed to improve the capability of the RBF network in modeling the available experimental data, leading to very low overall errors. Accordingly, RBF network with GA forms the basis for the development of an accurate and versatile prediction model of the DSP, hence becoming a useful support tool for the purposes of process automation and control.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 4

Main effects plot for energy E(MS12=1680 μm, MS16=1190 μm, MS20=841 μm)

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Figure 5

Main effects plot for elastic-plastic deformation ΔL(MS12=1680 μm, MS16=1190 μm, MS20=841 μm)

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Figure 6

Residuals plot for energy E

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Figure 7

Residuals plot for deformation ΔL

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Figure 1

Typical output of the compression tests

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Figure 2

FE-SEMs of an open cell aluminum foam: (a) compacted aluminum powders with carbamide particles, (b) precursor after the dissolution process, (c) carbamide residuals and oxidation inside the cell after the dissolution process, (d) detail of the compacted powders after the dissolution process, (e) detail of the open cell structure at an intermediate step of the sintering process, and (f) detail of the open cell structure at an advanced stage of the sintering process. Experimental conditions: carbamide wt %=50, mesh size=16, compaction pressure=400, and compaction speed=10 mm/min.

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Figure 3

EDXS inside the cell of the foam

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Figure 8

3D maps of the process

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Figure 9

Mean square error versus PEs number hidden layer

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Figure 10

Mean square error versus epoch number for the experimental output ΔL

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Figure 11

Mean square error versus epoch number for the experimental output E

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Figure 12

Neural network results for the experimental output ΔL

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Figure 13

Neural network results for the experimental output E

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