Abstract
Ni:Al laminate composites were fabricated by repeatedly cold-rolling Al and Ni foils that were stacked together with initial thicknesses of 25 and 18 μm, respectively. The rolling process consisted of multiple 50 % thickness reductions wherein the first reduction was followed by cutting, restacking, and rerolling to achieve a total of three, six or nine 50 % thickness reductions. However, some of the laminates also received a more mild series of six 20 % thickness reductions without restacking. An analysis program was written and used to quantify the distribution of layer thicknesses, bilayer thicknesses and local chemistries for the complex laminate microstructures, while also preserving positional information for the constituent layers. The resulting distributions show that while we see no clustering of very large bilayers in any of the composites, the heavily rolled laminates with only 50 % thickness reductions have a higher percentage of very large bilayers, relative to the volume mean bilayer, compared to laminates with the additional 20 % thickness reductions. This phenomenon is attributed to less uniform layer deformation and more layer pinch-off with 50 % thickness reductions compared to the more gradual 20 % thickness reductions. Differential scanning calorimetry was performed on the laminates to determine the exothermic peak temperatures and the total energy released during controlled heating. Peak temperatures correlate with the volume average bilayer thickness, while the energy release correlates with the bilayer thickness distribution. The velocity and maximum temperature of self-propagating reactions were measured for the laminates and were found to vary according to processing conditions but not according to the volume average bilayer thickness. Foils with 20 % thickness reductions have both hotter and faster reactions compared to samples with only 50 % thickness reductions. The distributions of layer thicknesses, bilayer thicknesses, and local chemistries within the laminates are used to predict the maximum temperature during reaction. The velocities of the unsteady reaction propagations, though, could not be predicted effectively, at least with current analytical models.
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