Dog-bone copper specimens prepared by one-step spark plasma sintering View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-11

AUTHORS

Claire Arnaud, Charles Manière, Geoffroy Chevallier, Claude Estournès, Ronan Mainguy, Florence Lecouturier, David Mesguich, Alicia Weibel, Lise Durand, Christophe Laurent

ABSTRACT

Copper dog-bone specimens are prepared by one-step spark plasma sintering (SPS). For the same SPS cycle, the influence of the nature of the die (graphite or WC–Co) on the microstructure, microhardness, and tensile strength is investigated. All samples exhibit a high Vickers microhardness and high ultimate tensile strength. A numerical electro-thermal model is developed, based on experimental data inputs such as simultaneous temperature and electrical measurements at several key locations in the SPS stack, to evaluate the temperature and current distributions for both dies. Microstructural characterizations show that samples prepared using the WC–Co die exhibit a larger grain size, pointing out that it reached a higher temperature during the SPS cycle. This is confirmed by numerical simulations demonstrating that with the WC–Co die, the experimental sample temperature at the beginning of the dwell is higher than the experimental control temperature measured at the outer surface of the die. This difference is mostly ascribed to a high vertical thermal contact resistance and a higher current density flowing through the WC–Co punch/die interface. Indeed, simulations show that current density is maximal just outside the copper sample when using the WC–Co die, whereas by contrast, with the graphite die, current density tends to flow through the copper sample. These results are guidelines for the direct, one-step, preparation of complex-shaped samples by SPS which avoids waste and minimizes machining. More... »

PAGES

7364-7373

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10853-015-9293-5

DOI

http://dx.doi.org/10.1007/s10853-015-9293-5

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https://app.dimensions.ai/details/publication/pub.1023104769


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