Speakers
Description
Cemented carbides are multiphase materials constituted by a hard ceramic phase,
commonly tungsten carbide (WC), embedded in a metallic matrix, being the mostly
used Co. The mechanical properties achieved from these two constitutive phases,
including an excellent hardness, strength, toughness, wear and abrasive resistance,
makes the WC a highly demanded material for industrial applications, such as cutting
tools, molds, mining parts or industrial nozzles, among others. However, cobalt is
considered as a Critical Raw Material (CRM) and carcinogenic-mutagenic and toxic to
reproduction material (CMR), hence new alternatives are emerging, being the most
promising one the nickel due to its similarity – in terms of mechanical, electrical and
chemical properties – and an improved corrosion resistance. Despite this situation,
cemented carbides with Ni-based metallic matrix remain unstudied, including its
mechanical behavior under stress, strongly influenced by the surrounding deformation
mechanisms between its constitutive phases.
The micropillar compression technique enables the application of a controlled stress
to a small volume of material, allowing the deformation mechanisms to be reproduced
and analyzed through the stress-strain curves. Moreover, when using an in-situ
nanoindenter, the live SEM monitoring of the micropillars during the experiment allows
the accurate correlation of the pop-in events, visible in the stress-strain curves, with
the different deformation mechanisms. Establishing the proposed correlation
becomes particularly relevant when examining its dependence on other variables,
such as testing temperature. Since different deformation mechanisms are expected to
operate under varying thermal conditions, this correlation offers a valuable means of
distinguishing between them.
As all the aforementioned procedures can be time-consuming, Machine Learning is
expected to improve the efficiency of the analysis by 1) facilitating the identification of
pop-in phenomena in the stress-strain curves, 2) facilitating the identification of
deformation mechanisms in the live SEM monitoring and 3) automating the procedure
of correlating pop-ins and deformation mechanisms. An additional area for
improvement with Machine Learning techniques could be the cross-section analysis by
means of Deep Learning, to automate the identification of deformation mechanisms
through cross-section images of the post-mortem micropillars.