Studying grain boundary regions in polycrystalline materials using spherical nano-indentation and orientation imaging microscopy

In this article, we report on the application of our spherical nanoindentation data analysis protocols to study the mechanical response of grain boundary regions in as-cast and 30% deformed polycrystalline Fe-3%Si steel. In particular, we demonstrate that it is possible to investigate the role of grain boundaries in the mechanical deformation of polycrystalline samples by systematically studying the changes in the indentation stress-strain curves as a function of the distance from the grain boundary. Such datasets, when combined with the local crystal lattice orientation information obtained using orientation imaging microscopy, open new avenues for characterizing the mechanical behavior of grain boundaries based on their misorientation angle, dislocation density content near the boundary, and their propensity for dislocation source/sink behavio

Probing nanoscale damage gradients in irradiated materials with spherical nanoindentation

We discuss applications of spherical nanoindentation stress-strain curves in characterizing the local mechanical behavior of materials with modified surfaces. Using ion-irradiation on tungsten as a specific example, we show that a simple variation of the indenter size (radius) can identify the depth of the radiation-induced-damage zone, as well as quantify the behavior of the damaged zone itself. Using corresponding local structure information from electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) we look at (a) the elastic response, elasto-plastic transition, and onset of plasticity in ion-irradiated tungsten under indentation, and compare their relative mechanical behavior to the unirradiated state, (b) correlating these changes to the different grain orientations in tungsten as a function of (c) irradiation from different sources (such as He, W, and He+W)

AI driven identification and parameter adjustment of self-supporting directwrite features

Direct-ink-write provides the capability to produce self-supporting “spanning” features however, the range of print parameters that lead to an acceptable spanning geometry vary with the geometry of the gap to span (i.e. width, height) and material. To analyze spanning segments, an image processing routine is developed and applied to a set of training samples to obtain a set of standardized image representation of the deviation from the ideal span. This standardized representation allows for classification of any linear spanning segment regardless of gap geometry or filament thickness. Please click Additional Files below to see the full abstract

Viscoelasticity and high buckling stress of dense carbon nanotube brushes

We report on the mechanical behavior of a dense brush of small-diameter (1–3 nm) non-catalytic multiwall (2–4 walls) carbon nanotubes (CNTs), with ~10 times higher density than CNT brushes produced by other methods. Under compression with spherical indenters of different radii, these highly dense CNT brushes exhibit a higher modulus (~17–20 GPa) and orders of magnitude higher resistance to buckling than vapor phase deposited CNT brushes or carbon walls. We also demonstrate the viscoelastic behavior, caused by the increased influence of the van der Waals’ forces in these highly dense CNT brushes, showing their promise for energy-absorbing coatings

Imparting machine intelligence into direct ink write manufacturing

While digital manufacturing methods such as computer numerical control machining and additive manufacturing have enabled the creation of small lots of components with various complex shapes and materials. Understated, is the degree of individual process engineering and expertise required to tune material behavior, processing conditions to achieve expected properties. Current robotic manufacturing control frameworks lack the sensing and autonomy to effectively perceive and decide a course of action in response to these dynamic manufacturing environments. As a result, many commercial platforms limit user control over materials to ensure repeatability at the cost of agility. This paradigm fundamentally prevents the maturation of processes like direct ink write (DIW) additive manufacturing, which has been used to 3D print tissue scaffolds, ceramics, metals, magnets, and free-form structures.[1-5] In DIW additive manufacturing, both the materials behavior and desired structure are constantly changing, but the machine itself is rigid and never “learns” from past experiences. In general, only the user learns, thereby creating experienced “super users”. Using DIW as an example, we will present how materials and printed device development spurred the push to address the gap between robot and human experience by combining image classification, adaptive feedback, and analytical methods. A generalizable image classification method was developed to characterize the spanning behavior of a thixotropic fluid printed across 2- and 3-D gaps. The automated classification informed how to adapt the tool path and subsequently predict printing conditions for log-pile structures. By harvesting the relevant data and outcomes with user context, we seek to build an open knowledge community to enable more task-agnostic direct ink write manufacturing. Please click Additional Files below to see the full abstract

Kinking nonlinear elastic solids, nanoindentations, and geology

Physical Review Letters, 92(25): pp. 2555081-4.The physical mechanism responsible for nonlinear elastic, hysteretic, and discrete memory response of nonlinear mesoscopic elastic solids has to date not been identified.We show, by nanoindenting mica single crystals, that this response is most likely due to the formation of dissipative and fully reversible, dislocation-based kink bands. We further claim that solids with high c=a ratios, which per force are plastically anisotropic, should deform by kinking, provided they do not twin. These kinking nonlinear elastic solids include layered ternary carbides, nitrides, oxides, and semiconductors, graphite, and the layered phases, such as mica, present in nonlinear mesoscopic elastic solids

Computationally efficient predictions of crystal plasticity based forming limit diagrams using a spectral database

The present investigation focuses on the development of a fast and robust numerical tool for the prediction of the forming limit diagrams (FLDs) for thin polycrystalline metal sheets using a Taylor-type (full constraints) crystal plasticity model. The incipience of localized necking is numerically determined by the well-known Marciniak–Kuczynski model. The crystal plasticity constitutive equations, on which these computations are based, are known to be highly nonlinear, thus involving computationally very expensive solutions. This presents a major impediment to the wider adoption of crystal plasticity theories in the computation of FLDs. In this work, this limitation is addressed by using a recently developed spectral database approach based on discrete Fourier transforms (DFTs). Significant improvements were made to the prior approach and a new database was created to address this challenge successfully. These extensions are detailed in the present paper. It is shown that the use of the database allows a significant reduction in the computational cost involved in crystal plasticity based FLD predictions (a reduction of about 96% in terms of CPU time)