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Abstracts Quantitatively defining the relationship between laser powder bed fusion (LPBF) process parameters and the resultant microstructures for LPBF fabricated alloys is one of main research challenges. To date, achieving the desired microstructures and mechanical properties for LPBF alloys is generally done by time-consuming and costly trial-and-error experiments that are guided by human experience. Here, we develop an approach whereby an image-driven conditional generative adversarial network (cGAN) machine learning model is used to reconstruct and quantitatively predict the key microstructural features (e.g., the morphology of martensite and the size of primary and secondary martensite) for LPBF fabricated Ti-6Al-4V. The results demonstrate that the developed image-driven machine learning model can effectively and efficiently reconstruct micrographs of the microstructures within the training dataset and predict the microstructural features beyond the training dataset fabricated by different LPBF parameters (i.e., laser power and laser scan speed). This study opens an opportunity to establish and quantify the relationship between processing parameters and microstructure in LPBF Ti-6Al-4V using a GAN machine learning-based model, which can be readily extended to other metal alloy systems, thus offering great potential in applications related to process optimisation, material design, and microstructure control in the additive manufacturing field.

The influence of strain rate across three orders of magnitude (1.70 × 10−5/s to 1.43 × 10−2/s) along with the effect of the plastic strain accumulation (up to 10%) on the serrated plastic flow were investigated in the nickel-chromium (Ni-Cr) solid solution alloy Nimonic 75 by performing constant-strain-rate tension testing at 600 °C. As the strain rate decreased, the critical strain for the onset of serrations transitioned from normal behavior to inverse behavior. The serrated flow was characterized as Type A+B serration at high strain rate (1.43 × 10−2/s). In the intermediate strain-rate regime (1.43 × 10−3/s and 1.45 × 10−4/s), Type B serrations were observed and followed by a transformation to Type C+B serrations. At the low strain rate (1.70 × 10−5/s), the plastic flow immediately displayed Type C serrations, which later evolved into Type C+B serrations. Regardless of the strain rate, plastic strain, or dislocation density, a critical dislocation velocity falling in the range of 1.2 × 10−6 – 2.2 × 10−6 m/s was identified to signify the onset of Type C serration, whereby the mobile dislocations break free from the solute cloud for short bursts of deformation. Finally, a novel model by solute rearrangement across dislocation cores was used to understand how the critical dislocation velocity is quantitatively determined by the rate at which solute atoms are able to hop across the glide plane as a partial dislocation core moves through the lattice.

A method for modeling cubically anisotropic elasticity within the discrete element method is presented. The discrete element method (DEM) is an approach originally intended for modeling granular materials (sand, soil, and powders); however, recent developments have usefully extended it to model stochastic mechanical processes in monolithic solids which, to date, have been assumed to be elastically isotropic. The method presented here for efficiently capturing cubic elasticity in DEM is an important prerequisite for further extending DEM to capture the influence of elastic anisotropy on the mechanical response of polycrystals, composites, etc. The system demonstrated here uses a directionally assigned stiffness in the bonds between adjacent elements and includes separate schemes for achieving anisotropy with Zener ratios greater and smaller than one. The model framework is presented along with an analysis of the accessible space of elastic properties that can be modeled and an artificial neural network interpolation scheme for mapping input parameters to model elastic behavior.

A method for modeling cubically anisotropic elasticity within the discrete element method is presented. The discrete element method (DEM) is an approach originally intended for modeling granular materials (sand, soil, and powders); however, recent developments have usefully extended it to model stochastic mechanical processes in monolithic solids which, to date, have been assumed to be elastically isotropic. The method presented here for efficiently capturing cubic elasticity in DEM is an important prerequisite for further extending DEM to capture the influence of elastic anisotropy on the mechanical response of polycrystals, composites, etc. The system demonstrated here uses a directionally assigned stiffness in the bonds between adjacent elements and includes separate schemes for achieving anisotropy with Zener ratios greater and smaller than one. The model framework is presented along with an analysis of the accessible space of elastic properties that can be modeled and an artificial neural network interpolation scheme for mapping input parameters to model elastic behavior.

Abstract Objectives Diet‐induced metabolic dysfunction such as type 2 diabetes mellitus increases the risk of implant failure in both dental and orthopaedic settings. We hypothesised that a diet high in fat and fructose would adversely affect peri‐implant bone structure and function including osseointegration. Materials and methods Thirty female Sprague‐Dawley rats were divided into three groups (n = 10), control group (normal chow) and two intervention groups on a high‐fat (60%), high‐fructose (20%; HFHF) diet. Titanium implants were placed in the proximal tibial metaphysis in all groups either before commencing the diet (dHFHF group) or 6 weeks after commencing the diet (HFHF group) and observed for an 8‐week healing period. Fasting blood glucose levels (fBGLs) were measured weekly. Structural and functional features of the peri‐implant bone, including bone‐to‐implant contact (BIC), were analysed post euthanasia using microcomputed tomography, pull‐out tests, and dynamic histomorphometry. Results The fBGLs were unchanged across all groups. Peri‐implant trabecular bone volume was reduced in the HFHF group compared with controls (p = .02). Percentage BIC was reduced in both HFHF group (25.42 ± 3.61) and dHFHF group (28.56 ± 4.07) compared with the control group (43.26 ± 3.58, p < .05) and reflected the lower pull‐out loads required in those groups. Osteoblast activity was reduced in both intervention groups compared with the control group (p < .05). Conclusion The HFHF diet compromised osseointegration regardless of whether the implant was placed before or after the onset of the diet and, despite the absence of elevated fBGLs, confirming that changes in bone cell function affected both the initiation and maintenance of osseointegration independent of blood glucose levels.

Abstract Background and Aims The purpose of this investigation was to test the hypothesis that a novel adhesive retention suture device (ARSD) can increase perfusion at elliptical wound closures by distributing stress away from the suture site. Methods Stress in the skin around a suture both with and without support from an ARSD was evaluated using a finite element model. A single‐center, randomized split‐scar comparison trial using laser speckle contrast analysis was used to quantify the perfusion at elliptical wound closures in human patients both with and without an ARSD. Results The finite element model revealed that the ARSD promoted load transfer to the skin over a larger area, thus reducing the local stress and deformation in the skin around the suture site. Results from the split‐scar study showed a mean improvement of 25% perfusion units with the ARSD, and the improvement was statistically significant (p = 0.002). Conclusion The reduction in local stress and enhanced perfusion around the suture site reveals the potential benefit of using an ARSD to enable more efficient healing by avoiding complications associated with both low perfusion and skin tearing, such as dehiscence, infection, and cheese wiring.

Dynamic cell‐culture materials that can change mechanical properties in response to extrinsic stimuli are emerging as promising tools for cell and tissue engineering research. However, most of these techniques involve a one‐way stiffening or softening through changes in the materials chemistry, which does not allow reversibility. Here, the incorporation of superparamagnetic iron‐oxide nanoparticles within poly(ethylene glycol) hydrogels as dynamic cell culture materials is demonstrated. Using simple permanent magnets and adipose‐derived stem cells, a near twofold increase in cell spread area and an accompanying 20% enrichment in cells expressing alpha‐smooth muscle actin is seen. This platform provides a means to study relationships between dynamic stiffening and cell behavior, using permanent magnets and clinically viable composite materials, with scope for use as a tool to enrich the myofibroblast population in stromal cells.

The dentin-enamel junction (DEJ), which is the interface between the dentin and outer enamel coating in teeth, is known for its unique biomechanical properties that provide a crack-arrest barrier for flaws formed in the brittle enamel. In this work, we re-examine how cracks propagate in the proximity of the DEJ, and specifically quantify, using interfacial fracture mechanics, the fracture toughness of the DEJ region. Additionally, we show that the vital function of the DEJ, in preventing cracks formed in enamel from traversing the interface and causing catastrophic tooth fractures, is not necessarily associated with the crack-arrest capabilities of the DEJ itself, but rather with the development of crack-tip shielding, primarily from uncracked-ligament bridging, in the mantle dentin adjacent to the DEJ. Measurements of the toughness of the DEJ region give estimates of Gc ~; 115 J/m2, i.e., ~;5 to 10 times higher than enamel and ~;75 percent of that of dentin.

Fracture and fatigue-crack growth properties are examined for a series of Mo-Mo3Si-Mo5SiB2 containing alloys, which utilize a continuous a-Mo matrix to achieve unprecedented room-temperature fracture resistance (>20 MPaAm). Mechanistically, these properties are explained in terms of toughening by crack trapping and crack bridging by the more ductile a-Mo phase.

A series of in situ toughened, A1, B and C containing, silicon carbide ceramics (ABC-SiC) has been examined with A1 contents varying from 3 to 7 wt%. With increasing A1 additions, the grain morphology in the as-processed microstructures varied from elongated to bimodal to equiaxed, with a change in the nature of the grain-boundary film from amorphous to partially crystalline to fully crystalline.

A method for determining worst-case cyclic fatigue thresholds in grain-bridging ceramics by quantifying the role of bridging is demonstrated for a model alumina. Crack-growth properties for both long and short (< 2 mm) cracks emanating from machined notches (root radii, (rho) ~; 15 - 150 (mu)m) were investigated. When compared as a function of the applied stress-intensity range (delta K), growth rates (da/dN) were far higher and fatigue thresholds (Delta)KTH were markedly lower with short cracks, with growth being observable at the lowest driving forces for short cracks emanating from razor micronotches ((rho)is approximately equal to 15 (mu)m). For growth rates < 10-8 m/cycle, da/dN vs. (delta)K data for short cracks merged with the steady-state data for long cracks after ~;2 mm of extension.

Metal/ceramic interfaces can be found in many engineering applications including microelectronic packaging, multi-layered films, coatings, joints, and composite materials. In order to design reliable engineering systems that contain metal/ceramic interfaces, a comprehensive understanding of interfacial and near interfacial failure mechanisms is ncessary.

The structural integrity of oxide/metal interfaces is important in many applications.