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Additive manufacturing (AM) is often used to create designs inspired by topology optimization and biological structures, yielding unique cross-sectional geometries spanning across scales. However, manufacturing defects intrinsic to AM can affect material properties, limiting the applicability of a uniform material model across diverse cross-sections. To examine this phenomenon, this paper explores the influence of specimen size and layer height on the compressive modulus of polycarbonate (PC) and thermoplastic polyurethane (TPU) specimens fabricated using fused filament fabrication (FFF). Micro-computed tomography imaging and compression testing were conducted on the printed samples. The results indicate that while variations in the modulus were statistically significant due to both layer height and size of the specimen in TPU, variations in PC were only statistically significant due to layer height. The highest elastic modulus was observed at a 0.2 mm layer height for both materials across different sizes. These findings offer valuable insights into design components for FFF, emphasizing the importance of considering mechanical property variations due to feature size, especially in TPU. Furthermore, locations with a higher probability of failure are recommended to be printed closer to the print bed, especially for TPU, because of the lower void volume fraction observed near the heated print bed. 

The equine hoof wall has a complex, hierarchical structure that can inspire designs of impact-resistant materials. In this study, we utilized micro-computed tomography (micro-CT) and serial block-face scanning electron microscopy (SBF-SEM) to image the microstructure and nanostructure of the hoof wall. We quantified the morphology of tubular medullary cavities by measuring equivalent diameter, surface area, volume, and sphericity. Highresolution micro-CT revealed that tubules are partially or fully filled with tissue near the exterior surface and become progressively empty towards the inner part of the hoof wall. Thin bridges were detected within the medullary cavity, starting in the middle section of the hoof wall and increasing in density and thickness towards the inner part. Porosity was measured using three-dimensional (3D) micro-CT, two-dimensional (2D) micro-CT, and a helium pycnometer. The highest porosity was obtained using the helium pycnometer (8.07%), followed by 3D (3.47%) and 2D (2.98%) micro-CT. SBF-SEM captured the 3D structure of the hoof wall at the nanoscale, showing that the tubule wall is not solid, but has nano-sized pores, which explains the higher porosity obtained using the helium pycnometer. The results of this investigation provide morphological information on the hoof wall for the future development of hoof-inspired materials and offer a novel perspective on how various measurement methods can influence the quantification of porosity. 

Previous studies show that the properties of parts manufactured via additive manufacturing, such as selective laser melting, depend on local feature sizes like lattice wall thickness and strut diameter. Although size dependence has been studied extensively, it was not included in constitutive models for numerical simulations. In this work, flat dog-bone tensile specimens of different thicknesses were manufactured and tested under quasi-static conditions to characterize the size-dependent properties experimentally. It was observed that key mechanical properties decrease with specimen thickness. Through curve-fitting to experimental data, this work provides approximate analytical expressions for the material properties values as a function of specimen thickness, furnishing a phenomenological size-dependent constitutive model. The interpolating capability of the model is cross-validated with existing experimental data. Two numerical examples demonstrate the application of the size-dependent material model. The axial crushing of thin-walled lattices at varying wall thicknesses was simulated by the size-dependent material model and one that ignores size effects. Results show that ignoring size effects leads to overestimated peak crushing force and specific energy absorption. The two material models were also compared in the topology optimization of thin-walled structures. Results show that the size-dependent model leads to a more robust optimized design: having higher energy absorption and sustaining less material fracture. 

The equine hoof wall has a complex, hierarchical structure that can inspire designs of impactresistant materials. In this study, we utilized micro-computed tomography (µ-CT) and serial blockface scanning electron microscopy (SBF-SEM) to image the microstructure and nanostructure of the hoof wall. We quantified the morphology of tubular medullary cavities by measuring equivalent diameter, surface area, volume, and sphericity. High-resolution µ-CT revealed that tubules are partially or fully filled with tissue near the exterior surface and become progressively empty towards the inner part of the hoof wall. Thin bridges were detected within the medullary cavity, starting in the middle section of the hoof wall and increasing in density and thickness towards the inner part. Porosity was measured using three-dimensional (3D) µ-CT, twodimensional (2D) µ-CT, and a helium pycnometer, with the highest porosity obtained using the helium pycnometer (8.07%), followed by 3D (3.47%) and 2D (2.98%) µ-CT. SBF-SEM captured the 3D structure of the hoof wall at the nanoscale, showing that the tubule wall is not solid, but has nano-sized pores, which explains the higher porosity obtained using the helium pycnometer. The results of this investigation provide morphological information on the hoof wall for the future development of hoof-inspired materials and offer a novel perspective on how various measurement methods can influence the quantification of porosity. 

The goal of this paper is to design a stabilizing feedback controller of roll instability near stall. This problem becomes immensely challenging since the aileron sensitivity is vanishes and even reversed sign at stall. This challenge is overcome by employing the recently developed Lie Bracket Roll Augmentation (LIBRA) mechanism. In this mechanism,the nonlinear dynamics of the airplane near stall is exploited to achieve a rolling motion that is independent of the aileron sensitivity. Rather, it depends on the variation of the aileron sensitivity with the angle of attack which is non-zero at stall. The open loop characteristics of the LIBRA mechanism have been studied previously. The contribution of the current manuscript lies in using the LIBRA mechanism in a feedback fashion to stabilize the roll unstable dynamics near stall using a stabilization scheme based on motion planning techniques for highly oscillatory inputs. 

Mobile application (app) reviews contain valuable information for app developers. A plethora of supervised and unsupervised techniques have been proposed in the literature to synthesize useful user feedback from app reviews. However, traditional supervised classification algorithms require extensive manual effort to label ground truth data, while unsupervised text mining techniques, such as topic models, often produce suboptimal results due to the sparsity of useful information in the reviews. To overcome these limitations, in this paper, we propose a fully automatic and unsupervised approach for extracting useful information from mobile app reviews. The proposed approach is based on keyATM, a keyword-assisted approach for generating topic models. keyATM overcomes the problem of data sparsity by using seeding keywords extracted directly from the review corpus. These keywords are then used to generate meaningful domain-specific topics. Our approach is evaluated over two datasets of mobile app reviews sampled from the domains of Investing and Food Delivery apps. The results show that our approach produces significantly more coherent topics than traditional topic modeling techniques. 

Geopolymers (GPs) are emerging, low‐density ceramic materials that are simple to manufacture, with high elastic modulus and strength, albeit with low toughness. Fiber reinforcements have been used to achieve varied ductile behaviors, but little is known about the GP addition to polymeric frame structures. Thus, drawing inspiration from the nanostructure of bones, this paper investigated an interpenetrating, co‐continuous composite consisting of a GP as the stiff but brittle phase, and a 3D‐printed polymer (PA12 White) as the soft and deformable phase. The composite mechanical properties and failure modes were studied experimentally using uniaxial compression and four‐point bending tests. The co‐continuous network constrained brittle cracking within the GP and reduced strain localization in the polymer. The results showed that the composite had higher strength (56.11 ± 2.12 MPa) and elastic modulus (6.08 ± 1.37 GPa) than the 3D‐printed polymer and had higher toughness (5.98 ± 0.24 MJ/mm³) than the GP for the specific geometries examined. The shape effect study demonstrated that cubic structures had higher elastic modulus and strength but at the expense of lower toughness when compared to rectangular prism structures. The study of scale effects indicated that increasing the number of periodic unit cells while maintaining consistent bulk dimensions led to augmented strength and toughness, albeit without statistically significant alterations in elastic modulus. Thus, this paper presents an experimental realization of a novel, bio‐inspired, interpenetrating, GP–polymer composite design, offering improved strength and toughness. It also provides valuable insights into the shape and size effects on the mechanical properties of this new composite.