Exploiting Viscoelastic Experimental Observations and Numerical Simulations to Infer Biomimetic Artificial Tendon Fiber Designs

Designing biomimetic artificial tendons requires a thorough, data-based understanding of the tendon's inner material properties. The current work exploits viscoelastic experimental observations at the tendon fascicle scale, making use of mechanical and data analysis methods. More specifically, based on reported elastic, volumetric and relaxation fascicle scale properties, we infer most probable, mechanically compatible material attributes at the fiber scale. In particular, the work provides pairs of elastic and viscous fiber-scale moduli, which can reproduce the upper scale tendon mechanics. The computed range of values for the fiber-scale tendon viscosity attest to the substantial stress relaxation capabilities of tendons. More importantly, the reported mechanical parameters constitute a basis for the design of tendon-specific restoration materials, such as fiber-based, engineering scaffolds

Investigating the Effect of Aging on the Viscosity of Tendon Fascicles and Fibers

In the current work, we investigate the effect of aging on the viscosity of tendon subunits. To that scope, we make use of experimental relaxation curves of healthy and aged tendon fascicles and fibers, upon which we identify the viscosity parameters characterizing the time-dependent behavior of each tendon subunit. We subsequently combine the obtained results with analytical viscoelastic homogenization analysis methods to extract information on the effective viscous contribution of the embedding matrix substance at the fiber scale. The results suggest that the matrix substance plays a significant role in the relaxation process of the upper tendon subunits both for aged and healthy specimens. What is more, the viscosity coefficients computed for the fibrillar components indicate that aging leads to a viscosity reduction that is statistically significant for both fascicles and fibers. Its impact is more prominent for the lower hierarchical scale of fibers. As such, the reduced stress relaxation capability at the tendon macroscale is to be primarily attributed to the modified viscosity of its inner fibrillar subunits rather than to the matrix substance

The European Migratory Crisis and the Legal Pressure on the States

A dissertation on the States´ legal struggle to respond to the migratory pressure in compliance with human rights provisions

Enhanced Cellular Materials through Multiscale, Variable-Section Inner Designs: Mechanical Attributes and Neural Network Modeling

In the current work, the mechanical response of multiscale cellular materials with hollow variable-section inner elements is analyzed, combining experimental, numerical and machine learning techniques. At first, the effect of multiscale designs on the macroscale material attributes is quantified as a function of their inner structure. To that scope, analytical, closed-form expressions for the axial and bending inner element-scale stiffness are elaborated. The multiscale metamaterial performance is numerically probed for variable-section, multiscale honeycomb, square and re-entrant star-shaped lattice architectures. It is observed that a substantial normal, bulk and shear specific stiffness increase can be achieved, which differs depending on the upper-scale lattice pattern. Subsequently, extended mechanical datasets are created for the training of machine learning models of the metamaterial performance. Thereupon, neural network (NN) architectures and modeling parameters that can robustly capture the multiscale material response are identified. It is demonstrated that rather low-numerical-cost NN models can assess the complete set of elastic properties with substantial accuracy, providing a direct link between the underlying design parameters and the macroscale metamaterial performance. Moreover, inverse, multi-objective engineering tasks become feasible. It is shown that unified machine-learning-based representation allows for the inverse identification of the inner multiscale structural topology and base material parameters that optimally meet multiple macroscale performance objectives, coupling the NN metamaterial models with genetic algorithm-based optimization schemes

Strength and Failure of Self-Piercing Riveted Aluminum and Steel Sheet Joints: Multi-axial Experiments and Modeling

The mechanical failure of self-piercing rivet (SPR) joints connecting seven series aluminum and high strength steel sheets is investigated, both numerically and experimentally. The joint strength and failure mechanisms are characterized for a total of four distinct loading modes, including the lap-shear, cross-tension, inclined cross tension and coach peel. For the analysis of the underlying influential parameters in each loading case, joint designs with equal total sheet thickness and equal rivet head diameters are considered. The highest strength values are obtained in lap-shear loading for all joint types, while high rivet interlock joints are observed to pair with increased cross-tension strength values. Moreover, the loading mode in which the highest energy is absorbed directly relates to the joint type. Depending on the material combination to be joined, either the cross-tension or the coach-peel cases yielded the highest energy absorption. The experimental results indicate that high rivet hardness and bottom sheet strength values have a favorable impact on the coach-peel strength and the associated deformation response. For all failure modes, the lowest rivet hardness employed (H4) was proven sufficient to prevent rivet failure in the joint types employed, despite the substantial equivalent plastic strains developed in it. Furthermore, high interlocks were noted to primarily affect the lap shear failure mode, inducing significant bottom sheet damage upon fracture, with the failure response observed in all other loading cases to remain practically insensitive to the interlock magnitude

Stress-strain response of polymers made through two-photon lithography: Micro-scale experiments and neural network modeling

Photopolymerization is the governing chemical mechanism in two-photon lithography, a multi-step additive manufacturing process. Negative-tone photoresist materials are widely used in this process, enabling the fabrication of structures with nano- and micro-sized features. The present work establishes the relationship among the process parameters, the degree of polymerization, and the nonlinear stress-strain response of polymer structures obtained through two-photon polymerization. Honeycomb structures are fabricated on a direct laser writing system (Nanoscribe) making use of different laser powers for two widely applicable, commercially available resins (IP-S and IP-Dip). The structures are then tested under uniaxial compression to obtain the corresponding stress-strain curves up to 30% strain. Raman spectroscopy is used to correlate the degree of conversion achieved upon different laser exposures of the base photoresist material with the selected mechanical properties (Young's modulus, tangent modulus, deformation resistance) after polymerization. Significant differences are recorded in the observed constitutive responses. Higher degrees of conversion result in higher elastic moduli and strength at large strains. Moreover, it is found that the IP-Dip resin yields higher degrees of conversion for the same laser power compared to the IP-S resin. A neural network model is developed for each resin that predicts the stress-strain response as a function of the degree of conversion. For each material, an analytical form of the identified constitutive response is provided, furnishing basic formulas for engineering practice.ISSN:2214-860

Machine-learning based prediction of crash response of tubular structures

This paper proposes a machine learning based methodology for predicting the buckling response of tubular structures. An extensive dataset of force-time curves is generated using a calibrated finite element model within a parametric space where buckling response is highly non-linear. Based on a fully connected neural network template, the machine learning hyper-parameters are determined and the resulting model is evaluated on a separate test set, with regard to maximum and average load and energy absorption errors. This evaluation shows a non-random error distribution which can be correlated with the physical properties of the structural collapse. To validate this assumption, a similar error analysis is conducted between finite element simulations with varying geometric imperfections. Evaluation of imperfection sensitivity reveals a similar error distribution and comparison of individual curves shows that errors made by the neural network model have a physical interpretation. These results indicate that the proposed machine learning based approach is capable of predicting the crushing response with a level of accuracy comparable to the errors that would be caused by a minor change in geometric imperfection.ISSN:0734-743xISSN:1879-350

Hybrid manufacturing of AlSi10Mg metamaterials: Process, static and impact response attributes

The work investigates the microstructural, static and impact mechanical attributes of hybrid-cast aluminum AlSi10Mg metamaterials. For the analysis, different metamaterial topologies, namely BCC, IWP and gyroid-based architectures, are considered. The microstructural characteristics of hybrid-cast metamaterials are thoroughly investigated, assessing their attributes through scanning electron microscopy (SEM) and CT-scanning methods. Moreover, their static and impact attributes are experimentally characterized, quantifying elastic and post-elastic properties, while associating the performance of hybrid-cast and as-built, powder bed fusion (PBF) based metamaterials. PBF samples yield overall superior Young's moduli and higher peak stresses, though upon a brittle post-elastic response. Contrariwise, hybrid-cast metamaterials result in a ductile post-elastic, continuum-type plastification performance with a considerable energy absorption capacity that depends on the metamaterial topology, aspects both experimentally and numerically elaborated. Under dynamic impact loading, substantial peak stress and toughness enhancements are recorded for the hybrid-cast specimens. The analysis furnishes process-structure-property benchmark data on the mechanical performance of advanced, hybrid-cast metamaterial topologies for the first time. We aspire that the provided results foster novel pathways in the engineering of advanced media for a variety of base materials beyond the aluminum alloy here investigated

Higher-gradient and micro-inertia contributions on the mechanical response of composite beam structures

In the current work, we study the role of higher-order and micro-inertia contributions on the mechanical behavior of composite structures. To that scope, we compute the complete set of the effective static and dynamic properties of composite beam structures using a higher-order dynamic homogenization method which incorporates micro-inertia effects. We consider different inner composite element designs, with material constituents that are of relevance for current engineering practice. Thereupon, we compute the effective static longitudinal higher-gradient response, quantifying the relative difference with respect to the commonly employed, Cauchy-mechanics formulation. We observe that within the static analysis range, higher-order effects require high internal length values and highly non-linear strain profile distributions for non-negligible higher-order effects to appear. We subsequently analyze the longitudinal, higher-gradient eigenfrequency properties of composite structural members, accounting for the role of micro-inertia contributions. Thereupon, we derive analytical expressions that relate the composite material's effective constitutive parameters with its macroscale vibration characteristics. We provide for the first-time evidence that micro-inertia contributions can counteract the effect of second-gradient properties on the eigenfrequencies of the structure, with their relative significance to depend on the mode of interest. What is more, we show that the internal length plays a crucial role in the significance of micro-inertia contributions, with their effect to be substantial for low, rather than for high internal length values, thus for a wide range of materials used in engineering practice.ISSN:0020-722