Linear multiscale analysis and finite element validation of stretching and bending dominated lattice materials

The paper presents a multiscale procedure for the linear analysis of components made of lattice materials. The method allows the analysis of both pin-jointed and rigid-jointed microtruss materials with arbitrary topology of the unit cell. At the macroscopic level, the procedure enables to determine the lattice stiffness, while at the microscopic level the internal forces in the lattice elements are expressed in terms of the macroscopic strain applied to the lattice component. A numeric validation of the method is described. The procedure is completely automated and can be easily used within an optimization framework to find the optimal geometric parameters of a given lattice material. © 2011 Elsevier Ltd. All rights reserved.

Structural optimization of lattice materials

Lattice materials are characterized at the microscopic level by a regular pattern of voids confined by walls. Recent rapid prototyping techniques allow their manufacturing from a wide range of solid materials, ensuring high degrees of accuracy and limited costs. The microstructure of lattice material permits to obtain macroscopic properties and structural performance, such as very high stiffness to weight ratios, highly anisotropy, high specific energy dissipation capability and an extended elastic range, which cannot be attained by uniform materials. Among several applications, lattice materials are of special interest for the design of morphing structures, energy absorbing components and hard tissue scaffold for biomedical prostheses. Their macroscopic mechanical properties can be finely tuned by properly selecting the lattice topology and the material of the walls. Nevertheless, since the number of the design parameters involved is very high, and their correlation to the final macroscopic properties of the material is quite complex, reliable and robust multiscale mechanics analysis and design optimization tools are a necessary aid for their practical application. In this paper, the optimization of lattice materials parameters is illustrated with reference to the design of a bracket subjected to a point load. Given the geometric shape and the boundary conditions of the component, the parameters of four selected topologies have been optimized to concurrently maximize the component stiffness and minimize its mass. Copyright © 2011 by ASME.

Composite electrospun gelatin fiber-alginate gel scaffolds for mechanically robust tissue engineered cornea

A severe shortage of good quality donor cornea is now an international crisis in public health. Alternatives for donor tissue need to be urgently developed to meet the increasing demand for corneal transplantation. Hydrogels have been widely used as scaffolds for corneal tissue regeneration due to their large water content, similar to that of native tissue. However, these hydrogel scaffolds lack the fibrous structure that functions as a load-bearing component in the native tissue, resulting in poor mechanical performance. This work shows that mechanical properties of compliant hydrogels can be substantially enhanced with electrospun nanofiber reinforcement. Electrospun gelatin nanofibers were infiltrated with alginate hydrogels, yielding transparent fiber-reinforced hydrogels. Without prior crosslinking, electrospun gelatin nanofibers improved the tensile elastic modulus of the hydrogels from 78±19. kPa to 450±100. kPa. Stiffer hydrogels, with elastic modulus of 820±210. kPa, were obtained by crosslinking the gelatin fibers with carbodiimide hydrochloride in ethanol before the infiltration process, but at the expense of transparency. The developed fiber-reinforced hydrogels show great promise as mechanically robust scaffolds for corneal tissue engineering applications. © 2013 Elsevier Ltd.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.