Evaluation of the Performance of Atomic Diffusion Additive Manufacturing Electrodes in Electrical Discharge Machining.

Atomic Diffusion Additive Manufacturing (ADAM) is an innovative Additive Manufacturing process that allows the manufacture of complex parts in metallic material, such as copper among others, which provides new opportunities in Rapid Tooling. This work presents the development of a copper electrode manufactured with ADAM technology for Electrical Discharge Machining (EDM) and its performance compared to a conventional electrolytic copper. Density, electrical conductivity and energy-dispersive X-ray spectroscopy were performed for an initial analysis of both ADAM and electrolytic electrodes. Previously designed EDM experiments and optimizations using genetic algorithms were carried out to establish a comparative framework for both electrodes. Subsequently, the final EDM tests were carried out to evaluate the electrode wear rate, the roughness of the workpiece and the rate of material removal for both electrodes. The EDM results show that ADAM technology enables the manufacturing of functional EDM electrodes with similar material removal rates and rough workpiece finishes to conventional electrodes, but with greater electrode wear, mainly due to internal porosity, voids and other defects observed with field emission scanning electron microscopy.

Optimization methodology for the material assignation in bioprinted scaffolds to achieve the desired stiffness over time.

The optimum scaffold for tissue engineering must guarantee the mechanical integrity in the damaged zone and ensure an appropriate stiffness to regulate the cellular function. For this to happen, scaffolds must be designed to match the stiffness of the native tissue. Moreover, the degradation rate in the case of bioresorbable materials must also be considered to fit the tissue regeneration rate. This paper presents a methodology based on design of experiments, finite element analysis, metamodels, and genetic algorithms to optimize the assignation of material in different sections of the scaffold to obtain the desired stiffness over time and comply with the constraints needed. The method applies an initial sampling focused on a modified Latin Hypercube strategy to obtain data from the simulations. These data are used in the next stages to generate the metamodels by using kriging. The predictions of the metamodels are used by the genetic algorithms to find the best estimated solutions. Different runs of the genetic algorithm drive the sampling, improving the accuracy of the surrogate models over the optimization process. Once the accuracy of the metamodels estimates is sufficient, a final genetic algorithm is applied to obtain the optimum design. This approach guarantees a low sampling effort and convergence to carry out the optimization process. The method allows the combination of discrete and continuous design variables in the optimization problem, and it can be applied both in solid and in hierarchical-based geometries.

Experimental Analysis and Simulation of Novel Technical Textile Reinforced Composite of Banana Fibre.

The use of natural fibres allows reducing environmental impact, due to their natural renewable origin and the lower energy needed for their production and processing. This work presents the mechanical characterization of a newly developed technical textile, with banana fibre treated by enzymes, comparing experimental results with numerical simulation based on the definition of the unit cell at micromechanical level. The experimental test shows that the composite with the fabric of banana fibre presents worse mechanical behaviour than the one with commercial flax fibre. The presence of wool, necessary for producing the yarn, reduces the mechanical properties of the banana textile. The numerical simulation had an acceptable error compared with the experimental results, with a global average error of 9%, showing that the predictive modelling based on the multiscale method is suitable for the design process of this kind of composite.

Production of Banana Fiber Yarns for Technical Textile Reinforced Composites.

Natural fibers have been used as an alternative to synthetic ones for their greener character; banana fibers have the advantage of coming from an agricultural residue. Fibers have been extracted by mechanical means from banana tree pseudostems, as a strategy to valorize banana crops residues. To increase the mechanical properties of the composite, technical textiles can be used as reinforcement, instead of short fibers. To do so, fibers must be spun and woven. The aim of this paper is to show the viability of using banana fibers to obtain a yarn suitable to be woven, after an enzymatic treatment, which is more environmentally friendly. Extracted long fibers are cut to 50 mm length and then immersed into an enzymatic bath for their refining. Conditions of enzymatic treatment have been optimized to produce a textile grade of banana fibers, which have then been characterized. The optimum treating conditions were found with the use of Biopectinase K (100% related to fiber weight) at 45 °C, pH 4.5 for 6 h, with bath renewal after three hours. The first spinning trials show that these fibers are suitable to be used for the production of yarns. The next step is the weaving process to obtain a technical fabric for composites production.