Microstructure and mechanical behavior of porous Ti-6Al-4V parts obtained by selective laser melting

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Rapid prototyping allows titanium porous parts with mechanical properties close to that of bone tissue to be obtained. In this article, porous parts of the Ti-6Al-4V alloy with three levels of porosity were obtained by selective laser melting with two different energy inputs. Thermal treatments were performed to determine the influence of the microstructure on the mechanical properties. The porous parts were characterized by both optical and scanning electron microscopy. The effective modulus, yield and ultimate compressive strength were determined by compressive tests. The martensitic alpha' microstructure was observed in all of the as-processed parts. The struts resulting from the processing conditions investigated were thinner than those defined by CAD models, and consequently, larger pores and a higher experimental porosity were achieved. The use of the high-energy input parameters produced parts with higher oxygen and nitrogen content, their struts that were even thinner and contained a homogeneous porosity distribution. Greater mechanical properties for a given relative density were obtained using the high-energy input parameters. The as-quenched martensitic parts showed yield and ultimate compressive strengths similar to the as-processed parts, and these were greater than those observed for the fully annealed samples that had the lamellar microstructure of the equilibrium alpha+beta phases. The effective modulus was not significantly influenced by the thermal treatments. A comparison between these results and those of porous parts with similar geometry obtained by selective electron beam melting shows that the use of a laser allows parts with higher mechanical properties for a given relative density to be obtained. (C) 2013 Elsevier Ltd. All rights reserved.2698108Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Numerical simulation of localized cure of thermosensitive resin during thermo stereolithography process (TSTL)

In this work, to analyze a type of rapid prototyping technique, a numerical model was developed that was able to simulate the heat transfer at thermosensitive polymeric material during cure by laser irradiation. The analysis was carried out as a transient thermal problem using the general-purpose finite element software ANSYS. The technique analyzed was thermal stereolithography, which uses a CO2 laser beam to cure (solidify) thermosensitive liquid resins in a selective way to produce three-dimensional parts. In this numerical analysis, the temperature distribution at thermoset material heated by a laser irradiation and its thermal properties are investigated. This resin is a high-viscosity sample composed of epoxy resin, diethylene-triamine, and silica powder, which become highly crosslinked when irradiated by infrared laser. The localized curing becomes critical when the amount of silica and laser parameters are not appropriate. Bearing this in mind, this work intends, by applying the numerical method developed, to analyze the thermal behavior of resins in function of amount of silica and the laser radiation conditions, so that it is possible to have a knowledge on these variables so as to achieve a product with the required specifications. (c) 2006 Wiley Periodicals, Inc.10232777278

Infrared laser stereolithography: prototype construction using special combination of compounds and laser parameters in localised curing process

Infrared Laser Stereolithography (ILS) is a novel and cost-effective method to produce three-dimensional (3D) plastic objects. An infrared laser beam is exploited to achieve localised curing with a thermosensitive resin containing a curing agent and a filling material. Physical and chemical models describing the localised curing process in ILS are presented. Differential scanning calorimetry (DSC) is used to characterise the curing process and to evaluate the curing rate as a function of temperature and activation energy. A mathematical simulation model, using the finite element method software Ansys, is applied to predict cure profiles of the resin Lis a function of laser radiation conditions, showing good agreement with experimental results. This novel stereolithographic process can provide 3D solid Structures with good spatial resolution and no significant shrinkage. The stoichiometric amount and type of silica is found to be critical to confine the Curing process to a localised volume.O TEXTO COMPLETO DESTE ARTIGO, ESTARÁ DISPONÍVEL À PARTIR DE FEVEREIRO DE 2015.21424125

Automated computer-aided design of cranial implants using a deep volumetric convolutional denoising autoencoder

Computer-aided Design (CAD) software enables the design of patient-specific cranial implants, but it often requires of a lot of manual user-interactions. This paper proposes a Deep Learning (DL) approach towards the automated CAD of cranial implants, allowing the design process to be less user-dependent and even less time-consuming. The problem of reconstructing a cranial defect, which is essentially filling in a region in a skull, was posed as a 3D shape completion task and, to solve it, a Volumetric Convolutional Denoising Autoencoder was implemented using the open-source DL framework PyTorch. The autoencoder was trained on 3D skull models obtained by processing an open-access dataset of Magnetic Resonance Imaging brain scans. The 3D skull models were represented as binary voxel occupancy grids and experiments were carried out for different voxel resolutions. For each experiment, the autoencoder was evaluated in terms of quantitative and qualitative 3D shape completion performance. The obtained results showed that the implemented Deep Neural Network is able to perform shape completion on 3D models of defected skulls, allowing for an efficient and automatic reconstruction of cranial defects.This work was supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013, CAMed (COMET K-Project 871132) which is funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) and the Austrian Federal Ministry for Digital and Economic Affairs (BMDW) and the Styrian Business Promotion Agency (SFG), the Austrian Science Fund (FWF) KLI 678-B31 and the Erasmus+ Programme. We gratefully acknowledge the support of the NVIDIA Corporation with their donation of a Quadro P6000 board that was used in this research