Investigation of the effect of nozzle design on rheological bioprinting properties using computational fluid dynamics

Bioprinting is the utilization of techniques derived from three-dimensional printing to generate complex biologicalstructures which may replace natural tissues or organs. It employs high spatial resolution depositionof different cell types, growth factors and biomaterials. Those together form bioinks, which are the bioprintinginputs, analogously to conventional inks with regard to inkjet printing. In extrusion bioprinting, continuousbioink filaments are deposited layer by layer on a surface by means of an extruder nozzle, employing thedisplacement of a piston or pneumatic pressure. If mechanical stresses applied on a cell membrane exceed acritical value, which depends on the cell type, the cell membrane may disrupt. Computational fluid dynamics(CFD) simulations of the bioink extrusion were done to evaluate shear stresses caused by the internal pressureof extruder nozzles during bioprinting. Different three-dimensional conical nozzle designs were testedby varying angles of convergence, lengths, input diameters and output diameters of the nozzles. The powerlawmodel, with constants k = 109.73 Pa·s0,154 and n = 0.154, was used to describe the expected non-Newtonian behavior of the bioink. Shear stresses and shear rates were evaluated for each nozzle design consideringdifferent pressures or velocities as boundary conditions at the nozzle entrance. The maximum wallshear stress value on each different nozzle varied between 1,038 Pa and 4,915 Pa. The results indicated whichdetails of nozzle geometry are most relevant in order to optimize bioprinting. The best conditions for bioinkrheology were also evaluated to ensure good printability and high cell viability.Keywords: bioink, bioprinting, biofabrication, 3D printing, CFD

Modelagem utilizando redes neurais artificiais para predição da percentagem de ferrita e parâmetros geométricos de cordões de solda de aços inoxidáveis austeníticos

Exportado OPUSMade available in DSpace on 2019-08-12T06:59:23Z (GMT). No. of bitstreams: 2 capa_marina_las_casas.pdf: 4760 bytes, checksum: de8b10432a2ca59fdb7707ffd397868e (MD5) marina_las_casas.pdf: 4028618 bytes, checksum: df422dba6b6d7b4c6c0b1adeaaa81c7f (MD5) Previous issue date: 6Esse trabalho investiga um modelo matemático que não exige grande recurso computacional e que define com boa precisão alguns parâmetros de saída mais importantes de uma solda de aços inoxidáveis austeníticos realizada com o processo de soldagem GMA. Os parâmetros de saída considerados mais importantes são a quantidade de ferrita e as medidas de largura, penetração e reforço do cordão de solda. Apesar de uma grande quantidade de fatores influenciarem os parâmetros de saída que se desejam encontrar, foram selecionados três fatores com maior influência para variar. São eles: a tensão, corrente e material do arame. Ou seja, esse modelo pretende avaliar o comportamento para soldas realizadas com diferentes metais de base e de adição. O modelo matemático apresentado se baseia em redes neurais artificiais e duas redes serão apresentadas. A primeira usa como dados de entrada os valores de tensão, corrente e material de adição e como dados de saída a quantidade de ferrita, largura, penetração e reforço do cordão, enquanto a segunda usa a quantidade de ferrita, largura, penetração e reforço do cordão como dado de entrada e tensão, corrente e material de adição como dados de saída. Essa segunda rede poderá ser utilizada pelo operador para programar o robô de acordo com os requisitos desejados para a solda.A model was developed based on experimental data obtained under laboratory conditions. To acquire these data we used an industrial robot that made welds with GMAW (Gas Metal Arc Welding) process. Welds were made with different values of voltage, current and filler material while all others parameters were kept constant. The following austenitic stainless steel wires were used: ER 308LSi ER, ER 309LSi and ER 312. All weld beads were performed on AISI 304 plates. The input parameters of the network are Vweld (input parameter that determines the robot welding voltage), Aweld (input parameter that determines the robot welding current) and values of Nickel and Chromium equivalent of wires calculated using the Schaeffler formula. The quantity of ferrite was analyzed by magnetic methods calibrated according to the AWS standard procedure and therefore will be adopted the term "Ferrite Number" (FN) in place of percent ferrite to identify this variable. In addition to FN, the model predicts the width, reinforcement and penetration of the weld beads