Most of the studies in three-dimensional (3D) bioprinting have been traditionally based on printing a single bioink. Addressing the complexity of organ and tissue engineering, however, will require combining multiple building and sacrificial biomaterials and several cells types in a single biofabrication session. This is a significant challenge, and, to tackle that, we must focus on the complex relationships between the printing parameters and the print resolution. In this paper, we study the influence of the main parameters driven multi-material 3D bioprinting and we present a method to calibrate these systems and control the print resolution accurately. Firstly, poloxamer hydrogels were extruded using a desktop 3D printer modified to incorporate four microextrusion-based bioprinting (MEBB) printheads. The printed hydrogels provided us the particular range of printing parameters (mainly printing pressure, deposition speed, and nozzle z-offset) to assure the correct calibration of the multi-material 3D bioprinter. Using the printheads, we demonstrated the excellent performance of the calibrated system extruding different fluorescent bioinks. Representative multi-material structures were printed in both poloxamer and cell-laden gelatin-alginate bioinks in a single session corroborating the capabilities of our system and the calibration method. Cell viability was not significantly affected by any of the changes proposed. We conclude that our proposal has enormous potential to help with advancing in the creation of complex 3D constructs and vascular networks for tissue engineering.

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Hybrid constructs represent substantial progress in tissue engineering (TE) towards producing implants of a clinically relevant size that recapitulate the structure and multicellular complexity of the native tissue. They are created by interlacing printed scaffolds, sacrificial materials, and cell-laden hydrogels. A suitable biomaterial is a polycaprolactone (PCL); however, due to the higher viscosity of this biopolymer, three-dimensional (3D) printing of PCL is slow, so reducing PCL print times remains a challenge. We investigated parameters, such as nozzle shape and size, carriage speed, and print temperature, to find a tradeoff that speeds up the creation of hybrid constructs of controlled porosity. We performed experiments with conical, cylindrical, and cylindrical shortened nozzles and numerical simulations to infer a more comprehensive understanding of PCL flow rate. We found that conical nozzles are advised as they exhibited the highest shear rate, which increased the flow rate. When working at a low carriage speed, conical nozzles of a small diameter tended to form-flatten filaments and became highly inefficient. However, raising the carriage speed revealed shortcomings because passing specific values created filaments with a heterogeneous diameter. Small nozzles produced scaffolds with thin strands but at long building times. Using large nozzles and a high carriage speed is recommended. Overall, we demonstrated that hybrid constructs with a clinically relevant size could be much more feasible to print when reaching a tradeoff between temperature, nozzle diameter, and speed.

In this day and age, galvanised coated steel is an essential product in several key manufacturing sectors because of its anticorrosive properties. The increase in demand has led managers to improve the different phases in their production chains. Among the efforts needed to accomplish this task, process modelling can be identified as the one with the most powerful outputs in spite of its non-trivial development. In many fields, such as industrial modelling, multilayer feedforward neural networks are often proposed as universal function approximators. These supervised neural networks are commonly trained by the traditional, back-propagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted or extremely deviated samples (outliers), this training scheme may produce incorrect models, and it is well known that industrial data sets frequently contain outliers. The process modelled is a steel coil annealing furnace in a galvanising line, which shares characteristics with most of the furnaces used in galvanised lines all over the world. This paper reports the effectiveness of robust learning algorithms compared to the classical mse-based learning algorithm for the modelling of a real industry process. From this model an adequate line velocity (the velocity set point) for a coil, depending on its characteristics and the furnace condition to receive this coil (temperature set points), can be obtained. With this set point generation model the operator could set strategies to manage the line, i.e. set the order of the coil to be treated or preview the line's speed conditions for the transitory situations.

Polyoxymethylene (POM), an excellent engineering material, shows drawbacks as a three‐dimensional (3D) printing material: (a) difficulties in the adhesion of the first‐printed layer and (b) thermal contractions during printing. In this study, an atmospheric pressure air plasma treatment is applied on a polycarbonate (PC)‐printing base. The effect of the plasma treatment parameters is studied. Chemical and morphological tests are conducted. Results shows that, in general, as the plasma exposure rises, so does the degree of oxidation of the PC surface and the adhesion of POM. However, care must be taken as thermal residual stresses may reduce adhesion. Finally, an increase in adhesion of up to 45% is achieved.

El sector siderúrgico, a pesar de ser una actividad tradicional y madura, se caracteriza por realizar importantes esfuerzos en el campo de las nuevas tecnologí-as de fabricación y de mejora de calidad de sus productos. Así, por ejemplo, en el caso del acero recubierto mediante galvanizado, un producto que está experimentando una creciente demanda, en sectores como la automoción, la fabricación de electrodomés-ticos y la construcción, por sus propiedades anticorrosión, las empresas plantean una estrategia de mejora continua en cada una de las fases de que consta el proceso de galvanizado [1][2][3][4] , con el propósi-to de liderar el mercado, así como de satisfacer las cada vez mayores exigencias de los clientes.En la actualidad, un problema que puede presentarse en la fabricación de bobinas de acero galvanizado, consiste en el etiquetado incorrecto del grado de acero de una bobina. A pesar de tratarse de un problema con una ocurrencia esporádica, sus consecuencias pueden ser graves ya que, bajo estas circunstancias, una bobina es tratada como si tuviera una composición química que en realidad no posee y, dado que generalmente estas bobinas de acero galvanizado sufren posteriores transformaciones antes de D De es sa ar rr ro ol ll lo o d de e u un n c ce er rr ro oj jo o a ar rt ti if fi ic ci ia al l p pa ar ra a e el l s sk ki in n--p pa as ss s e en n u un na a l lí ín ne ea a d de e a ac ce er ro o g ga al lv va an ni iz za ad do o p po or r i in nm me er rs si ió ón n e en n c ca al li ie en nt te e ( (• •) )A. González-Marcos * , J.B. Ordieres-Meré ** , A.V. Pernía-Espinoza ** y V. Torre-Suárez *** R Re es su um me en n En este trabajo se presenta la aplicación de técnicas de minería de datos en el desarrollo de un "cerrojo artificial" para el skin-pass, que permita solucionar un problema que puede presentarse en la fabricación de bobinas de acero galvanizado: el etiquetado incorrecto del grado de acero de una bobina. Para tratar de detectar estos errores y evitar así que los clientes reciban bobinas con propiedades distintas de las esperadas, se proponen modelos, basados en redes neuronales, que predicen on-line el alargamiento de las bobinas en el skin-pass en función de las variables del proceso de fabricación y de su composición química. De esta forma, si la diferencia entre el alargamiento que estima el modelo y el medido realmente es significativa, se hace necesario sacar la bobina de la línea para someterla a análisis más exhaustivos.P Pa al la ab br ra as s c cl la av ve e Acero galvanizado. . Minería de datos. Redes neuronales. Cerrojo artificial.D De ev ve el lo op pm me en nt t o of f a an n a ar rt ti if fi ic ci ia al l l lo oc ck k f fo or r t th he e s sk ki in n--p pa as ss s s se ec ct ti io on n i in n a a h ho ot t d di ip p g ga al lv va an ni is si in ng g l li in ne e A Ab bs st tr ra ac ct tIn this paper, we present the application of data mining techniques to develop an "artificial lock" for the skin-pass in an attempt to solve a problem that can arise during the galvanising manufact...

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