La gran parte de productos de consumo contienen partes realizadas a través del proceso de inyección de termoplásticos, esto constata la importancia de este proceso de conformado con respecto a otros procesos de transformación de plástico. La minimización de los costes para ser más competitivos así como la eliminación o reducción de defectos en las piezas inyectadas, han sido los motivos principales para controlar el proceso a través de la optimización de las variables que entran en juego en este proceso, es por ello que se han realizado numerosos estudios referentes a obtener las relaciones existentes entre las variables del proceso y los aspectos de rentabilidad, estética y defectología de las piezas inyectadas. Modelizar dichas relaciones a través de algoritmos matemáticos con el fin de optimizar los resultados obtenidos y predecir el estado final de las piezas inyectadas han sido los objetivos de la mayoría de estudios. Uno de los efectos intrínsecos a la inyección es la deformación de la pieza, esta deformación tiene lugar debido a distintos factores que intervienen en el diseño del proceso en su conjunto, diferencias en la contracción, diferencias en la refrigeración, las esquinas de la pieza, la orientación molecular, etc son elementos condicionantes de la deformación que se han estudiado en infinidad de artículos, en esta divulgación científica se estudiará la deformación bajo aspectos dimensionales de la pieza con la finalidad de intentar descubrir y optimizar las condiciones de entrada que en este caso serían las dimensiones de la pieza a través de la observación y modelización de las variables de salida que seria la deformación. Y la pregunta que nos realizamos es ¿Cómo varían las deformaciones modificando las dimensiones de la pieza? ¿Cuáles son las dimensiones de la pieza a estudio que minimizan los efectos negativos de la deformación? ¿se puede predecir la deformación que obtendremos en una pieza solo con las dimensiones de una pieza? A todas estas preguntas intentamos dar respuesta en el estudio siguiente. / Gámez Martínez, JL. (2014). ANÁLISIS DE LA DEFORMACIÓN EN LA INYECCIÓN DE TERMOPLÁSTICOS BAJO VARIABLES DE FORMA DE LA PIEZA MEDIANTE RED NEURONAL Y SUPERFICIES RESPUESTA [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39350 / TESIS

<p>Cilj ovog rada je ispitivanje mogućnosti primene koncepta neuronskih mreža i postupka odzivne povr&scaron;ine za modelovanje cross-flow mikrofiltracije suspenzija kvasca. Drugi cilj je bio ispitivanje pobolj&scaron;anja procesa primenom Kenics statičkog me&scaron;ača kao promotora turbulencije. Primena statičkog me&scaron;ača ispitana je i sa energetskog stanovi&scaron;ta, a ne samo sa aspekta povećanja fluksa permeata. Svi eksperimenti izvedeni su u uslovima recirkulacije i koncentrisanja napojne suspenzije.</p><p>Dobijeni rezultati ukazuju da se pobolj&scaron;anje mikrofiltracije može se ostvariti primenom statičkog me&scaron;ača bez primene dodatne opreme. Tokom eksperimentalnog rada porast fluksa iznosio je između 89,32% i 258,86% u uslovima recirkulacije napojne suspenzije u zavisnosti od odabranih eksperimentalnih uslova, dok je u uslovima koncentrisanja napojne suspenzije porast fluksa imao vrednosti od 100% do 540% u istom eksperimentalnom opsegu.</p><p>Koncept neuronskih mreža daje veoma dobre rezultate fitovanja posmatranih odziva.<br />Pored primene ovog koncepta ispitana je i mogućnost procene uticaja pojedinih<br />eksperimentalnih promenljivih na odzive primenom jednačine Garsona i metode jačine sinapsi koje povezuju neurone. Rezulati ovog ispitivanja u saglasnosti su sa regresionom analizom.</p><p>Za detaljniju analizu uticaja eksperimentalnih promenljivih na posmatrane odzive primenjen je postupak odzivne povr&scaron;ine funkcije. Prvi korak u ovom segmentu istraživanja bio je određivanje uticaja srednjeg prečnika pora membrane na proces mikrofiltracije. Najbolji rezultati dobijeni su za membranu srednjeg prečnika 200 nm, po&scaron;to kod većih prečnika pora dolazi do izraženijeg unutra&scaron;njeg prljanja koje rezultuje manjim vrednostima fluksa permeata tokom proces mikroflitracije.</p><p>Dalja istraživanja usmerena su na ispitivanje uticaja pojedinih eksperimentalnih promenljivih ali i njihovih interakcija za odabranu membranu (srednji prečnik pora 200 nm). Rezultati fitovanja eksperimentalnih podataka dobijeni za jednu membranu bolji su u poređenju sa rezultatima kada su fitovani eksperimentalni rezultati za sve tri kori&scaron;tene membrane. Sa energetske tačke gledi&scaron;ta primećeno je da je najbolje raditi u umerenom opsegu protoka napojne suspenzije. Kao kranji cilj primene postupka odzivne povr&scaron;ine urađena je optimizacija vrednosti eksperimentalnih promenljivih, primenom postupka željene funkcije. Optimalni uslovi rada dobijeni u uslovima recirkulacije napojene suspenzije su transmembranski pritisak 0,2 bara, koncentracija napojne suspenzije 7,54 g/l i protok 108,52 l/h za maksimalne vrednosti specifične redukcije potro&scaron;nje energije. Sa sruge strane u uslovima koncentrisanja napojne suspenzije eksperimentalne promenljive imale su vrednosti transmembranski pritisak 1 bar, koncentracija napojne suspenzije 7,50 g/l i protok 176 l/h za maksimalne vrednosti specifične redukcije potro&scaron;nje energije.</p> / <p>The aim of this work was to investigate<br />possibilities of applying neural network and<br />response surface methodology for modeling crossflow<br />microfiltration of yeast suspensions. Another<br />aim was to investigate the improvement of process<br />using Kenics static mixer as turbulence promoter.<br />Experimental work was performed on 200, 450 and<br />800 nm tubular ceramic membranes. The use of<br />static mixer was also examined from an energetic<br />point of view not only its influence on permeate<br />flux. All experiments were done in recirculation and<br />concentration mode.<br />The results clearly show that the<br />improvement of cross-flow microfiltration of yeast<br />suspensions performances can be done with static<br />mixer without any additional equipment. In<br />experimental work, flux increase had values<br />between 89.32% and 258.86% for recirculation of<br />feed suspension depending on experimental values<br />of selected variables while in concentration mode<br />this improvement was in range between 100% and<br />540% for the same range of experimental variables.<br />Neural networks had excellent predictive<br />capabilities for this kind of process. Besides<br />examination of predictive capabilities of neural<br />networks influence of each variable was examined<br />by applying Garson equation and connection<br />weights method. Results of this analysis were in<br />fairly good agreement with regression analysis.<br />For more detailed analysis of variables influence on<br />the selected responses response surface<br />methodology was implemented. First step was to<br />investigate the influence of membrane pore size on<br />the process of microfiltration. The results suggested<br />that the best way to conduct microfiltration of yeast<br />suspensions is by using the membrane with mean<br />pore size of 200 nm, because bigger mean pore size<br />can lead to more prominent internal fouling that<br />causes smaller flux values.<br />Further investigations of microfiltration<br />process were done in order to investigate influences<br />of variables as well as their interactions and it was<br />done for the membrane with pore size of 200 nm.<br />Results for this membrane considering regression<br />analysis were considerably better compared with<br />results obtained for modeling all three membranes.<br />From the energetic point of view it was concluded<br />that it is optimal to use moderate feed flows to<br />achieve best results with implementation of static<br />mixer.<br />As the final goal of response surface<br />methodology optimization of process variables was<br />done by applying desirability function approach.<br />Optimal values of process variables for<br />recirculation of feed suspension were<br />trasmembrane pressure 0.2 bar, concentration 7.54<br />g/l and feed flow 108.52 l/h for maximal values of<br />specific energy reduction. On the other side for<br />concentration of feed suspension these variables<br />had values of 1 bar, 7.50 g/l and 176 l/h</p>

Joint probability methods for assessing storm surge flood risk involve the use of a collection of hydrodynamic storm simulations to fit a response surface model describing the functional relationship between storm surge and storm parameters like central pressure deficit and the radius of maximum wind speed. However, in areas with a sufficiently low probability of flooding, few storms in the simulated storm suite may produce surge, with most storms leaving the location dry with zero flooding. Analysts could treat these zero-depth, “non-wetting” storms as either truncated or censored data. If non-wetting storms are excluded from the training set used to fit the storm surge response surface, the resulting suite of wetting storms may have too few observations to produce a good fit; in the worst case, the model may no longer be identifiable. If non-wetting storms are censored using a constant value, this could skew the response surface fit. The problem is that non-wetting storms are indistinguishable, but some storms may have been closer to wetting than others for a given location. To address these issues, this thesis proposes the concept of a negative surge, or “pseudo-surge”, value with the intent to describe how close a storm came to causing surge at a location. Optimal pseudo-surge values are determined by their ability to improve the predictive performance of the response surface via minimization of a modified least squares error function. We compare flood depth exceedance estimates generated with and without pseudo-surge to determine the value of perfect information. Though not uniformly reducing flood depth exceedance estimate bias, pseudo-surge values do make improvements for some regions where <40% of simulated storms produced wetting. Furthermore, pseudo-surge values show potential to replace a post-processing heuristic implemented in the state-of-the-art response surface methodology that corrects flood depth exceedance estimates for locations where very few storms cause wetting.

Parmi les différents procédés d’élaboration de minigranules, le procédé d’extrusion-sphéronisation présente de nombreux avantages, puisqu’il permet notamment d’élaborer des minigranules fortement chargées en principe actif et d’éviter l’emploi de solvants organiques. Ce travail a pour objectif de comparer les performances de plusieurs systèmes d’extrusion à vis, de l’échelle du laboratoire jusqu’à la transposition à l’échelle industrielle. Pour cela, des plans d’expériences ont été construits afin d’identifier les variables critiques et de sélectionner l’extrudeur le plus favorable selon différentes approches spécifiques à cette étude. En effet, le système d’extrusion idéal est défini dans ce travail comme celui donnant les meilleurs résultats en termes de productivité et de caractéristiques des minigranules (« qualité »), entraînant le moins d’impact sur le produit après transposition d’échelle (« transposabilité »), montrant le moins d’influence sur le produit lorsque la formule utilisée change (« robustesse »), et permettant d’ajuster ou d’améliorer la qualité des minigranules lorsque les conditions opératoires varient (« flexibilité »). Quelle que soit l’approche étudiée, les résultats ont permis de mettre en évidence l’influence de paramètres critiques et de leurs interactions sur les différentes réponses et ont montré des différences entre les différents systèmes d’extrusion. L’étude à l’échelle du laboratoire a permis de comparer les extrudeurs radial, dôme et frontal et a mis en évidence l’intérêt des systèmes frontal et dôme en termes de qualité des minigranules, et du système radial en termes de robustesse et de flexibilité du procédé. L’étude à l’échelle industrielle a permis de comparer les extrudeurs radial et frontal, et a permis d’identifier l’extrudeur frontal comme étant le plus favorable en termes de qualité des minigranules, de robustesse, de flexibilité et de transposabilité. Les conclusions observées à l’échelle industrielle sont donc différentes de celles considérées à l’échelle du laboratoire, pour l’étude comparative des différents systèmes. Cela confirme l’importance de tester les systèmes à échelle industrielle avant l’acquisition d’un équipement. / Among the various methods of developing minigranules, extrusion-spheronization has many advantages, particularly since it allows to develop minigranules highly charged with active pharmaceutical ingredient and to avoid the use of organic solvents. This work aims to compare the performance of several extrusion screws systems, from the lab to the scale-up at industrial scale. Designs of experiments were built to identify critical variables and compare the extruder in terms of different approaches specific to this study. As a matter of fact, the ideal extrusion system is defined in this work as the one which gives the best results in terms of productivity and pellets characteristics (“quality”), the one which shows less impact on the product after scaling-up (“scalability”), the one which shows the less influence on these same properties when the formula used changes (“robustness”), and the one which allows the possibility to adjust or improve pellets properties with operating variables (“flexibility”). Whatever the approach studied, the results allowed to highlight the influence of critical parameters and their interactions on the different responses and showed differences between the different extrusion systems. The study at lab scale compared radial, dome and axial extruders and underlined the interest of axial and dome systems in terms of pellets quality, and radial system in terms of process robustness and flexibility. The study at industrial scale compared radial and axial systems, and identified the axial system as the most favorable in terms of pellets quality , robustness, flexibility and scalability. The conclusions observed at industrial scale are different from those observed at lab scale for the different systems comparative study. This confirms the importance to test systems at industrial scale before investing in one equipment.

Load induced top-down fatigue cracking has been recognized recently as a major distress phenomenon in asphalt pavements. This failure mode has been observed in many parts of the world, and in some regions, it was found to be more prevalent and a primary cause of pavements failure. The main factors which are identified as potential causes of top down fatigue cracking are primarily linked to age hardening, mixtures fracture resistance and unbound layers stiffness. Mechanistic Empirical analytical models, which are based on hot mix asphalt fracture mechanics (HMA-FM) and that could predict crack initiation time and propagation rate, have been developed and shown their capacity in delivering acceptable predictions. However, in these methods, the effect of age hardening and healing is not properly accounted and moreover, these models do not consider the effect of mixture morphology influence on long term pavement performance. Another drawback of these models is, as analysis tools they are not suitable to be used for pavement design purpose. The main objective of this study is to develop a reliability calibrated design framework in load resistance factor design (LRFD) format which could be implemented to design pavement sections against top down fatigue cracking. For this purpose, asphalt mixture morphology based sub-models were developed and incorporated to HMA-FM to characterize the effect of aging and degradation on fracture resistance and healing potential. These sub-models were developed empirically exploiting the observed relation that exist between mixture morphology and fracture resistance. The developed crack initiation prediction model was calibrated and validated using pavement sections that have high quality laboratory data and observed field performance history. As traffic volume was identified in having a dominant influence on predicted performance, two separate model calibration and validation studies were undertaken based on expected traffic volume. The predictions result for both model calibration and validation was found to be in an excellent agreement with the observed performance in the field. A LRFD based design framework was suggested that could be implemented to optimize pavement sections against top-down fatigue cracking. To achieve this objective, pavement sections with various design target reliabilities and functional requirements were analyzed and studied.  A simplified but efficient limit state equation was generated using a central composite design (CCD) based response surface methodology, and FORM based reliability analysis was implemented to compute reliabilities and formulate associated partial safety factors. A design example using the new partial safety factors have clearly illustrated the potential of the new method, which could be used to supplement existing design procedures. / <p>QC 20150427</p>

Finite element simulation of thermoplastic components is gaining importance as the companies aim to avoid overdesign of the components. Cost of the component can be minimized by using an adequate amount of material for its application. Life of the component, in a particular application, can be predicted as early as during its design phase with the help of computer simulations. To achieve reliable simulation results, an accurate material model which can predict the material behaviour is vital. Most material models consist of a number of material parameters that needs to be fed into them. These material parameters can be identified with the inputs from physical tests. The accuracy of the data extracted from the physical tests, however, remains the base for the aforementioned process. The report deals with the implementation of optical measurement technique such as Digital Image Correlation (DIC) in contrast with the conventional extensometers. A tensile test is conducted on a glass fibre reinforced thermoplastic specimen, according to ISO 527-2/1A, to extract the experimental data with the help of DIC technique. The material behavior is reproduced within a finite element analysis software package LS-DYNA, with the combination of elastoplastic model called *MAT_024 and stress state dependent damage and failure model called GISSMO. The tensile test is performed under quasi-static condition to rule out the strain rate dependency of the thermoplastic material. The mesh sensitivity of the damage model is taken into account with the element size regularization. The thesis concerns setting up a routine for material parameter identification of thermoplastics by full-field calibration (FFC) approach. Also, comparison of the strain field in the specimen, obtained through the newly set up routine against the regular non-FFC i.e. extensometer measurement routine. The major objective being, through the comparisons, a qualitative assessment of the two routines in terms of calibration time vs. gain in simulation accuracy. Material models obtained through both the routines are implemented in three-point and four-point bending simulations. The predicted material behaviors are evaluated against experimental tests.

Artificial Neural Network (ANN) ensemble and Response Surface Method (RSM) surrogate models were generated from Finite Element (FE) simulations to predict the overpressure load and vehicle impact response of a novel rapidly deployable protective structure. A Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used in conjunction with the surrogate models to determine structure topology input variable configurations which were suited to produce the optimal balance of minimum mass, minimum rotation angle, minimum displacement, and maximum total length of the deployable structure. The structure was designed to retract into a container, be lightweight to facilitate transportation, and be able to adapt to varying terrain slopes. This research demonstrates that, in comparison to the RSM, ANN ensembles can more accurately and efficiently be used for identifying optimal design solutions for multi-objective design problems when two surrogate models from the same method corresponding to separate FE models are used simultaneously in a NSGA-II.

Biocatalytic membrane reactors combine the highly efficient biotransformation capability of enzymes with the selective filtration performance of membrane filters. Common strategies to immobilize enzymes on polymeric membranes are based on chemical coupling reactions. Still, they are associated with drawbacks such as long reaction times, high costs, and the use of potentially toxic or hazardous reagents. In this study, a reagent-free immobilization method based on electron beam irradiation was investigated, which allows much faster, cleaner, and cheaper fabrication of enzyme membrane reactors. Two industrial lipase enzymes were coupled onto a polyvinylidene fluoride (PVDF) flat sheet membrane to create self-cleaning surfaces. The response surface methodology (RSM) in the design-of-experiments approach was applied to investigate the effects of three numerical factors on enzyme activity, yielding a maximum activity of 823 118 U m2 (enzyme concentration: 8.4 g L1, impregnation time: 5 min, irradiation dose: 80 kGy). The lipolytic membranes were used in fouling tests with olive oil (1 g L1 in 2 mM sodium dodecyl sulfate), resulting in 100% regeneration of filtration performance after 3 h of self-cleaning in an aqueous buffer (pH 8, 37 C). Reusability with three consecutive cycles demonstrates regeneration of 95%. Comprehensive membrane characterization was performed by determining enzyme kinetic parameters, permeance monitoring, X-ray photoelectron spectroscopy, FTIR spectroscopy, scanning electron microscopy, and zeta potential, as well as water contact angle measurements.

The purpose of this research is to investigate the influence of stitching architectures by using different stitching periodic patterns on the in-plane and out-of-plane mechanical properties. By using the inherent periodic architecture of these composites, their mechanical properties may be tailored for specific applications. Composite structures are extensively used in several industries such as aerospace, automotive, sports, and construction due to their many advantages, which include tailorable mechanical properties, high strength-to-weight ratios, and high specific stiffness. However, due to their low interlaminar tensile strength, composites are prone to delaminations, which can degrade the overall mechanical performance of the structure. Through-thickness stitching provides the third-direction reinforcement to enhance the interlaminar tensile and shear strengths. In this study, quasi-isotropic composite test articles were manufactured and stitched through-thickness using different chain stitch patterns. Full-field surface strain measurements were collected through the non-contact digital image correlation (DIC) technique. A design of experiments (DoE) approach was used to investigate the stitch parameters, such as stitch density (number of stitches per unit area), stitch angle (stitch seam orientation), and linear thread density (thread diameter), and their interactions on the in-plane and out-of-plane mechanical properties. Experimental results are then used to develop a statistically informed response surface model (RSM) to find optimal stitching parameters based on a maximum predicted tensile strength, tensile modulus and flexural strength.

Detab is a company in Jönköping that develops systems for feeding parts for robots and packaging processes. To control the flow of parts on their vibratory feeders, Detab uses different carpets with angled straws. The two mainly used carpets have been discontinued and must now be replaced. A newly emerged option is a contender in replacing one of the carpets. The new white carpet is completely additively manufactured. To compare the old carpet and the new carpet, data is needed to show how different settings affect the flow of the parts. Through design of experiments and response surface methods, tests can be performed in a systematic and mathematical way compare the performance of different settings. Some basic parameters were defined to begin the investigation of which design of experiments method would be used to best compare the carpets. The parameters defined where the parts that would be used in the tests, the factors that were to be adjusted during the tests, the maximum and minimum values of the selected factors and how the results would be measured. Different methods were tested, and finally optimal design was chosen and executed using the program Design-Expert. The completed tests resulted in response surfaces that were constructed using Design-Expert. These response surfaces were analyzed and several similarities and differences between the different carpets were observed. Primarily, it was found that the green carpet had more settings that resulted in a flow above 0 parts/minute. Using Design-Expert, the response surfaces could be analyzed to find the optimal settings and their expected response. These points and more were then compared with tests performed afterwards to see if the expected values matched reality. It turned out that there were some uncertainties in how well the surfaces could predict the response in comparison with reality. Based on the experiments carried out, recommendations were made on how the company should proceed to be able to assess whether the new carpet is worth using in future construction of their machines.