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Michael Gehde - One of the best experts on this subject based on the ideXlab platform.
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creating Material Data for thermoset injection molding simulation process
Polymer Testing, 2019Co-Authors: Ngoc Tu Tran, Michael GehdeAbstract:Abstract Thermoset Material Data for reactive injection molding simulation process is found in limited sources and seldom available from Data bank of simulation tools because of complication not only in rheological and thermal properties measurement but also in writing optimization algorithm to model rheological and thermal mathematical equations. In this paper, rheological and thermal properties of thermoset injection molding compounds were successfully measured. In addition, a numerical method was developed to create Material Data of thermoset injection molding compounds, which was directly imported into a simulation tool, namely, Moldex3D to investigate its application in thermoset injection molding simulation process. Furthermore, a strong slip phenomenon on the interface between thermoset melt and wall surface which was investigated and detected during injection molding experiments was taken into account in the filling simulation process. The computation was found to be in good agreement with the experimental results, indicating that the new generated Material Data is reasonable and the influence of wall slip on the mold filling characterization of thermoset injection compounds during simulation process is not ignorable.
Ngoc Tu Tran - One of the best experts on this subject based on the ideXlab platform.
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Creating Material properties for thermoset injection molding simulation process
Universitätsverlag Chemnitz, 2020Co-Authors: Ngoc Tu TranAbstract:Um den Spritzgießprozess zu simulieren, sind korrekte Materialdaten nötig. Diese Daten umfassen Viskositätsmodelle, Wärmekapazitätskoeffizienten, Wärmeleitfähigkeitskoeffizienten, PVT-Modelle und bei reaktiven Materialien Härtungsmodelle. Bei der Spritzgießsimulation von Thermoplasten sind die Materialdaten in der Regel in den Simulationstools verfügbar. Der Anwender kann problemlos ThermoplastMaterialdaten auswählen, die bereits in die Materialdatenbank der Simulationswerkzeuge eingebettet waren, um die gesamten Phasen des Thermoplastspritzgießprozesses zu simulieren. Bei der Duroplastspritzgießsimulation sind nur begrenzt Materialdaten vorhanden und selten aus der Datenbank der Simulationswerkzeuge verfügbar, da sie nicht nur bei der Messung rheologischer und thermischer Eigenschaften, sondern auch bei der Modellierung rheologischer und kinetischer mathematischer Modelle kompliziert sind. Daher ist es notwendig, eigene Materialdaten zu generieren. Um dieses Problem zu lösen, bedarf es einer umfangreichen Wissensbasis bei der Messung von Materialeigenschaften sowie der Erstellung eines Optimierungsalgorithmus´. Um den Prozess des duroplastischen Spritzgießens exakt zu simulieren, bedarf es zudem fundierter Kenntnisse über die Formfüllungseigenschaften dieser Materialien. Die Untersuchung des Fließverhaltens von duroplastischen Spritzgießmassen im Inneren der Kavität ist jedoch nicht ausreichend beschrieben. Bisher gab es noch keine veröffentlichten Hinweise, die zeigen, wie man aus experimentellen Messdaten (thermische und rheologische Daten) für den reaktiven Spritzgießsimulationsprozess komplette Materialdaten für Duroplaste erzeugen kann. Diese Probleme führen zu einer Abhängigkeit der Anwender von der Materialdatenbank der Simulationssoftware, was zu einer Einschränkung der Anwendung der Computersimulation in der duroplastischen Spritzgießsimulation und dem Vergleich zwischen experimentellen und Simulationsergebnissen führt. Darüber hinaus stellt sich die Frage, ob es beim Füllen der Kavität ein Wandgleiten zwischen Duroplastschmelze und Wandoberfläche gibt oder nicht. Aus diesem Grund wird die Wirkung des Wandgleitens auf die Kavitätenoberfläche bei der Simulation des duroplastischen Spritzgießens immer noch vernachlässigt. Die vorliegende Arbeit konzentriert sich auf drei wichtige wissenschaftliche Ziele. Das erste ist die Innovation eines neuen technischen Verfahrens zur physikalischen Erklärung des Formfüllverhaltens von duroplastischen Spritzgießmassen. Das zweite Hauptziel ist die Entwicklung einer numerischen Methode zur Erstellung eines duroplastischen Materialdatenblattes zur Simulation der Formfüllung von duroplastischen Spritzgießmassen. Schließlich wird die Erstellung von Simulationswerkzeugen auf der Grundlage der physikalischen Gegebenheiten und des erzeugten Materialdatenblattes durchgeführt.To simulate the injection molding process, it is necessary to set Material Data. The Material Data for an injection molding process must include a viscosity model and its fitted coefficients, heat capacity coefficients, thermal conductivity coefficients, a PVT model and its coefficients, a curing model and its coefficients (only for reactive injection molding). With thermoplastics injection molding simulation, the Material Data is generally available from simulation tools. Users could easily choose thermoplastics Material Data that was already embedded in the Material Data bank of simulation tools to simulate the entire phases of thermoplastics injection molding process. However, with thermosets injection molding simulation, the Material Data is found in limited sources and seldom available from Data bank of simulation tools because of complication not only in rheological and thermal properties measurement but also in modeling rheological and cure kinetics mathematical models. Therefore, with thermoset injection molding compounds that its Material Data bank has not been found in Data bank of simulation tools, before setting Material Data, it is necessary to create its own Material Data that simulation packages do not supply a tool. Therefore, to solve this problem, it requires an extensive knowledge base in measurements of Material properties as well as optimization algorithm. In addition, to simulate exactly the thermosets injection molding compound process, it requires a profound knowledge in the mold filling characteristics of thermoset injection molding compounds. However, investigation of flow behavior of thermosets injection molding compounds inside the mold has not been adequately described. Up to now, there has not been any article that shows a complete way to create thermoset Material Data from measured experimental Data (thermal Data and rheological Data) for the reactive injection molding simulation process. These problems are leading to the users ‘dependency on the Material Data bank of simulation tools, leading to restriction in application of computer simulation in the thermoset injection molding simulation and comparison between experimental and simulation results. Furthermore, there is still a big question related to whether there is or no slip phenomenon between thermosets melt and the wall surface during filling the cavity, for which has not yet been found an exact answer. Because of this the effect of wall slip on the cavity surface is still ignored during thermoset injection molding simulation process. This thesis focused on three key scientific goals. The first one is innovation of a new technical method to explain the mold filling behavior of thermoset injection molding compounds physically. The second key goal is developing numerical method to create thermoset Material Data sheet for simulation of mold filling characterizations of thermoset injection molding compounds. Finally, creating a simulation tool base on the physical technique and generated Material Data sheet
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creating Material Data for thermoset injection molding simulation process
Polymer Testing, 2019Co-Authors: Ngoc Tu Tran, Michael GehdeAbstract:Abstract Thermoset Material Data for reactive injection molding simulation process is found in limited sources and seldom available from Data bank of simulation tools because of complication not only in rheological and thermal properties measurement but also in writing optimization algorithm to model rheological and thermal mathematical equations. In this paper, rheological and thermal properties of thermoset injection molding compounds were successfully measured. In addition, a numerical method was developed to create Material Data of thermoset injection molding compounds, which was directly imported into a simulation tool, namely, Moldex3D to investigate its application in thermoset injection molding simulation process. Furthermore, a strong slip phenomenon on the interface between thermoset melt and wall surface which was investigated and detected during injection molding experiments was taken into account in the filling simulation process. The computation was found to be in good agreement with the experimental results, indicating that the new generated Material Data is reasonable and the influence of wall slip on the mold filling characterization of thermoset injection compounds during simulation process is not ignorable.
Bertrand Iooss - One of the best experts on this subject based on the ideXlab platform.
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Global sensitivity analysis in welding simulations -- what are the Material Data you really need ?
Finite Elements in Analysis and Design, 2011Co-Authors: Olivier Asserin, Alexandre Loredo, Matthieu Petelet, Bertrand IoossAbstract:In this paper, the Sensitivity Analysis methodology is applied to numerical welding simulation in order to rank the importance of input variables on the outputs of the code like distorsions or residual stresses. The numerical welding simulation uses the Finite Element Method, with a thermal computation followed by a mechanical one. Classically, a Local Sensitivity Analysis is performed, hence the validity of the results is limited to the neighborhood of a nominal point, and cross effects cannot be detected. This study implements a Global Sensitivity Analysis which allows to screen the whole Material space of the steel family mechanical properties. A set of inputs of the mechanical model --Material properties that are temperature-dependent-- is generated with the help of Latin Hypercube Sampling. The same welding simulation is performed with each sampling element as input Data. Then, output statistical processing allows us to classify the relative input influences by means of different sensitivity indices estimates. Two different welding configurations are studied. Considering their major differences, they give a different ranking of inputs, but both of them show that only a few parameters are responsible of the variability of the outputs. To prove it a posteriori for the first configuration, two series of computations are performed for a complete sample and for its reduced copy --where all the secondary parameters are set to mean values. They match perfectly, showing a substantial economy can be done by giving to the rest of the inputs mean values. Sensitivity analysis has then provided answers to what we consider one of the probable frequently asked questions regarding welding simulation: for a given welding configuration, which properties must be measured with a good accuracy and which ones can be simply extrapolated or taken from a similar Material? That leads us to propose a comprehensive methodology for welding simulations including four sequential steps: a problem characterization, a sensitivity analysis, an experimental campaign, simulations.
Geert Vaes - One of the best experts on this subject based on the ideXlab platform.
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Methodology of Accelerated Characterization for long-term creep prediction of polymer structures to ensure their service life
Polymer Testing, 2019Co-Authors: Eric Lainé, Claire Bouvy, Jean-claude Grandidier, Geert VaesAbstract:Predicting long-term creep behaviour (10 or 20 years) is important in the development and design of thermoplastic structures to ensure their service life. But providing robust Material Data to engineers is not easy, as testing is expensive and time-consuming. The purpose of this paper is to propose a new Methodology of Accelerated Characterization for long-term creep Prediction (MACcreeP) which gives more Material information much faster and at a lower cost, than Classical Methodology (CM) used today by engineers. In order to prove the effectiveness of this new method, the Material used for the study is a well-known high density polyethylene grade (HDPE). More precisely MACcreeP is a characterization protocol based on creep tests performed in true tensile stress conditions over short time periods (24 h) and at different temperatures combined with the Time-Temperature Superposition Principle to construct master curves. In addition to the Material Data obtained over a short period of time, the Data generated by the behaviour laws and the use of the CREEP law in a FEA software allows a more predictive numerical response than in CM. Finally, this methodology, which implies very few tests, allows to compare and choose the appropriate Material properties to design a thermoplastic structure which will comply with end-use product requirements.
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Methodology of Accelerated Characterization for long-term creep prediction of polymer structures to ensure their service life
Polymer Testing, 2019Co-Authors: Eric Lainé, Claire Bouvy, Jean-claude Grandidier, Geert VaesAbstract:Predicting long-term creep behaviour (10 or 20 years) is important in the development and design of thermoplastic structures to ensure their service life. But providing robust Material Data to engineers is not easy, as testing is expensive and time-consuming. The purpose of this paper is to propose a new Methodology of Accelerated Characterization for long-term creep Prediction (MACcreeP) which gives more Material information much faster and at a lower cost, than Classical Methodology (CM) used today by engineers. In order to prove the effectiveness of this new method, the Material used for the study is a well-known high density polyethylene grade (HDPE). More precisely MACcreeP is a characterization protocol based on creep tests performed in true tensile stress conditions over short time periods (24 h) and at different temperatures combined with the Time-Temperature Superposition Principle to construct master curves. In addition to the Material Data obtained over a short period of time, the Data generated by the behaviour laws and the use of the CREEP law in a FEA software allows a more predictive numerical response than in CM. Finally, this methodology, which implies very few tests, allows to compare and choose the appropriate Material properties to design a thermoplastic structure which will comply with end-use product requirements.
Luboš Náhlík - One of the best experts on this subject based on the ideXlab platform.
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Influence of Threshold Values on Residual Fatigue Lifetime of Railway Axles under Variable Amplitude Loading
Procedia Engineering, 2015Co-Authors: Pavel Pokorný, Luboš Náhlík, Pavel HutarAbstract:Abstract The paper deals with an estimation of residual fatigue lifetime of railway axles under real loading spectrum. The residual fatigue lifetime is given by magnitude of fatigue crack propagation rate. This rate depends predominantly on load, geometry and Material of the axle. Standard steel EA4T for manufacturing of railway axles is considered in this paper. The scatter of Data in v-K curve could be caused by inaccuracy of experimental measurement or by local change of Material properties. The paper shows important differences between obtained residual fatigue lifetime estimations considering scatter in measured Material Data, especially near the threshold region.
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3rd International Conference on Material and Component Performance under Variable Amplitude Loading, VAL2015 Influence of threshold values on residual fatigue lifetime of railway axles under variable amplitude loading
2015Co-Authors: Pavel Pokorný, Luboš NáhlíkAbstract:The paper deals with an estimation of residual fatigue lifetime of railway axles under real loading spectrum. The residual fatigue lifetime is given by magnitude of fatigue crack propagation rate. This rate depends predominantly on load, geometry and Material of the axle. Standard steel EA4T for manufacturing of railway axles is considered in this paper. The scatter of Data in v-K curve could be caused by inaccuracy of experimental measurement or by local change of Material properties. The paper shows important differences between obtained residual fatigue lifetime estimations considering scatter in measured Material Data, especially near the threshold region.
