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Sonne M.R.,Technical University of Denmark | Smistrup K.,APS Technology Inc. | Hannibal M.,Danish Fundamental Metrology | Thorborg J.,Technical University of Denmark | And 3 more authors.
Journal of Materials Processing Technology

In the presented work, simulations of the deformation process of flexible stamps used for nanoimprint lithography on curved surfaces are presented. The material used for the flexible stamps was polytetrafluoroethylene (PTFE) whose material behavior was found to be viscoelastic-viscoplastic. This behavior was described in a temperature dependent constitutive model consisting of a Zenerbody for the viscoelastic deformation and the Johnson-Cook model for the description of the viscoplastic deformation. The constitutive model was implemented in the general purpose finite element software ABAQUS through a user material subroutine. In order to take the large strains and deformations during the imprinting manufacturing process into account, non-linear geometry was applied in the simulations. The model was first verified through a series of experiments, where nanoimprint lithography on a curved tool insert for injection molding were performed with various process parameters such as temperature, imprinting pressure and flexible stamp thickness. Good agreement between simulations and experimental results was found. The optimum process parameters were then used in the final application, where nanoimprint of a nanostructure giving a color effect was performed numerically and experimentally. Both experiment and simulation showed a mismatch between the defined and measured nanostructures as a result of stretching of the flexible stamp. The model was shown to predict the stretch of the nanostructures with a maximum error of 0.5%, indicating that the model is able to capture the physics of this manufacturing process and can be used to give an insight into the nanoimprinting procedure on curved surfaces. © 2014 Elsevier B.V. Source

Magma Giessereitechnologie Gmbh | Date: 2010-06-28

An apparatus for simulating a casting or molding process, said apparatus comprising means for providing a computer model (

Sturm J.C.,MAGMA Giessereitechnologie GmbH | Busch G.,MAGMA Giessereitechnologie GmbH
China Foundry

High strength compacted graphite iron (CGI) or alloyed cast iron components are substituting previously used non-ferrous castings in automotive power train applications. The mechanical engineering industry has recognized the value in substituting forged or welded structures with stiff and light-weight cast iron castings. New products such as wind turbines have opened new markets for an entire suite of highly reliable ductile iron cast components. During the last 20 years, casting process simulation has developed from predicting hot spots and solidification to an integral assessment tool for foundries for the entire manufacturing route of castings. The support of the feeding related layout of the casting is still one of the most important duties for casting process simulation. Depending on the alloy poured, different feeding behaviors and self-feeding capabilities need to be considered to provide a defect free casting. Therefore, it is not enough to base the prediction of shrinkage defects solely on hot spots derived from temperature fields. To be able to quantitatively predict these defects, solidification simulation had to be combined with density and mass transport calculations, in order to evaluate the impact of the solidification morphology on the feeding behavior as well as to consider alloy dependent feeding ranges. For cast iron foundries, the use of casting process simulation has become an important instrument to predict the robustness and reliability of their processes, especially since the influence of alloying elements, melting practice and metallurgy need to be considered to quantify the special shrinkage and solidification behavior of cast iron. This allows the prediction of local structures, phases and ultimately the local mechanical properties of cast irons, to asses casting quality in the foundry but also to make use of this quantitative information during design of the casting. Casting quality issues related to thermally driven stresses in castings are also gaining increasing attention. Stateof- the-art tools allow the prediction of residual stresses and iron casting distortion quantitatively. Cracks in castings can be assessed, as well as the reduction of casting stresses during heat treatment. As the property requirements for cast iron as a material in design strongly increase, new alloys and materials such as ADI might become more attractive, where latest software developments allow the modeling of the required heat treatment. Phases can be predicted and parametric studies can be performed to optimize the alloy dependent heat treatment conditions during austenitization, quenching and ausferritization. All this quantitative information about the material's performance is most valuable if it can be used during casting design. The transfer of local properties into the designer's world, to predict fatigue and durability as a function of the entire manufacturing route, will increase the trust in this old but highly innovative material and will open new opportunities for cast iron in the future. The paper will give an overview on current capabilities to quantitatively predict cast iron specific defects and casting performance and will highlight latest developments in modeling the manufacture of cast iron and ADI as well as the prediction of iron casting stresses. Source

Hahn I.,MAGMA Giessereitechnologie GmbH | Sturm J.C.,MAGMA Giessereitechnologie GmbH
69th World Foundry Congress 2010, WFC 2010

Twenty years after the introduction of simulation software for foundries into the industry. casting process simulation has become an accepted tool for process and design lay-out. Casting process simulation always displays the status quo of its expert user. The user decides if the rigging system or process parameters lead to an acceptable result. Additionally. proposals for optimized solutions have to come from the operator. One of the biggest benefits of the casting process is also its biggest downfall: Everything happens at the same time and is coupled. Changes in one process parameter impact many casting quality defining features during the process. Multiobjective autonomous optimization offers a way out. Autonomous optimization uses the simulation tool as a virtual experimentation field and changes pouring conditions. gating designs or process parameters and this way tries to find the optimal route to fulfill the desired objective. Several parameters can be changed and evaluated independently from each other. Autonomous optimization tools take the classic approach of foundry engineers. to find the best compromise and use validated physics. This not only further reduces the need for trial runs to find the optimal process window. but allows the detailed evaluation of many process parameters and their individual impact on providing a robust process. Obviously. what can be simulated can be optimized. Optimization. therefore. is not a replacement for process knowledge and expertise. Despite beliefs to the contrary. the simulation user of the future needs to know the objectives and goals. and especially the quality criteria that are needed to reach these goals. The questions to ask a program are easy: What is a good gating system? To answer this question. quantitative solutions are required. An old foundry man's dream is becoming reality: trial and error is not performed on the shop floor but on the computer. The foundry man defines his optimization goals and can evaluate the best possible solution. He also receives quantitative information about the sensitivity of important process parameters and can assess the robustness of his designs. The paper will give an overview on the state of the art of virtual autonomous optimization on selected industrial examples. Source

Fainberg J.,MAGMA Giessereitechnologie GmbH | Schaefer W.,MAGMA Giessereitechnologie GmbH
IOP Conference Series: Materials Science and Engineering

A new algorithm for heat exchange between thermally coupled diffusely radiating interfaces is presented, which can be applied for closed and half open transparent radiating cavities. Interfaces between opaque and transparent materials are automatically detected and subdivided into elementary radiation surfaces named tiles. Contrary to the classical view factor method, the fixed unit sphere area subdivision oriented along the normal tile direction is projected onto the surrounding radiation mesh and not vice versa. Then, the total incident radiating flux of the receiver is approximated as a direct sum of radiation intensities of representative "senders" with the same weight factor. A hierarchical scheme for the space angle subdivision is selected in order to minimize the total memory and the computational demands during thermal calculations. Direct visibility is tested by means of a voxel-based ray tracing method accelerated by means of the anisotropic Chebyshev distance method, which reuses the computational grid as a Chebyshev one. The ray tracing algorithm is fully parallelized using MPI and takes advantage of the balanced distribution of all available tiles among all CPU's. This approach allows tracing of each particular ray without any communication. The algorithm has been implemented in a commercial casting process simulation software. The accuracy and computational performance of the new radiation model for heat treatment, investment and ingot casting applications is illustrated using industrial examples. © Published under licence by IOP Publishing Ltd. Source

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