D6
Materials 1

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10:40
conference time (CEST, Berlin)
Predictive Numerical Simulations in Different Loading Cases for Polymer Structures
26/10/2021 10:40 conference time (CEST, Berlin)
Room: D
P. Mahajan, Y. Trolez, E. Maziers, G. Hibert (Total Energies, BEL); E. Laine, J.C Grandidier (ENSMA, FRA)
P. Mahajan, Y. Trolez, E. Maziers, G. Hibert (Total Energies, BEL); E. Laine, J.C Grandidier (ENSMA, FRA)
The application of polymers is spread in many sectors, and these materials are increasingly replacing metals. It is therefore crucial to develop the constitutive model of these polymers in order to perform predictive numerical simulations for industrial structures under different types of loading. An elasto-viscoplastic behavior law taking into account hydrostatic pressure, based on the model proposed by Grandidier et al [1] has been devolved for a Polyethylene. All material constants were determined, from experimental data of six tensile tests at different constant real strain rates, by inverse problems coupling to the simulation software (Abaqus©) an optimization software (DAKOTA [2]). The validation of the behavior law is performed by comparing tests and numerical simulations of 3 and 4 point bending tests for different specimen thicknesses. In a 3-point bending test, the combination of tension, compression, and shear occurs in the material, whereas in the 4-point bending test, pure bending occurs in the specimen between the two moving rollers, which allows the model to be evaluated under different types of loading. The effect on the simulation results of different parameters such as the hydrostatic pressure coefficient, the friction coefficient, and the geometrical dimension of the specimens are studied. Mesh convergence was performed, different 3D elements were used to see the influence on the numerical responses. Bending test simulation results were in good agreement with the experimental results. Finally, a correlation between the experimental results of a test on a polyethylene reference structure subjected to an internal hydraulic pressure which generates complex loading conditions, and the corresponding numerical simulation is presented. The confrontation of the 3D displacement fields obtained on the one hand by a non-contact optical method during the test and on the other hand by numerical simulation confirms the predictivity of the numerical tool. [1] J. C. Grandidier and É. Lainé, “Identification by genetic algorithm of a constitutive law taking into account the effects of hydrostatic pressure and speeds,” Oil Gas Sci. Technol., vol. 61, no. 6, pp. 781–787, 2006. [2] J. G. Adams, B.M., Bohnhoff, W.J., Dalbey, K.R., Ebeida, M.S., Eddy, J.P., Eldred, M.S., Hooper, R.W., Hough, P.D., Hu, K.T., Jakeman, J.D., Khalil, M., Maupin, K.A., Monschke, J.A., Ridgway, E.M., Rushdi, A.A., Seidl, D.T., Stephens, J.A., Swiler, L.P., and Wi, “‘Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User’s Manual,’ Sandia Technical Report SAND2020-12495, November 2020.” .
Numerical simulation, Constitutive model, Polymer, Elasto-viscoplastic, Structures, strain rate, 3- and 4- point bending, Correlation test/simulation
11:00
conference time (CEST, Berlin)
Bridging the Gap Between Materials Selection and Simulation
26/10/2021 11:00 conference time (CEST, Berlin)
Room: D
L. Mohee (Ansys UK, GBR); D. Mercier (Ansys, FRA)
L. Mohee (Ansys UK, GBR); D. Mercier (Ansys, FRA)
In silico testing in healthcare is becoming increasingly cost-effective and time saving to understand the behavior of medical devices before conducting clinical trials. It also enables regulatory approval while prioritizing patient safety. Our proposal is the development of an innovative modelling framework based on the coupling of analytical material selection methodology with a simulation driven design approach, that supports material and geometry optimisation for new medical devices development [1]. One example of a medical application, where this approach might be used, is in the design of a total hip joint implant to replace a worn or damaged femur. The first part of this work is to identify the best material candidates that have met design constraints and objectives of a total hip joint replacement, using Ansys Granta software which contains databases of advanced bio-engineering materials and their properties. In the second part, a computer-aided design (CAD) model of a hip implant is imported on Ansys Discovery where it is simulated using finite-element analysis (FEA). The model is subjected to multiple structural forces that mimic a real-life scenario of a hip implant. The software facilitates meshing of the model and delivers instant multiphysics simulation. An instantaneous visualization of stress and strain fields into the hip joint led to an optimized combination of material and geometry; resulting in developing relevant new designs of a common hip joint. The effect of material choice on various components of the hip implant is also analyzed in the study. Our numerical outcomes were finally compared to reference data from literature [2]. [1] Fleischmann C. et al., A new approach to quickly edit geometries and estimate stresses and displacements of implants in real-time, Current Directions in Biomedical Engineering 5(1):553-556 (2019). https://doi.org/10.1515/cdbme-2019-0139 [2] Şensoy, A.T. et al. Optimal Material Selection for Total Hip Implant: A Finite Element Case Study. Arab J Sci Eng 44, 10293–10301 (2019). https://doi.org/10.1007/s13369-019-04088-y
material selection, optimization process, simulation driven design, innovative modelling framework, finite element analysis, biomaterials, bioengineering, in silico, healthcare
11:20
conference time (CEST, Berlin)
Integration and Processing of Material Property Data from Different Sources to Create Materials Cards
26/10/2021 11:20 conference time (CEST, Berlin)
Room: D
U. Diekmann, P. Rostami (Matplus GmbH, DEU); R. Ufer, T. Marwitz (HS Mittweida, DEU)
U. Diekmann, P. Rostami (Matplus GmbH, DEU); R. Ufer, T. Marwitz (HS Mittweida, DEU)
Consistent materials information is an important input for many CAE-simulations. A big variety of material card formats for different applications is available. In this paper we are addressing the following practical issues: (a) Material designations are usually related to standards which allow a scatter of properties depending on variations in chemical composition and process history. This scatter is frequently not considered in simulations. (b) Materials testing to describe complex material behaviour is frequently expensive and time consuming. Budgetary and time constraints lead to a situation, that such expensive tasks are not statistically validated so that material cards relying on few measurements may be inconsistent. In many cases material properties can be calculated using materials simulations which is state- of-the-art for many data of structural metals using CalPhad, JMAK and additional physically based models. We are extending this approach by performing high throughput calculations with variation of chemical compositions and variation of processing like heat treatment. As a result of several thousand calculations which are consolidated in a NoSQL database we can derive multiple property maps for a single material designation. This allows a selection of worst-case and best-case materials from which we can derive material cards for different solvers. Additionally those calculations can help to assess the calculated influence of variations on the resulting material models. Frequently material models, e.g. for plasticity are taken from different sources which show significant deviations, like text books, material catalogues and different tests. We can now consolidate these data into a common data structure which allows (a) a direct comparison of curves and (b) the generation of hybrid models by curve fitting to different constitutive equations. This involves methods for thinning/resampling/averaging of measurements represented as parametrized time-series. Data from materials simulation are usually consistent with respect to the influence of composition, temperature and strain rate. Combining this information with selected measurements can be used to calibrate and extrapolate the data. We will present the influence of different material models for plasticity, like Johnson-Cook and Hensel-Spittel as well as the influence of different minimizers, like Nelder-Mead and BFGS on the results. The modelling results are subsequently consolidated in a materials master model, which is designed to be a homogeneous source of information for different solvers of different vendors. Configurable mapping tables allow the export of different material cards from such single source of information. Existing material cards can in turn be imported and data of them can be compared to the data of the master model.
Data Management, Materials Master Model, CAE-Tools, Material Cards, JMatPro®, EDA®
11:40
conference time (CEST, Berlin)
Prepare for the Future With a Digital Transformation for Your Engineering Materials Data
26/10/2021 11:40 conference time (CEST, Berlin)
Room: D
C. Bream (Ansys, GBR)
C. Bream (Ansys, GBR)
Digital Transformation is often used to cover many different digitalisation activities that can often cost a lot to implement and yield little overall benefit. However, true digital transformation changes processes in ways that solve real business challenges and make transformative business benefits. We have seen many businesses achieve true digital transformation like this by digitising their materials processes and data. For an engineering enterprise, the digital transformation journey typically starts by focusing on the product definition and working through the various value adding processes required to create it. The ‘digital thread’ being the framework that connects the data flows. Materials data is a fundamental component in any product’s definition, design and simulation. It therefore makes sense that many engineering companies treat the digitalisation of materials data as one of the first steps in achieving their overall digital transformation. The value of this transformation and delineating the ‘digital thread’ for materials data is clear. Data is structured, standardized, and easy to find, enterprise-wide, adding value by bringing added context for the material’s application. This in turn results in time and cost savings across the engineering functions. The challenge comes in finding the right technology protocol to achieve the reuse of the above data across functions, processes, and applications. But, also in creating the culture and leadership buy-in to make it happen. This presentation aims to take a step-by-step approach through the technology journey in achieving the above using Ansys Granta MI - a leading materials management solution from Ansys. We follow practical steps with tangible case studies from industry that follow the end-to-end requirements for engineering materials data from testing in labs to their application in leading CAD and CAE software products. The idea being that these practical steps form a roadmap that will also help in creating a culture of awareness and ultimately leadership buy-in.
engineering materials digital transformation
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