Materials Modelling and Interoperability Siemens PLM Vision

Transcription

1 Materials Modelling and Interoperability Siemens PLM Vision November 2017 Realize innovation.

2 Siemens PLM Simulation & Test Solutions Mission: Help end user industry to manufacture better products more efficiently Approach: addressing Industry s burning needs related to their product development Lower Fuel Consumption Renewable Energy CO 2 Emission Management Lighter Materials R&D strategy: foster technology innovation through R&D collaboration Products: PLM Software, Test Software and Hardware, Engineering Services Supporting the end to end engineering process workflow, and seamlessly linking to manufacturing. Page 2

3 MacroModelMat (M3) program Knowledge centers Solving lightweight challenges by advanced testing & simulation Industry Page 3

4 M3 research program for composites: multi-material, -scale, -attribute & -physics Multi-Material right material Multi-Physics T [C] manufacturing Curing at right place Multi-Scale Macro µicro MacroModelMat Macro level simulation solutions Multi-Attribute applications meso Page 4

5 M3 research program for additive manufacturing: multi-material, -scale, -attribute & -physics Multi-Material right material Multi-Physics manufacturing M3 AMESTO (in prep.) at right place µicro Multi-Scale Macro MacroModelMat Macro level simulation solutions Multi-Attribute applications meso Page 5

6 Simcenter Portfolio for Predictive Engineering Analytics Simcenter 3D & NX Nastran Simcenter 3D NX Nastran, Samcef Page 6

7 Open environment, with Multi-CAE solver support NX Nastran LMS Samcef Abaqus Multi-CAD geometry editing Comprehensive meshing Assembly management Simcenter 3D Solution / subcase management Post-processing & reporting Associativity ANSYS LS-Dyna MSC Nastran Page 7

8 Simcenter 3D Unified, scalable, open and extensible environment Centralized pre/post to efficiently build models for your solver of choice A scalable environment for analysts, discipline specialists, and design engineers Customizable (via NX Open and DMAP scripting, user materials) to meet (y)our needs Page 8

9 Predictive CAE for (CFR) Composites through Multiscale Modelling! 1.E+01 1.E+00 1.E-01 Information Management: Store the answer... input for next scale 1.E-02 Macro FEA Length Scale (m) 1.E-03 1.E-04 1.E-05 1.E-06 1.E-07 1.E-08 1.E-09 1.E-10 1.E-11 Molecular dynamics Constituents (fiber, matrix, interface) Requirements flow:... Ask the question 1.E-12 1.E-14 1.E-12 1.E-10 1.E-08 1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 UD Reinforcement type * RVE incl. stacking sequence Two-level interactions are emerging (one example shown here): Coupled simulation: concurrent two-scale codes in one solver; Co-simulation: two interacting codes, interchanging per timestep. Page 9 Time Scale (Seconds) * WiseTex, courtesy of KU Leuven. Figure: courtesy of

10 Predictive CAE for (CFR) Composites through Multiscale Modelling! 1.E+01 DISCRETE 1.E+00 Any info from 1.E-01 lower levels that can help to 1.E-03 This requires 1.E-04 further R&D and development Length Scale (m) 1.E-06 1.E-07 C-MICRO Information Management: Store the answer... input for next scale alleviate the need for testing & simulation 1.E-02 at OEMs / other suppliers is an advantage. of new modeling 1.E-05 and new standardized testing procedures. UD Reinforcement type C-MESO RVE incl. stacking sequence C-MACRO Macro FEA PI Macro-level structure model for performance predictions Coupon Tests Stiffness Strength Damage 1.E-08 1.E-09 Physics/Chemistry: 1.E-10 additional source of 1.E-11 information Molecular dynamics Constituents (fiber, matrix, interface) Architecture Geometry (e.g. weave, laminates) Material scans (e.g. Micro-CT) Constitutive Requirements flow: Behaviour Failure mechanisms... Ask the question 1.E-12 1.E-14 1.E-12 1.E-10 1.E-08 1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 Page 10 Time Scale (Seconds)

11 Composite design at Honda R&D Co., Ltd. Enabled by multi-scale approach Joseph Joseph Vehicle Expertise build-up full vehicle simulation Subsystem Validation of complex subsystem modeling Component Model validation on components and joint technology Coupon Design/Validation of material models Multi-scale simulation Frontloading Composite design to maximize design space exploration (Multi-attributes) We need more simulation-based product design data and coupon level testing to establish a dependable simulation process for all the material and design choices at hand. Dr. Yuta Urushiyama, Chief Engineer, Technology Research Division, Honda Page 11 Originates from project M3Strength

12 Virtual Material Characterization To Accelerate the Composites Design Process Test Based (Coupon) Micro Meso Models Simcenter - Virtual Material Characterization Simulation - Analysis Material Characteristics: Damage, Permeability Very much reduced number of tests Include performance and manufacturing-related aspects (effect of defects) Allows multi-attribute virtual material optimization Critical Enabler for Expanded Composite Design Space Exploration and Optimization Page 12

13 Trend: need for better product performance drives OEMs to lower-level knowledge Need arises to analyze and prioritize underlying physics and chemistry characteristics: Surface roughness Porosity Chemical bonding / adhesion properties Crystalline structure... of fiber & matrix material TRANSLATOR Walt Disney, 1956 Automotive OEM: Example We wish to avoid shear failure at the fiber-matrix interface. Courtesy of Ghent University. Many interacting phenomena (physics, chemistry, mechanics) together determine whether or not a shear failure mode is likely to occur. First step is understanding this & converting the new know-how into guidelines (Translator activity). Second step is to script and automate such information transfers into new software processes / tools that connect databases and interface with users. Page 13

14 Differences in data needs at different industry segments 1.E+01 DISCRETE 1.E+00 1.E-01 1.E-02 Information Management: Store the answer... input for next scale C-MICRO C-MESO C-MACRO Macro FEA Length Scale (m) 1.E-03 1.E-04 Material Manufacturers: Strong need for detailed physics & chemistry data and models, to support materials design challenge IPR / secrecy of recipe and manufacturing process 1.E-05 1.E-06 1.E-07 1.E-08 1.E-09 1.E-10 1.E-11 Molecular dynamics Constituents (fiber, matrix, interface) UD Reinforcement type RVE incl. stacking sequence Collaboration between people is key: End-user product manufacturers: Need for macro-level model parameters (from testing, characterization, calculation/simulation, ) Need for new materials that are fit-for-purpose for Requirements challenging flow: applications... Ask the question 1.E-12 1.E-14 1.E-12 1.E-10 1.E-08 1.E-06 1.E-04 1.E-02 1.E+00 1.E+02 1.E+04 1.E+06 Page 14 Time Scale (Seconds) physicists, chemists, materials scientists, engineers

15 DATA How to generate, store and use? From raw test data Or data sheet From Simulation Manufacturing Simulation Data Management and Parameter Identification (PI) Updating input data for optimisation Performance Simulation Static Damage Durability etc. Page 15

16 Towards an optimal design of 3D printed lightweight structures 1 Design space & FE Model preparation for Topology Optimization (TO) 5 Print final lightweight design 2 TO drives solution to find zones for Lightweight (Lattice) and Bulk considering true lattice material properties and manufacturability Red = Bulk Blue = Lattice 4 FE verification of design for any load case 3 Post TO treatment Lightweight structure creation Variable local truss diameter based on TO results Octet Objective: Optimal design of 3D printed structures. Content: Achieve an optimal design of 3D printed lightweight structures by adopting the predictive CAE workflow including topology optimization. Page 16 Originates from project M3AMCAE

17 Melt Pool Simulation: Simulation strategies Continuum simulation approach Solve mainly energy conservation equation; optionally, flow transport equation Approximate melt pool surface shape, relatively coarse spatial discretization Surface forces (surface tension, Marangoni effect, wetting, recoil pressure) neglected or approximated Moderate computational cost Powder-scale simulation Flow + energy equations Detailed surface shape, fine spatial discretization (powder particles) Surface forces modeled in detail (surface tension, wetting, recoil pressure) High computational cost Page 17 Jamshidinia et al, 2013 Panwisawas et al, 2017

18 Melt Pool Simulation: Target of the simulations with flow Prediction of melt pool size & thermal history Flow transport due to Marangoni effect increases heat transport and can influence melt pool size & thermal history Jamshidinia et al, 2013 Prediction of defects/undesirable effects: Balling (Plateau-Rayleigh instability) Spattering (melt pool surface disturbances) Porosity due to incomplete melting Keyhole-related porosity King et al, 2015 Qiu et al, 2015 Page 18

19 Optimization of 3D printing machines and process window Fine tuning of AM process parameters Understanding of gas flow in SLM Simcenter 3D STAR-CCM+ supports: CFD-based SLM melt-pool simulation to determine optimal AM process parameters Design of 3D printers: Design of optimal DED nozzles Optimization of gas-flow in SLM build chambers for performant AM machines Optimal DED nozzles Page 19

20 Additive Manufacturing Courtesy of Access, a business partner of Siemens PLM Page 20

21 Thank you! For comments or questions about this presentation, please contact