September 11, American Chemistry Council Predictive Engineering Activities Michael G. Wyzgoski ACC Consultant for Predictive Engineering

Transcription

1 September 11, 2012 American Chemistry Council Predictive Engineering Activities Michael G. Wyzgoski ACC Consultant for Predictive Engineering

2 PREDICTIVE ENGINEERING DEFINITION: Use of finite element analyses with constitutive and failure models to accurately simulate the mechanical performance of plastic and composite components and systems GOAL: Advance the state of the art in predictive engineering and modeling for automotive plastics, especially in structural and safety related applications IMPORTANCE: *Critical for increased uptake of plastics and polymer composites into automotive applications *Source: Frost and Sullivan Automotive Light-Weighting Consortium final report- October 10, 2011

3 Predictive Engineering State of the Art Simulation Performance IMPROVING

4 American Chemistry Council Role Facilitate development of predictive engineering tools for plastics and composites Provide communication and coordination between industry, government and academia Help to define specific areas for ongoing research in materials, processing and properties Help provide financial support to university or government labs efforts to undertake key research

5 American Chemistry Council Role: 5

6 SFT and LFT Injection-Molded Composites Lfiber = mm Lfiber = mm Short-Fiber Thermoplastics (SFT) : The most common injectionmolded composite Glass (or carbon) fibers Polypropylene, nylon are common matrices Add fibers to any matrix to gain stiffness, reduce creep Long-Fiber Thermoplastics (LFT): Pultruded pellets give long (initial) fiber length Glass or carbon fibers Polypropylene, nylon are common matrices Better properties than SFTs; easier processing than continuous-fiber composites 6

7 ACC FUNDED PE RESEARCH SUMMARY Oak Ridge National Labs (LFT) University of Illinois (LFT) Virginia Tech (LFT) University of Dayton Research Institute (LFT) Michigan State University (LFT) Axel Products Testing Laboratory (SFT) Mississippi State University (Unreinforced)

8 Engineering Property Prediction Tools for Tailored Polymer Composite Structures DOE FUNDED ORNL/PNNL Focus: Long Glass Fiber Thermoplastics (LFT) Principal Researchers: Ba Nghiep Nguyen (Pacific Northwest National Lab) and Vlastimil Kunc (Oak Ridge National Lab) In Collaboration with: Professor Charles L. Tucker III, University of Illinois at Urbana-Champaign Acknowledgements This work was funded by the DOE s Office of Vehicle Technologies, Dr. Carol Schutte, Team Leader for Materials Technology Autodesk, Inc. provided PNNL with research versions of Autodesk Moldflow Plastics Insight under CRADAs Nr. 260 and Nr. 304 Steering Committee: American Chemistry Council Plastics Division, Dr. Michael Wyzgoski, Consultant and USCAR: Dr. Peter H Foss, GM Corporation

9 Fiber Orientation Tensor Component in the Flow Direction Fiber Orientation Tensor Component in the Flow Direction Microstructural Aspects S-/LFT microstructural aspects: spatial variations in fiber orientation and length Skin Shell Core Thickness Fiber length distribution in LFTs Skin/shell/core orientation layer structure in LFTs Injection-molded SFT Injection-molded LFT shell core shell shell core shell LFTs possess larger cores and thinner shell layers Normalized Thickness 9

10 Summary of the Models Developed for LFTs (Long Fiber Thermoplastics) Step 1) Process models developed provide LFTs Microstructure The anisotropic rotary diffusion - reduced strain closure model (Phelps & Tucker, 2009) for Fiber Orientation Distribution The fiber length attrition model in the mold cavity (Phelps & Tucker, 2009) for Fiber Length Distribution Models implemented in Autodesk Simulation Moldflow Insight Validation with a 3-D part has yet to occur but is planned Step 2) Micromechanics models predict LFTs Mechanical Performance Eshelby-Mori-Tanaka Analysis (EMTA) for strength and stiffness Nonlinear constitutive models (EMTA-NLA) for creep, impact and fatigue life further validation required. Autodesk /PNNL discussing incorporating EMTA into Autodesk Moldflow Structural Alliance (AMSA) Other property prediction models are available (e-xstream, esi-group, etc.)

11 Schematic of Autodesk Moldflow AMSA Source: Autodesk Moldflow

12 e-xstream Engineering Digimat Modeling for LFTs Source: e-xstream Engineering Young s modulus (MPa) Large aspect ratio and fiber length distribution Clustering Fiber waviness Specific fiber orientation mechanisms w.r.t to short fibers Non homogeneous fiber content Use of mean field and finite element based homogenization No cluster Partially clustered Fully clustered Straight fiber limit Stress Tortuosity Strain Clustering leads to stiffness reduction Waviness leads to stiffness reduction

13 Multi-scale Material Modeling with DIGIMAT Full workflow from injection molding to structural FEA Copyright e-xstream engineering, 2012

14 University of Illinois Fiber Length Distribution Model Status The model structure and preliminary results: Improvements are likely to come from better modeling of the breakage probability, P i Include flow-type and orientation state dependence? (D, A) Better understand volume-fraction and length dependence Will work to simplify or transform the model to speed up computation: Fiber length has130 variables per node: this number of calculations to be decreased to increase speed/efficiency Fiber orientation is routine, uses 5 variables/node

15 A Proper Orthogonal Decomposition (POD) model is under development, with pre-computed modes Solve the mold-filling flow to get typical shear stress values Use simple-shear solutions of full model for 3 different shear stress values Solve the POD model for the full mold 15

16 Virginia Tech Modeling Fiber Breakage in the Injection Molding Machine Approaches: Professor Don Baird Developing mathematical model (Preferred) Utilize dimensionalized parameters to develop an empirical model

17 Schematic of Injection Molding Machine Showing Molten Polymer Film MODEL ASSUMES CRITICAL BENDING MOMENT FOR FIBER BREAKAGE 17

18 University of Dayton Research Institute High Rate Tensile Testing of Injection Molded Long Fiber Filled Polypropylene Susan I. Hill Peter Phillips NOTE: Existing SAE J2749 uses a 3mm wide specimen

19 PHASE I: Finite Element Analysis Study of stress ratios and property prediction Specimen evaluation & dynamic test simulation Increasing tab length

20 Phase II: Testing Thickness, Fiber Content, and Orientation Fiber content of 20, 30 & 40 wt%, Thickness of 2, 3, & 4mm Specimen width 10mm, Nominal rate of 40/s Sensitivity of mechanical properties to the specimen size varied Peak Strength: 15W 10W = 5W at 30wt%, 15W> 5W at 40wt% Modulus: 15W > 10W > 5W Failure strain: 15W = 10W < 5W Panel Thickness strength as panel thickness Relative change with fiber content Strength at 40/s as a function of thickness and fiber content

21 Michigan State Composite Vehicle Research Center Modeling Injection Molded LFT Composites Xinran Xiao, Danghe Shi, Motozo Horikawa, Christopher Cater, Wenzhe Zhu Objective of this Work: Develop a modeling approach for driven dart impact prediction Current task: examine the feasibility of modeling in-plane and out-plane elastic behavior using a simple laminate approach

22 In-Plane Modulus (GPa) Flexural Modulus (GPa) Comparison With Experiment Comparison With Experiment Laminate 1 - [45/0/-45/90]s [0.18/0.62/0.18/0.36]s. Laminate 2 - [45/15/-15/-45/75/-75]s [0.18/0.31/0.31/0.18/0.12/0.12]s. Prediction with upper bound values and numerically predicted transverse and shear properties: In-plane Laminate 2 improved the prediction but under predicted both the in-plane and out-of-plane elastic properties with the exception of in-plane shear modulus G. experiment laminate1 laminate2 E_flow E_cross E_45 G Out-of-plane experiment laminate1 laminate2 E_flow E_cross E_45 22

23 Effect of Fiber Orientation on Fatigue (S-N) 35% SGF Polyamide AXEL PRODUCTS, INC. TEST DATA Fatigue Crack Growth Measurements Underway

24 PRESENTATION SUMMARY Advances in predictive engineering of SGF and LGF reinforced plastics has significantly increased in recent years Conducting Case studies of actual components (3-D Parts) and systems is the next step Implementing more testing and test standards for data suitable to feed FEA models is an ongoing effort The American Chemistry Council Plastics Division will continue to facilitate the development of predictive engineering tools through coordination and support of industry, government and academia activity

25 Michael G. Wyzgoski ACC Consultant for Predictive Engineering Questions? For further information contact Gina Oliver Gina-Marie