Composite Materials Simulation and Modeling for Enhanced Lightweight

Body StructuresVenkat Aitharaju, William R. Rodgers

General Motors Global Research and Development02/20/2019

Outline• Mass Breakup of an Automobile• Automotive Lightweighting• Composites in the Vehicle Body• Challenges for Composites Automotive Structures• Predictive Models for Composites• Extension of Simulation Models• Conclusions

Mass Breakup of an Automobile

Body structures comprise the highest fraction of vehicle body Electric vehicles are likely to be heavier, light weighting is critical to increase the range and competitive advantage between different OEM’s

20%

6%

8%6%

10%17%

33%

Typical Gas Powered Vehicle

Body structureBody closurePowertrainExteriorInteriorChassisBattery

31%

10%32%

9%

18%

Typical Electric Vehicle

Body structureBody closurePowertrainExteriorInterior

Mass Breakup of an Automobile• Body structure is one of the heaviest pieces of the vehicle and

therefore poses a significant opportunity for lightweighting for improved fuel economy and reduced emissions.

• For battery powered vehicles, added battery weight requires increased lightweighting from the structures for increased range and efficiency.

Due to these extraordinary demands posed by next generation vehicles, composites are being considered seriously for the next level of lightweighting.

Automotive LightweightingPotential lightweighting materials over current steel:

High strength steels: 10 to 15%, Glass fiber composites: 20 to 30%,Aluminum : 25 to 35%,Magnesium 35 to 50%,Carbon fiber composites: 50 to 65%.

Composites have a major role to play in mixed material strategies for lightweighting.

Composites in the Vehicle Body

Closures – several applications are already on record using sheet molding compounds (SMC) and continuous carbon fiber thermosets.

Underbody – few applications are seen in the industry – Example, I3.

Structure – in I3 and I7, race cars and some premium vehicles.

Challenges for Composites in Automotive Structures

Multiple Design options – fiber types, matrix, etc.

Multiple process options – RTM, compression molding

Performance requirements - crash, durability

Variability - material, process, joining, final performance

Numerical models to predict accurately the behavior of composites is the key and it will only meet the automotive design time lines. This will only enable large scale implementation of composites in structural areas of automobile!

Predictive Models for Composites• Manufacturing • Structural performance• Stochastics Manufacturing has a strong influence on the performance.

An integrated approach for modeling, including both manufacturing and structural performance, will optimize the design.

Stochastic manufacturing engine(PAM-RTM, PAM-

FORM, PAM-DISTORTION)

Optimization for mass, cost and performance

(Isight)

Stochastic performance engine(LS-DYNA)

PDF of Manufacturing

material property data

PDF of process parameters

PDF of Mechanical

property input data

PDF of fill time, fiber angles,

residual stress, degree of cure.

Multiscale Designer

PDF of performance

Input

output

Integrated Process Flow for Accurate Predictive Tools – GM Development

Manufacturing output is critical input for structural simulation

Variability is accounted for by working with the probability distribution functions of the data

ICME Components - Composites

Materials Process

Design

Experiments, mechanics theory, computational models are integrated underICME framework.

Fluid infusion in porous mediaFluid viscosityThermal effectsResin curingAir pockets, residual stressesFiber angle changes

Fracture criteriaProgressive failure modelsSimulation process

FiberResin Fiber sizingTow architecturesPrepregThermoplastic sheetMaterial characterization

Manufacturing MethodsThermosets:• Resin transfer molding – a) traditional b) C-RTM c) HP-RTM• Prepreg compression molding• Filament winding• Pultrusion• Sheet molding compoundsThermoplastics• Compression molding• Tape Laying/Compression Molding

Resin transfer molding and prepreg compression molding are high volumemanufacturing methods for automotive composites considered.

Modeling Challenges For HP-RTMDraping• Non-crimp fabric behavior including stitch behaviorInjection• Coupled fluid flow, heat transfer from exotherm, resin viscosity and curing changes• Fluid structure coupling at high injection pressures• Permeability change as fabrics shearCuring• Polymer curing, exothermDemolding• Structural problem under residual stresses – mismatch of CTE of fiber and resin,

cooling down

HP-RTM• Injection strategy should enable the use of fast curing resin (fill times

Structural Performance Models –Multiscale Models

Fibrous Woven

NCF

Chopped 2D Chopped 3D

Random

Square LatticeHoneycomb

Lattice Plain Weave 8 Harness Weave4 Harness Weave

Multi-scale Approach

Nonlinear plasticity of resinReduced Order Models – Unique development

An Impact Beam – Sled Test

Top View

Side ViewTop View

Intrusion Beam (Sled Test): Comparison of Simulation and experiment

ExperimentSimulation

Intrusion Beam (Sled Test)

Force-Displacement

Very good match given thecomplexity of the shape and test parameters

Damage Comparison (Sled Test)

Expe

rimen

t

Sim

ulat

ion

Top

Bottom

Side

Stochastic Models• Variability is endemic in composites – from materials,

process, structural design, to performance.• Understanding the variability influencing the final

performance is critical in the design, monitoring.• From modeling side, variability of each of the

parameter increases the computational expense enormously.

• Very few developments to date are available!

Stochastic Models• GM and USC are working together to develop state of

the art models to include stochastic parameters for both manufacturing and structural performance.

• Polynomial chaos expansion (PCE) methods were used to create the developments and several problems were evaluated. These are computationally efficient compared to Latin Hypercube and Mano-Carlo Methods.

Stochastic Manufacturing - Draping, Injection, Curing and Demolding

A total of 74 variables were considered in this integrated problem. This problem is computationally challenging and was solved using state of the art methods developed in this project at USC.

Draping Injection Curing Distortion

Stochastic Variables

Initial orientation of fabric (5D)Elasticity of fibers (4D)Draping process (2D)

Mechanical and thermal properties of resins (18D)Temperature boundary conditions (3D)

FiberOrientations, permeabilityas function ofOrientation

Parameters dictating the curing kinetics (8D)Temperature cycle (1D)

Ply mechanical, thermal, and chemical properties (33)

Specially Varying Fields –Model Development Elasto-plastic response

𝜎𝜎 = 𝐾𝐾 𝜀𝜀𝑛𝑛

• Spatial correlation length corresponds to RVE of unit cell or microstructure• Models are still computationally efficient!

𝑛𝑛

Extension of Simulation Models• Accurate simulation models not only provide ways to

optimize the design, manufacturing process, maximize the through-put, etc., but also provide a clear picture of business case of cost modeling.

• Cost modeling utilizes essential features of simulation such as process planning, operation, required tooling, and labor. This provides an upfront understanding of the business case.

Cost Modeling

Crucial data from process simulation

• Process steps• Preform size• Cycle time• Press selection

Empirical + Experience

• Die cost based on volume• Automation• Floor space• % of scrap• downtime• Press allocation• maintenance

Software – Siemens PlantSim(very recent software in development)

Software – Siemens PlantSim4 major components of the an assembly

Cutting/Lay-upOvens for Pre-forming (2 molds per oven)

Preform trimming

Final RTM Molding Press

Final Trim

Final Assembly

Conclusion

• Composites offer significant lightweightingopportunities that are not yet completely explored.

• Development of predictive tools for composites is a must for large scale implementation to achieve the next level of lightweighting.

• Significant efforts are ongoing to improve the predictability of simulations models.

Acknowledgments• Satvir Aashat• Ali Seyed Yaghoubi• Gurpinder Dhaliwal• Selina Zhao• Praveen Pasupuleti• Roger Ghanem• Jacob Fish

Composite Materials Simulation and Modeling for Enhanced Lightweight� Body StructuresOutlineMass Breakup of an AutomobileMass Breakup of an AutomobileAutomotive LightweightingComposites in the Vehicle BodyChallenges for Composites in Automotive StructuresPredictive Models for CompositesSlide Number 9ICME Components - CompositesManufacturing MethodsModeling Challenges For HP-RTMSlide Number 13Structural Performance Models – Multiscale ModelsAn Impact Beam – Sled Test Intrusion Beam (Sled Test): Comparison of Simulation and experimentIntrusion Beam (Sled Test)Damage Comparison (Sled Test) Stochastic ModelsStochastic ModelsStochastic Manufacturing - Draping, Injection, Curing and DemoldingSlide Number 22Extension of Simulation ModelsCost Modeling Slide Number 25ConclusionAcknowledgments