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Performance Modeling of Polymer Composites

Performance Modeling of Polymer Composites. Pete Foss Materials and Processes Lab General Motors Research and Development Center. GM. DELPHI VISION.

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Performance Modeling of Polymer Composites

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  1. Performance Modeling of Polymer Composites Pete Foss Materials and Processes Lab General Motors Research and Development Center

  2. GM DELPHI VISION PREDICT DIMENSIONAL AND MECHANICAL PERFORMANCE OF POLYMER COMPOSITES BY UTILIZING PROCESS SIMULATIONS TO PREDICT MICROSTRUCTURE AND THUS PROPERTIES FOR ANY PART GEOMETRY

  3. Areas of Interest - Thermoplastics • Short fiber reinforced thermoplastics • Fiber orientation measurements and predictions by commercial codes (Moldflow, Moldex, Sigmasoft) • Utilization of microstructural predictions coupled to FEA to predict mechanical performance including dimensions, strength, stiffness, fatigue and creep. • Currently use nonlinear orthotropic plaque data in conjunction with 2D flow analysis to predict performance. • Future would like to use 3D but must include material nonlinearity. • Long fiber reinforced thermoplastics • Prediction of initial mechanical properties • Long term durability • Rate dependence

  4. Areas of Interest - Thermosets • Automotive Composites Consortium • Crash modeling of composites. • Material properties for durability predictions. • Carbon fiber cost reduction • Class A structural composites • Paintability of SMC • Cost effective low volume solutions • Glass fiber • Carbon fiber

  5. Barriers To Expanded Use Of Composites • Cosmetic • Powder prime • SMC and RRIM are incompatible with the powder prime at our assembly plants. • Need materials that “Paint like steel”. • CLTE • Thermal expansion coefficient of thermoplastics forces inter panel gaps to be much larger than steel. • Structural • Cost vs. mass savings • Predictability of crash behavior • Automotive is fairly unique in that we must model progressive damage behavior not just failure initiation. • Assembly and joining methods

  6. Gaps • Accuracy of 2 and 3D fiber orientation predictions • Short fiber injection molding • Long fiber injection molding • Long fiber extrusion-compression molding • 3D structural analysis • Current solvers cannot handle millions of materials - one for every element • Rate dependence • Data, material models, testing procedures • Progressive damage material models • Software validation

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