In-mold rheological characterization

Accurate modeling of the rheological behavior, including the viscous and frictional behavior, is essential for reliable compression molding simulation. However, no standard approach is yet acknowledged. Simutence has developed and provides in-mold rheological characterization to account for the specificities of SMC, LFT, and GMT materials during compression molding.

Initial charge forming

Most simulation approaches do not capture the forming of complex initial charge configurations before the onset of material flow. SimuFill enables a sequential approach to predict material forming and material flow to account for complex initial charge configurations. Consideration and optimization of the initial charge configurations ensure a complete mold filling and the reduction of processing time.

Defect prediction

Combining our advanced material models with a process-oriented material card of the SMC, LFT, or GMT material allows for accurate prediction of the flow front progression and potential unfilled areas. In this context, virtual studies support efficient process optimization and identification of suitable process parameters to ensure a complete filling of the mold.

Press tonnage optimization

Press tonnage is critical for manufacturing and a cost-driver for capital invest. The complex material behavior, design iterations, and processing strategies hugely affect the required process tonnage. Molding simulation with SimuFill enables reliable estimation of process forces and thus is key to reduce capital invest.

Fiber orientation prediction

SimuFill is fully integrated into our virtual process chain, enabling a downstream consideration of local fiber orientation, temperature, or degree of cure/crystallization in subsequent FEA.

Fiber orientation initialization for LFT strands

The initial fiber orientation significantly affects the final fiber orientation if the flow length is sufficiently short. In LFT strands, the local fiber orientation is inhomogeneous and determined through the extrusion process. SimuFill enables the initialization of the local fiber orientation in LFT strands using analytical equations.

Fiber interaction coefficients

Fiber orientation models such as Folgar-Tucker’s equation or the ARD-RSC model can be used to predict the change in fiber orientation due to material flow as a function of so-called fiber interaction coefficients. We support you in determining fiber interaction coefficients using µCT scans and subsequent fiber orientation analyses.

Long-fiber-reinforced thermoplastics (LFT)

Compression molding of LFT or tape LFT sandwiches are widespread processes to produce large-scale parts with high geometric complexity.

We provide advanced material models and support you in creating material cards for LFT and tape LFT sandwiches.

Our expertise includes the initialization of fiber orientation in the LFT strand, modeling the anisotropy of viscosity, and predicting the crystallization kinetics during molding.

Sheet molding compound (SMC)

Compression molding of SMC is an established process to produce large-scale parts with a high geometric complexity for applications at elevated temperatures.

We provide advanced material models and support you in creating material cards for SMC as well as SMC in combination with local reinforcements through prepregs. This is valid for both glass and carbon fiber-reinforced SMC.

Our expertise includes predicting the forming of complex initial charge configurations as the basis for accurate modeling of material flow using a sequential molding simulation approach. Modeling the anisotropy of viscosity enables a unified material modeling approach.

Glass mat thermoplastics (GMT)

Compression molding of GMT is an established process to produce low-cost parts with a low degree of geometric complexity and higher demand in mechanical properties compared to LFT.

We provide advanced material models and support you through material card creation for GMT materials.

Our expertise includes a sequential molding simulation approach covering the stages of material forming and material flow. Modeling the anisotropy of viscosity enables a unified material modeling approach. Based on this, forming defects, such as local wrinkling, and flow defects, such as incomplete mold filling, are predicted.

Viscosity

The viscosity is the most decisive material property defining the rheological behavior. We offer in the context of compression molding simulation the following state-of-art characterization approaches:

• Plate plate rheometer
  (LFT, GMT, SMC)
• Torsion bar rheometer
  (GMT)

Wall slip / mold friction

SMC molding goes along with a thin lubrication layer between the charge and the mold surface. Therefore, considering the wall slip respectively the friction between the material and the mold surface is crucial for accurate compression molding simulation with SMC materials.

We developed an in-mold characterization approach using a plaque mold equipped with pressure sensors for characterizing the frictional behavior along with the viscosity of the material. Our studies have proven that this approach is key for reliable compression molding simulation with SMC materials.

In-mold viscosity

In some projects, there is no time or budget to conduct rheometer tests to determine the viscosity. We developed an approach to extract the viscosity from plaque molding trials. Thus, possibly already existing data from plaque molding trials can be used to parameterize the viscosity. Only the time, force, and gap height signals are required.

Thermophysical properties

The evolution of temperature has a crucial influence on the flow properties. Moreover, the volumetric behavior plays a major role, especially in the packing phase. Therefore, accurately characterizing the thermophysical properties is key.

• Capillary rheometer for characterizing pvT (pressure-volume-temperature) behavior and heat conductivity of thermosets and thermoplastics
• DSC (Differential scanning calorimetry) for determination of the heat capacity of the polymer

Fiber interaction coefficients

Fiber orientation models such as Folgar-Tucker’s equation or the ARD-RSC model can be used to predict the change in fiber orientation due to material flow as a function of so-called fiber interaction coefficients. We support you in determining fiber interaction coefficients using µCT scans and subsequent fiber orientation analyses.

LFT strand fiber orientation

The initial fiber orientation significantly affects the final fiber orientation if the flow length is sufficiently short. In LFT strands, the local fiber orientation is inhomogeneous and determined through the extrusion process. We support you in determining the initial fiber orientation of LFT strands using µCT scans and subsequent fiber orientation analysis as a basis to validate our analytical equations to describe the initial fiber orientation in LFT strands for your extrudes.