  In the paper, we present an integrated data-driven modeling framework based
on process modeling, material homogenization, mechanistic machine learning, and
concurrent multiscale simulation. We are interested in the injection-molded
short fiber reinforced composites, which have been identified as key material
systems in automotive, aerospace, and electronics industries. The molding
process induces spatially varying microstructures across various length scales,
while the resulting strongly anisotropic and nonlinear material properties are
still challenging to be captured by conventional modeling approaches. To
prepare the linear elastic training data for our machine learning tasks,
Representative Volume Elements (RVE) with different fiber orientations and
volume fractions are generated through stochastic reconstruction. More
importantly, we utilize the recently proposed Deep Material Network (DMN) to
learn the hidden microscale morphologies from data. With essential physics
embedded in its building blocks, this data-driven material model can be
extrapolated to predict nonlinear material behaviors efficiently and
accurately. Through the transfer learning of DMN, we create a unified
process-guided material database that covers a full range of geometric
descriptors for short fiber reinforced composites. Finally, this unified DMN
database is implemented and coupled with macroscale finite element model to
enable concurrent multiscale simulations. From our perspective, the proposed
framework is also promising in many other emergent multiscale engineering
systems, such as additive manufacturing and compressive molding.
