Polylactic acid (PLA) is one of the highly applicable bio-polymers in a wide variety of applications including medical fields and packaging. In order to quantitatively model the mechanical behavior of PLA and PLA based bio-composite materials, and also tailor new bio-composites, it is required to characterize the mechanical behavior of PLA. In this study, thin films of PLA are fabricated via hot-pressing, and tensile experiments are performed under different strain rates. To model the mechanical behavior, an elasto-viscoplastic constitutive model, developed in a finite strain setting, is adopted and calibrated. Using the physically-based constitutive model, all regimes of deformation under uniaxial stress state, including post-yield softening, were adequately captured in the simulations. Also, the rate dependency of the stress–strain behavior was properly modelled.

The macroscopic response of short fiber reinforced composites (SFRCs) is dependent on an extensive range of microstructural parameters. Thus, micromechanical modeling of these materials is challenging and in some cases, computationally expensive. This is particularly important when path‐dependent plastic behavior is needed to be predicted. A solution to this challenge is to enhance micromechanical solutions with machine learning techniques such as artificial neural networks. In this work, a recurrent deep neural network model is trained to predict the path‐dependent elasto‐plastic stress response of SFRCs, given the microstructural parameters and the strain path. Micromechanical mean‐field simulations are conducted to create a database for training the validating the model. The model gives very accurate predictions in a computationally efficient manner when compared with independent micromechanical simulations.