Improving Numerical Modeling Accuracy for Fiber Orientation and Mechanical Properties of Injection Molded Glass Fiber Reinforced Thermoplastics

Abstract

Local fiber alignment in fiber-reinforced thermoplastics is governed by complex flows during the molding process. As fiber-induced material anisotropy leads to non-homogeneous effective mechanical properties, accurate prediction of the final orientation state is critical for integrated structural simulations of these composites. In this work, a data-driven inverse modeling approach is proposed to improve the physics-based structural simulation of short glass fiber reinforced thermoplastics. The approach is divided into two steps: (1) optimization of the fiber orientation distribution (FOD) predicted by the Reduce Strain Closure (RSC) model, and (2) identification of the composite’s mechanical properties used in the Ramberg–Osgood (RO) multiscale structural model. In both steps, the identification of the model’s parameters was carried out using a Genetic Algorithm. Artificial Neural Networks were used as a machine learning-based surrogate model to approximate the simulation results locally and reduce the computational time. X-ray micro-computed tomography and tensile tests were used to acquire the FOD and mechanical data, respectively. The optimized parameters were then used to simulate a tensile test for a specimen injection molded in a dumbbell-shaped cavity selected as a case study for validation. The FOD prediction error was reduced by 51% using the RSC optimized coefficients if compared with the default coefficients of the RSC model. The proposed data-driven approach, which calculates both the RSC coefficients and the RO parameters by inverse modeling from experimental data, allowed improvement in the prediction accuracy by 43% for the elastic modulus and 59% for the tensile strength, compared with the non-optimized analysis.

Efficient identification of a flow-induced crystallization model for injection molding simulation

“…All of the previously mentioned publications use a surrogate model to find optimal process parameters. However, another interesting utilization of surrogate modeling in injection molding was recently published by Ivan et al [74], where the surrogate model is used to identify two fiber orientation model parameters. The authors used experimental fiber orientation data obtained by micro-computed tomography to calibrate the fiber orientation model used in their high-fidelity injection molding simulation.…”

Single and Multiple Gate Design Optimization Algorithm for Improving the Effectiveness of Fiber Reinforcement in the Thermoplastic Injection Molding Process

“…The GDF formulation proved to be slightly superior (~5%) to the largely employed reduced strain closure (RSC) model proposed by Wang [ 20 ], normally coupled with the Anisotropic Rotary Diffusion (ARD) model [ 21 ], and successfully applied by Favaloro and Tucker [ 22 ] for the prediction of the FOD in the molding process of short FRC. The ARD-RSC model is easy to employ and calibrate and, for this reason, has been employed in various recent studies [ 11 , 14 , 17 , 23 ], and is also available within the Autodesk Moldflow Insight (AMI) 2023 environment [ 24 , 25 , 26 ], also employed in this research.…”