Many researchers have applied X-ray scattering, electronic microscopy, and atomic force microscopy to examine the strong dependencies among the mechanical properties of filler morphologies 2, 3, 4, 5, 6, 7, 8, 9. The filled rubber is a composite material made of polymers and fine filler nanoscale particles. Manufacturers are extremely interested in the properties of the filled-rubber 1 constituents of tires, because they are directly related to tire performances, such as rolling resistance, wear, and wet traction.
Afterwards, we extract the fillers that dominate the mechanical properties using the surrogate model and we confirm their validity using MD. The resultant surrogate model provides higher prediction accuracy than that trained only by images of the entire region. The images include fringe regions to reflect the influence of the filler constituents outside the core regions. To derive a highly accurate surrogate model using only a small amount of training data, we increase the number of training instances by dividing the large-scale simulation results into 3D images of middle-scale filler morphologies and corresponding regional stresses. The major difficulty when employing machine-learning-based surrogate models is the shortage of training data, contributing to the huge simulation costs. To alleviate this problem, we introduce a surrogate convolutional neural network model to achieve faster and more accurate predictions. Unfortunately, the computation time for a simulation can require several months’ computing power, because the interactions of thousands of filler particles must be calculated. Hybrid.Molecular dynamics (MD) simulation is used to analyze the mechanical properties of polymerized and nanoscale filled rubber. Reviewers should indicate in a review (i) any relevant published work that has not been cited by the authors, (ii) anything that has been reported in previous publications and not given appropriate reference or citation, (ii) any substantial similarity or overlap with any other manuscript (published or unpublished) of which they have personal knowledge.
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