The ductile forming process of a polymer in a standard screw extruder and pin-barrel extruder, equipped with or without a field synergy elongation screw, was investigated by the finite element method. In order to assess the mixing and heat transfer capabilities of screws, characteristic parameters such as the mixing efficiency, segregation scale, and temperature distribution of different structures were analyzed and compared. The results indicated that the flow pattern of the polymer melt in the extruder was significantly influenced by the screw structure and was improved by the newly designed field synergy screw configuration, which brought a desirable elongational flow to enhance the radial convection. This was attributed to the unique radial wedge-shaped repeated convergence region of the field synergy elongation screw, increasing the synergistic effect between the velocity field, velocity gradient field, and temperature gradient field and thus improving the heat transfer and mixing efficiency. After adding barrel pins, the flow was forced to split, resulting in a more significant stretching effect on the melt. The field synergy effect in the pin mixed region was strengthened, which further increased the heat and mass transfer efficiency of the screw. However, increasing barrel pins could also lead to undesired temperature fluctuation and flow resistance, which have a negative impact on the melt uniformity. This study offers an important reference for optimizing screw structure to obtain strong mixing and heat transfer performances.

This study combines multi-frequency dynamic mechanical analysis (DMA) with machine learning (ML) to characterize and predict the viscoelastic properties of a commercial polycarbonate/acrylonitrile–butadiene–styrene (PC/ABS) blend. DMA temperature sweeps at four frequencies (1–10 Hz) in single cantilever mode yielded a glass transition range of 115.8–$123.2$ °C ($E^{″}$ peak), frequency sensitivity of $7.18$ °C/$\mathrm{decade}$, and an apparent activation energy of $335\pm 85 \mathrm{k}\mathrm{J} {\mathrm{mol}}^{-1}$. Time–temperature superposition master curves were parameterized with a six-term Prony series ($R^2=0.998$). Four data-driven models (RF, XGB, SVR, MLP) and a physics-informed NeuralWLF model were evaluated through a hierarchical validation framework. Temperature-blocked CV ranked MLP ($\overline{R^2}=0.989$) above RF (0.950) for interpolation; LOFO validation revealed that NeuralWLF achieved the best cross-frequency generalization ($R^2>0.92$ for all targets) with interpretable WLF parameters ($C_1\approx 12.2$, $C_2\approx 51.7 °\mathrm{C}$). A systematic block size sweep (5–30 $°\mathrm{C}$) revealed a validation inflation effect in which MLP $tan\delta$$R^2$ dropped from 0.986 to 0.592 as the gap-to-FWHM ratio increased from 0.5 to 3.1, establishing the gap/FWHM ratio as a quantitative validation stringency criterion. A physics–data crossover was identified at gap/FWHM $\approx 2$: beyond this threshold, NeuralWLF outperformed all data-driven models in $tan\delta$ prediction by up to $+0.300$ in $R^2$, while curriculum learning (freezing the WLF layer for 300 epochs) further improved the most stringent 30 $°\mathrm{C}$ validation from $R^2=0.660$ to 0.731. The integrated framework demonstrates that honest evaluation of DMA–ML models requires validation gaps exceeding the characteristic feature width and introduces a quantifiable physics-data crossover criterion for selecting between data-driven and physics-informed architectures.

Carbon nanotubes (CNTs), renowned for their exceptional mechanical, electrical, and thermal properties, are regarded as the most promising multifunctional reinforcement filler for rubber composites. However, existing reviews on CNT‐reinforced composites predominantly focus on cementitious and metallic matrices, with few studies systematically addressing the unique challenges of “dispersion‐homogenization‐interface coupling” posed by the extreme viscosity, significant deformation, and complex cross‐linked networks inherent to rubber matrices. This review employs a systematic evaluation approach to comprehensively summarize recent advancements in CNT‐reinforced rubber‐based composites. Focusing on rubber matrices, this paper compares non‐thermal dispersion with thermal‐assisted dispersion techniques optimized for processing high‐viscosity elastomers, elucidates the unique multi‐scale synergistic reinforcement mechanism inherent to viscoelastic networks, and clarifies the intrinsic correlations between processing parameters and final performance attributes such as wear resistance, tear strength, and electromagnetic shielding capabilities.

Die swell is the dominant source of dimensional deviation in rubber profile extrusion. Because it is driven by recoverable elastic strain, a purely viscous baseline flow field cannot reproduce its speed dependence; a viscoelastic correction is required. This study presents, to the best of our knowledge, the first controlled comparison of a Carreau–Arrhenius baseline flow field against a fractional-order viscoelastic correction for carbon-black-filled EPDM across an industrial speed window. The viscoelastic correction (PyCFD-FMM) is a post-processing fractional-order viscoelastic swell correction built on the shared non-isothermal Polyflow Carreau–Arrhenius flow field, derived from a six-mode fractional Maxwell model parameterized from dynamic mechanical analysis via the Laun rule and closed through the Tanner recoverable-strain theory. Three carbon-black-filled EPDM compounds (Shore A 60–80) were extruded at four screw speeds (15–30 rpm) under instrumented conditions. Experimentally, swell ratios of 1.12–1.15 increase monotonically with screw speed (Fisher-combined $p=0.007$; measurement repeatability CV $\le 0.27$% across $n=4$ replicates per condition). The purely viscous baseline output gives a decreasing apparent swell–speed trend—opposite to experiment—whereas PyCFD-FMM recovers the correct increasing trend for all compounds. Under single-anchor hold-out evaluation at 20/25/30 rpm, the non-anchor MAPE decreases from 0.99% for the baseline flow-field output to 0.30% (PyCFD-FMM); an anchor-sensitivity check over all four rpm choices keeps the compound-averaged non-anchor MAPE within 0.27–0.39% and preserves the correct slope sign in every case. Swell decomposition into geometric baseline and net correction factor ($B_{\mathrm{PyCFD}}=B_{\mathrm{geom}}\times f_{\mathrm{corr}}$) confirms that the viscous baseline flow field captures flow-geometry effects but carries no elastic memory. Within the tested window, the viscoelastic correction meets a dual-gate criterion—correct slope sign and reduced non-anchor MAPE—which the purely viscous baseline cannot satisfy by construction.

Filled elastomers often exhibit a low-frequency power-law storage modulus (G-prime), yet quantitative links between molecular interfacial structure and macroscopic reinforcement remain unresolved. This gap is addressed using a hierarchical multiscale framework that integrates coarse-grained molecular dynamics (MD) and dynamic mechanical analysis (DMA). Overall, MD contributes transferable structural descriptors rather than direct macro-rheology prediction. MD simulations yield a bound-layer scaling relation for chain length $N=50$ in coarse-grained simulations serving as a structural probe. For EPDM master curves, the single-phase fractional Maxwell model is statistically preferred (Delta AICc > 147, n = 56), reflecting limited statistical power; larger datasets (e.g., PC/ABS, n = 952) favor the dual-phase formulation. For cross-scale prediction, an MD-derived effective-volume-fraction baseline (MAPE = 54.1%) provides a structural prior; the regime-partitioned bridge model absorbs relaxation physics not resolved at the MD scale, reducing error to 7.3% (blocked-CV MAPE = 9.5%, with a 2.3% fold-to-fold spread). Linear-viscoelastic constraints improve nonlinear PTT calibration, reducing die-swell error by 87%.