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Machine learning assisted optimization of blending process of polyphenylene sulfide with elastomer using high speed twin screw extruder.

ABSTRACT: Random forest regression was applied to optimize the melt-blending process of polyphenylene sulfide (PPS) with poly(ethylene-glycidyl methacrylate-methyl acrylate) (E-GMA-MA) elastomer to improve the Charpy impact strength. A training dataset was constructed using four elastomers with different GMA and MA contents by varying the elastomer content up to 20 wt% and the screw rotation speed of the extruder up to 5000 rpm at a fixed barrel temperature of 300 °C. Besides the controlled parameters, the following measured parameters were incorporated into the descriptors for the regression: motor torque, polymer pressure, and polymer temperatures monitored by infrared-ray thermometers installed at four positions (T1 to T4) as well as the melt viscosity and elastomer particle diameter of the product. The regression without prior knowledge revealed that the polymer temperature T1 just after the first kneading block is an important parameter next to the elastomer content. High impact strength required high elastomer content and T1 below 320 °C. The polymer temperature T1 was much higher than the barrel temperature and increased with the screw speed due to the heat of shear. The overheating caused thermal degradation, leading to a decrease in the melt viscosity and an increase in the particle diameter at high screw speed. We thus reduced the barrel temperature to keep T1 around 310 °C. This increased the impact strength from 58.6 kJ m-2 as the maximum in the training dataset to 65.3 and 69.0 kJ m-2 at elastomer contents of 20 and 30 wt%, respectively.

SUBMITTER: Takada S

PROVIDER: S-EPMC8674312 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The practical application of polymer composites in the electronic and communications industries often requires multi-properties, such as high thermal conductivity (TC), efficient electromagnetic interference (EMI) shielding ability with low electrical conductivity, superior tribological performance, reliable thermal stability and excellent mechanical properties. However, the integration of these mutually exclusive properties is still a challenge, ascribed to their different requirement on the incorporated nanofillers, composite microstructure as well as processing process. Herein, a well-designed boron nitride nanosheet (BN)/graphene nanosheet (GNP)/polyphenylene sulfide (PPS) composite with a dual-segregated structure is fabricated via high-pressure molding. Rather than homogenous mixing of the hybrid fillers, GNP is first coated on PPS particles and followed by encapsulating the conductive GNP layers with insulating BN, forming a BN shell-GNP layer-PPS core composite particles. After hot-pressing, a dual segregated structure is constructed, in which GNP and BN are distinctly separated and arranged in the interfaces of PPS, which on the one hand gives rise to high thermal conductivity, and on the other hand, the aggregated BN layer can act as an “isolation belt” to effectively reduce the electronic transmission. Impressively, high-pressure is loaded and it has a more profound effect on the EMI shielding and thermal conductive properties of PPS composites with a segregated structure than that with homogenous mixed-structure composites. Intriguingly, the synergetic enhancement effect of BN and GNP on both thermal conductive performance and EMI shielding is stimulated by high pressure. Consequently, PPS composites with 30 wt% GNP and 10 wt% BN hot-pressed under 600 MPa present the most superior comprehensive properties with a high TC of 6.4 W/m/K, outstanding EMI SE as high as 70 dB, marvelous tribological performance, reliable thermal stability and satisfactory mechanical properties, which make it promising for application in miniaturized electronic devices in complex environments.

Dataset Information

Machine learning assisted optimization of blending process of polyphenylene sulfide with elastomer using high speed twin screw extruder.

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