Influence of processing parameters on the impact behaviour of glass/polyamide-6 composite

Balakrishnan, Venkateswaran Santhanakrishnan; Wartig, Kevin; Tsombanis, Nikolas; Seidlitz, Holger

doi:10.1016/j.compositesb.2018.09.064

Cited by 16 publications

(3 citation statements)

References 32 publications

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“…PA6 was chosen as polymer matrix due to its very good mechanical properties, excellent sliding, and wear characteristics, which make it widely used in automotive parts, [ 23 ] electronic, and electrical devices. [ 24 ] BN particles having large average dimension (45 μm) and a wide size range (from 2 to 122 μm) was selected as thermally conductive filler.…”

Section: Introductionmentioning

confidence: 99%

A comparison of thermally conductive polyamide 6‐boron nitride composites produced via additive layer manufacturing and compression molding

et al. 2021

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Thermally conductive but electrically insulating polymer matrix composites are of great interest in many engineering applications such as thermal management applications (i.e., heat sinks in microelectronic packaging, cooling of mechatronic components and mechanisms). Among possible functional fillers, hexagonal boron nitride seems to be one of the most promising choice due to its high thermal conductivity, and in this work, it was used as filler of polyamide 6 matrix. The resulting composites were first prepared in the form of pellets, extruded into filaments, and successively 3D printed, with the aim to study and compare the thermal performances of the 3D printed samples to the ones manufactured using traditional compression molding technique. The thermal conductivity of 25% wt and 45% wt loaded compression molded samples were 1 and 2.5 W/mK, respectively, while the neat polymer showed a thermal conductivity of 0.29 W/mK. 3D printed samples showed the same trend, namely increasing thermal conductivity with filler amount, despite a great influence of the 3D printing processing parameters (i.e., melt rheology and printing temperature), which lower the resulting thermal conductivity. 3D printed 25% and 45% by weight loaded samples showed a through plane thermal conductivity of 0.67 and 0.92 W/mK, respectively, and in‐plane thermal conductivity of 0.96 and 1.52 W/mK due to the presence of porosity and filler alignment along the printing direction.

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Section: Introductionmentioning

confidence: 99%

A comparison of thermally conductive polyamide 6‐boron nitride composites produced via additive layer manufacturing and compression molding

et al. 2021

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“…The lower density of reinforcement (2.1 gm•cm -3 ) compared with the plain matrix (2.7 gm•cm -3 ) would cause a decrease in the theoretical density of the composites. The difference in theoretical and experimental densities is attributed to the sintering defects, contents of the filler and sintering temperature [23,24]. In fact, GNP formed at grain boundaries would interrupt the grain boundary diffusion during the sintering process, therefore reduced the potential of bonding and resulted in lower sinter densities in the composites.…”

Section: Density Porosity and Vickers Hardness Assessmentmentioning

confidence: 99%

Tribological properties of Al-GNP composites at elevated temperature

Poudel,

Bajwa,

Xia

et al. 2024

Friction

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Lighter and more powerful next generation vehicles and other rotary machinery demand bearings to operate in harsher conditions for higher efficiency, and the continuous development of advanced low-wear and friction materials is thus becoming even more important to meet these requirements. New aluminium composites reinforced with high performance lubricate phases such as graphene nanoplatelets (GNPs) are very promising and have been vigorously investigated. By maintaining a low coefficient of friction (COF) and offering great strength against wear due to their self-lubricating capability, the solid lubricant like GNPs protect the bearing surface from wear damage and prevent change in metallurgical properties during temperature fluctuations. This paper first studies the high-temperature tribological performance of aluminium matrix composites reinforced with GNP, consolidated via powder metallurgy, then elucidates their tribological mechanism. We report that the best tribological performance is achieved by the composite containing 2.0 wt% GNP, with an extraordinarily low COF of 0.09 and a specific wear rate of 3.5×10−2 mm3·N−1·m−1, which represent 75% and 40% reduction respectively, against the plain aluminium consolidated under identical conditions. The in-track and out-of-track Raman analysis have confirmed the role of GNPs in creating a tribofilm on the counterpart surface which contributed to the excellent performance.

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“…Examples of such materials are: aluminum–aluminum oxide [1], titanium–titanium boride [2], aluminum–titanium carbide [3], cooper–titanium carbide [4] and many others. Moreover, the properties of composite constituents may change during the material processing [5,6,7]. Extraction of specimens of single phase from the composite for ex situ mechanical testing is cumbersome; therefore, in order to measure the local material properties, advanced experimental techniques like for example nanoindentation [8,9] or micropillar compression [10,11] have been developed.…”

Section: Introductionmentioning

confidence: 99%

Inverse Identification of Elastic Properties of Constituents of Discontinuously Reinforced Composites

Ogierman

2018

Materials

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This paper is devoted to determination of elastic properties of composite constituents by using an inverse identification procedure. The aim of the developed identification procedure is to compute the elastic constants of individual material phases on the basis of known properties of composite materials. The inverse problem of identification has been solved by combining an evolutionary algorithm with a micromechanical model. The paper also focuses on selection of a suitable micromechanical model for optimization which should ensure a compromise between accuracy and complexity. Two different cases have been studied: composite reinforced with short cylindrical fibers and composite reinforced with cubic particles. Moreover, Monte Carlo simulations have been carried out to expose a difference in outcome of identification which may occur when uncertain input data is considered. Obtained results show that identification is successful only when properties of composite materials with at least two different volume fractions of the reinforcement are known.

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Influence of processing parameters on the impact behaviour of glass/polyamide-6 composite

Cited by 16 publications

References 32 publications

A comparison of thermally conductive polyamide 6‐boron nitride composites produced via additive layer manufacturing and compression molding

A comparison of thermally conductive polyamide 6‐boron nitride composites produced via additive layer manufacturing and compression molding

Tribological properties of Al-GNP composites at elevated temperature

Inverse Identification of Elastic Properties of Constituents of Discontinuously Reinforced Composites

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