Effects of carbon fibre reinforcement on the geometric properties of PETG-based filament using FFF additive manufacturing

Abstract

The increasing worldwide demand for high-quality on-demand products manufactured with flexible and efficient productions systems has led to the development additive manufacturing technologies (AM). One of the most popular AM technologies, is fused filament fabrication (FFF) due to its ability to manufacture complex parts using a broad range of thermoplastic polymers with low production costs. However, FFF still cannot compete with traditional manufacturing processes when it comes to producing high quality end-use products. To improve mechanical properties and geometric quality features of end-use products, researchers are developing new advanced filaments infused with nanoparticles , short and continuous fibres. In the search for enhanced materials, glycol-modified polyethylene terephthalate (PETG) filaments and PETG reinforced with carbon fibres (PETG-CF) have been developed for FFF, but the effects of the addition of these fibres on geometric properties have not been analysed. The main objective of this study is to evaluate the impact of fibre addition to PETG filaments on the geometric properties of FFF manufactured parts by analysing their dimensional accuracy, and surface roughness. The effect of the 3D printing parameter − speed, layer thickness, and build orientation – on the geometric behaviour were assessed. Artificial neural network based predictive models of geometric parameters of the two candidate PETG-based filaments were used to find optimal printing parameters. In general terms, the carbon fibre addition to PETG-based polymers negatively affected the dimensional accuracy, flatness, and surface roughness in most of the printing conditions, significantly reducing the printing parameter combinations where the optimal values were achieved. • The effect of carbon fibre addition on geometric properties of PETG was assessed. • The 3D-printing parameters influence on geometric behaviour was analysed. • ANNs were the optimal predictive models for geometric property characterisation. • In general, carbon fibre addition to PETG negatively affected geometric properties.