Hybrid Manufacturing and Experimental Testing of Glass Fiber Enhanced Thermoplastic Composites

Additive Manufacturing (AM) is gaining enormous attention from academic and industrial sectors for product development using different materials. Fused Deposition Modelling (FDM) is a popular AM method that works with thermoplastics. This process offers benefits of customisation both in terms of hardware and software in the case of desktop-based FDM systems. Enhancement of mechanical properties for the traditional thermoplastic material is a widely researched area and various materials have been added to achieve this goal. This paper focuses on the manufacture of glass fiber reinforced plastic (GFRP) composites using Hybrid Fused Deposition Modelling (HFDM). Commonly available polylactic acid or polylactide (PLA) material was inter-laced with 0.03 mm thick glass fiber sheets to manufacture GFRP products followed by tensile testing. This was done to investigate whether adding more layers increases the tensile strength of the GFRP products or not. Furthermore, the maximum number of glass fiber layers that can be added to the 4 mm thick specimen was also identified. This was done to demonstrate that there is an optimal number of glass fiber layers that can be added as after this optimal number, the tensile strength start to deteriorate. Microstructural analysis was undertaken after tensile testing followed by ultrasonic testing to assess the uniformity of the GFRP composites

Experimental and numerical analysis of 3D ODF and fibre crimp of nonwovens

Experimental and numerical analysis of 3D ODF and fibre crimp of nonwovens</p

Modelling of nonwoven fibrous materials: Orientation distribution function and fiber crimp [Abstract]

Modelling of nonwoven fibrous materials: Orientation distribution function and fiber crimp [Abstract]</p

Assessing Crimp of Fibres in Random Networks with 3D Imaging

The analysis of fibrous structures using micro-computer tomography (µCT) is becoming more important as it provides an opportunity to characterise the mechanical properties and performance of materials. This study is the first attempt to provide computations of fibre crimp for various random fibrous networks (RFNs) based on µCT data. A parametric algorithm was developed to compute fibre crimp in fibres in a virtual domain. It was successfully tested for six different X-ray µCT models of nonwoven fabrics. Computations showed that nonwoven fabrics with crimped fibres exhibited higher crimp levels than those with non-crimped fibres, as expected. However, with the increased fabric density of the non-crimped nonwovens, fibres tended to be more crimped. Additionally, the projected fibre crimp was computed for all three major 2D planes, and the obtained results were statistically analysed. Initially, the algorithm was tested for a small-size, nonwoven model containing only four fibres. The fraction of nearly straight fibres was computed for both crimped and non-crimped fabrics. The mean value of the fibre crimp demonstrated that fibre segments between intersections were almost straight. However, it was observed that there were no perfectly straight fibres in the analysed RFNs. This study is applicable to approach employing a finite-element analysis (FEA) and computational fluid dynamics (CFD) to model/analyse RFNs

Algorithm to determine orientation distribution function from microscopic images of fibrous networks: Validation with X-ray microtomography

Quantitative analysis of fibre orientation in a random fibrous network (RFN) is important to understand their microstructure, properties and performance. 2D fibre orientation distribution presents an in-plane fibre orientation without any information on fibre orientation in thickness direction. This research introduces a fully parametric algorithm for computing 3D fibre orientation as thickness is important for high-density or thick fibrous networks. The algorithm is tested for 3 major classes of nonwoven fabrics called low- (L), medium- (M) and high-density (H) ones. H fabric density is 6–8 times larger than the L fabric density. M fabric density (traditional intermediate fabric density) is 3–4 times larger than the L fabric density. Voxel models of experimental nonwoven webs were generated by an X-ray micro-CT (µCT) system and evaluated with the algorithm. Statistical results showed that a fraction of fibres orientated along the thickness direction increases as fibre density grows. To validate the accuracy of findings, deterministic voxelated virtual fibrous structures, created using mathematical functions were used. This novel algorithm is able to produce a 3D orientation distribution function (ODF) for any RFN including, models of nonwovens produced with various manufacturing parameters, experimentally verified and validated with X-ray µCT. Also, it can compute 2D ODFs of various types of RFNs to evaluate 2D behaviour of fibrous structures. The obtained results are useful for applications in many fields including finite element analysis, computational fluid dynamics, additive manufacturing, etc