Investigating the Influence of Material Extrusion Rates and Line Widths on FFF-Printed Graphene-Enhanced PLA

Fused filament fabrication (FFF) is a widely used additive manufacturing process that can produce parts from thermoplastics. Its ease of operation and wide variety of materials make it a popular choice for manufacturing. To leverage such benefits, the commonly used thermoplastics (e.g., PLA and ABS) are impregnated with nanoparticles, short or continuous fibers, and other additives. The addition of graphene nanoplatelets to PLA makes for a high-quality filament possessing enhanced mechanical, electrical, and thermal properties. Even with the advancement in materials, the optimisation of the process parameter remains the most complex aspect for FFF. Therefore, this study investigates the influence of two under-researched and overlooked processing parameters (material extrusion rates and line widths) on commercially available graphene-enhanced PLA (GPLA). Nine different material extrusion rates (70% to 150%) and five different line widths (0.2 mm to 1 mm) were used to manufacture GPLA specimens using a low-cost, desktop-based 3D printer, as per British and international standards. The study analyses the influence of these two processing parameters on mass, dimensional accuracy, surface texture, and mechanical properties of GPLA specimens. A non-destructive test has also been conducted and correlated with three-point flexural test to establish its applicability in evaluating flexural properties of GPLA. The results how that small line widths provide more accuracy with longer print times whereas large line widths offer more strength with shorter printing times. Increase in material extrusion rates adversely affect the surface finish and hardness but positively influence the flexural strength of GPLA specimens. The study shows that the manipulation of material extrusion rates and line widths can help designers in understanding the limitations of the default printing settings (100% material extrusion rate and 0.4 mm line width) on most desktop 3D printers and identifying the optimal combination to achieve desired properties using the FFF process

Non-Destructive and Destructive Testing to Analyse the Effects of Processing Parameters on the Tensile and Flexural Properties of FFF-Printed Graphene-Enhanced PLA

The significance of non-destructive testing (NDT) methods cannot be overstated as they help to evaluate the properties of a material without damaging/fracturing it. However, their applicability is dependent on their ability to provide reliable correlation with destructive tests such as tensile and flexural. This correlation becomes more problematic when the material is not homogeneous, such is the case with parts manufactured using a popular additive manufacturing process termed as fused filament fabrication (FFF). This process also requires optimisation of its parameters to achieve desired results. Therefore, this study aims to investigate the effects of four different nozzle temperatures, print bed temperatures, and print speeds on FFF-printed Haydale’s Synergy Graphene Enhanced Super Tough PLA through three non-destructive (ultrasonic, hardness, strain) and two destructive (tensile, flexural) testing methods. Samples were manufactured using Anet® ET4 Pro 3D printer and evaluated as per British and International standards. Two non-destructive tests, i.e., ultrasonic and hardness have been associated with evaluating the tensile properties of the manufactured parts. These results were correlated with destructive tensile testing and showed good agreement. The NDT method of strain measurement showed a very good correlation with the destructive three-point flexural test and was able to provide a reliable evaluation of flexural properties as a function of all three processing parameters. The results presented in this work highlight the importance of NDT methods and how they can be used to evaluate different properties of a material

Investigating the Effects of Ironing Parameters on the Dimensional Accuracy, Surface Roughness, and Hardness of FFF-Printed Thermoplastics

Ironing is a useful feature for parts made by fused filament fabrication (FFF), as it can smooth out surfaces using heat and extruding a small amount of material. Like any other processing parameter for FFF, ironing also requires optimisation to ensure a smooth surface can be achieved with limited adverse effects on the other features of the printed part. Even with such a beneficial use case, ironing is still considered experimental and, therefore, this study aims to investigate its effects on dimensional accuracy, surface roughness, and the hardness of two commonly used amorphous thermoplastics, i.e., ABS (acrylonitrile butadiene styrene) and ASA (acrylonitrile styrene acrylate). An extensive comparative analysis has been provided where parts have been manufactured using a low-cost, desktop-based 3D printer, with the two materials at three different ironing line spacings (0.1 mm, 0.2 mm, 0.3 mm), three different ironing flows (10%, 20%, 30%), and three different ironing speeds (50 mm/s, 100 mm/s, 150 mm/s). The study focuses on evaluating the effects of these different ironing parameters and determining the optimal combination for bespoke product requirements. The results showed that ASA was more adversely affected by the changes in ironing parameters compared to ABS. However, the different ironing parameters were proven to improve the smoothness as well as hardness of the parts, compared to the un-ironed samples of ABS and ASA. This work provides a good comparison between two popular amorphous materials and offers ways to leverage ironing parameters to achieve dimensional accuracy, optimal surface finish, and better hardness values

Finite Element Modeling and Mechanical Testing of Metal Composites Made by Composite Metal Foil Manufacturing

Foils of aluminum 1050 H14 ½ hard temper and 99.9% copper with 500-micron thickness have been used to manufacture similar and dissimilar composites by composite metal foil manufacturing (CMFM). The metal foils are bonded to each other using a special 80% zinc and 20% aluminum by weight brazing paste. A 3D finite element model has been developed to numerically analyze the time required to heat the metal foils so that a strong bond can be developed by the paste. The numerical simulations run in ANSYS 19.1 have been validated through experiments and rectangular layered composite products have been developed for flexural testing. The flexural test results for layered Al and Al/Cu composites are compared with solid samples of Al 1050 and 99.9% pure copper made by subtractive method. The results show that the layered Al composite is 5.2% stronger whereas the Al/Cu sample is 11.5% stronger in resisting bending loads compared to a solid Al 1050 sample. A higher bend load indicates the presence of a strong intermetallic bond created by the brazing paste between the metal foils. Corrosion testing was also carried out on the composite samples to assess the effect of corrosion on flexural strength. The tests revealed that the composites made by CMFM are not affected by galvanic corrosion after 7 days of testing and the flexural loads remained consistent with composites that were not immersed in a solution of distilled water and NaCl

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

Combining Digital Twin and Machine Learning for the Fused Filament Fabrication Process

In this work, the feasibility of applying a digital twin combined with machine learning algorithms (convolutional neural network and random forest classifier) to predict the performance of PLA (polylactic acid or polylactide) parts is being investigated. These parts are printed using a low-cost desktop 3D printer based on the principle of fused filament fabrication. A digital twin of the extruder assembly has been created in this work. This is the component responsible for melting the thermoplastic material and depositing it on the print bed. The extruder assembly digital twin has been separated into three simulations, i.e., conjugate convective heat transfer, multiphase material melting, and non-Newtonian microchannel. The functionality of the physical extruder is controlled by a PID/PWM circuit, which has also been modelled within the digital twin to control the virtual extruder’s operation. The digital twin simulations were validated through experimentation and showed a good agreement. After validation, a variety of parts were printed using PLA at four different extrusion temperatures (180 °C, 190 °C, 200 °C, 210 °C) and ten different extrusion rates (ranging from 70% to 160%). Measurements of the surface roughness, hardness, and tensile strength of the printed parts were recorded. To predict the performance of the printed parts using the digital twin, a correlation was established between the temperature profile of the non-Newtonian microchannel simulation and the experimental results using the machine learning algorithms. To achieve this objective, a reduced order model (ROM) of the extruder assembly digital twin was developed to generate a training database. The database generated by the ROM (simulation results) was used as the input for the machine learning algorithms and experimental data were used as target values (classified into three categories) to establish the correlation between the digital twin output and performance of the physically printed parts. The results show that the random forest classifier has a higher accuracy compared to the convolutional neural network in categorising the printed parts based on the numerical simulations and experimental data

Investigating the effects of extrusion temperatures and material extrusion rates on FFF-printed thermoplastics

Fused filament fabrication (FFF) is one of the most widely used additive manufacturing processes in the market. It is based on material extrusion and utilises thermoplastic materials to manufacture bespoke products. The process is extremely popular due to its ease of operation and variety of available materials. To enhance the mechanical performance of parts made by FFF, reinforcements including nanoparticles, short or continuous fibres, and other additives have been added to commonly used thermoplastics such as acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA). Such new materials require optimisation of process parameters to achieve the desired results. One such parameter is the material extrusion rate that can result in under- or over-extrusion leading to a variety of applications. In this study, PLA and HDPlas® PLA-GNP-A (PLA reinforced with functionalised graphene nanoplatelets) have been used to investigate the effects of material extrusion rate. An extensive comparative analysis has been provided where parts have been manufactured using a desktop 3D printer with the two materials at four extrusion temperatures (180 °C, 190 °C, 200 °C, and 210 °C) and ten different extrusion rates (ranging from 70 to 160%). The study aims to evaluate the effects of extrusion temperatures and material extrusion rates on mass, dimensional accuracy, surface texture, and mechanical properties of the two materials. Microstructural analysis has also been carried out to evaluate the surfaces of parts after manufacture as well as their fractured surfaces after mechanical testing to determine the impact of extrusion rate on failure modes. The results have shown that the graphene reinforced PLA material is affected more adversely by changes in material extrusion rate compared to PLA. This work provides a good comparison between two materials manufactured at four different extrusion temperatures and how the material extrusion rate can be leveraged to achieve optimal surface finish and mechanical strength

Artificial intelligence and duplex ultrasound for detection of carotid artery disease

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