Fused Filament Fabrication Additive Manufacturing Research Articles

Essential work of fracture assessment of acrylonitrile butadiene styrene (ABS) processed via fused filament fabrication additive manufacturing

Experiments and finite element (FE) calculations were performed to study the raster angle–dependent fracture behaviour of acrylonitrile butadiene styrene (ABS) thermoplastic processed via fused filament fabrication (FFF) additive manufacturing (AM). The fracture properties of 3D-printed ABS were characterized based on the concept of essential work of fracture (EWF), utilizing double-edge-notched tension (DENT) specimens considering rectilinear infill patterns with different raster angles (0°, 90° and + 45/− 45°). The measurements showed that the resistance to fracture initiation of 3D-printed ABS specimens is substantially higher for the printing direction perpendicular to the crack plane (0° raster angle) as compared to that of the samples wherein the printing direction is parallel to the crack (90° raster angle), reporting EWF values of 7.24 kJ m−2 and 3.61 kJ m−2, respectively. A relatively high EWF value was also reported for the specimens with + 45/− 45° raster angle (7.40 kJ m−2). Strain field analysis performed via digital image correlation showed that connected plastic zones existed in the ligaments of the DENT specimens prior to the onset of fracture, and this was corroborated by SEM fractography which showed that fracture proceeded by a ductile mechanism involving void growth and coalescence followed by drawing and ductile tearing of fibrils. It was further shown that the raster angle–dependent strength and fracture properties of 3D-printed ABS can be predicted with an acceptable accuracy by a relatively simple FE model considering the anisotropic elasticity and failure properties of FFF specimens. The findings of this study offer guidelines for fracture-resistant design of AM-enabled thermoplastics.Graphical abstract

An Overview on Personal Protective Equipment (PPE) Fabricated with Additive Manufacturing Technologies in the Era of COVID-19 Pandemic.

Different additive manufacturing technologies have proven effective and useful in remote medicine and emergency or disaster situations. The coronavirus disease 2019 (COVID-19) disease, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, has had a huge impact on our society, including in relation to the continuous supply of personal protective equipment (PPE). The aim of the study is to give a detailed overview of 3D-printed PPE devices and provide practical information regarding the manufacturing and further design process, as well as describing the potential risks of using them. Open-source models of a half-face mask, safety goggles, and a face-protecting shield are evaluated, considering production time, material usage, and cost. Estimations have been performed with fused filament fabrication (FFF) and selective laser sintering (SLS) technology, highlighting the material characteristics of polylactic acid (PLA), polyamide, and a two-compound silicone. Spectrophotometry measurements of transparent PMMA samples were performed to determine their functionality as goggles or face mask parts. All the tests were carried out before and after the tetra-acetyl-ethylene-diamine (TAED)-based disinfection process. The results show that the disinfection has no significant effect on the mechanical and structural stability of the used polymers; therefore, 3D-printed PPE is reusable. For each device, recommendations and possible means of development are explained. The files of the modified models are provided. SLS and FFF additive manufacturing technology can be useful tools in PPE development and small-series production, but open-source models must be used with special care.

Plateau-Rayleigh instabilities in pore networks of additively manufactured polymers: A modeling perspective

Fused Filament Fabrication (FFF) additive manufacturing (AM) is a popular and widespread polymer AM method that provides rapid part production with relatively low cost prototyping. Polymeric structures made through FFF AM typically have internal pore structures which result from the manufacturing process. A Monte Carlo algorithm is implemented to simulate pore morphology evolution from build-path networks to spheroidal pores in AMed post-annealed structures. The model demonstrated that the evolution of these pore networks phenomenologically follows stages associated with the Plateau-Rayleigh instability and that thermal gradients result in migration of the spherical pores, as was observed experimentally.

Mechanical and Geometric Performance of PLA-Based Polymer Composites Processed by the Fused Filament Fabrication Additive Manufacturing Technique.

In this work, the effect of short carbon fibre (CF) on the mechanical and geometric properties of 3D printed polylactic acid (PLA) composite parts processed using the Fused Filament Fabrication (FFF) technique have been analysed. Tensile, flexural and interlaminar shear strength (ILSS) tests were performed to obtain the mechanical performance of the different samples. The surface quality and geometric accuracy of the printed specimens were also evaluated. Finally, Scanning Electron Microscope (SEM) images of the printed samples are analysed. The results revealed that the addition of carbon fibres effectively improved all assessed mechanical properties of PLA-CF composites as compared to the neat PLA. In particular, Flat PLA-CF samples showed an average increase in tensile performance of 47.1% for the tensile strength and 179.9% for the tensile stiffness in comparison to the neat PLA. From the flexural behaviour point of view, Flat PLA-CF samples revealed an increase in average flexural strength and stiffness of 89.75% and 230.95%, respectively in comparison to the neat PLA. Furthermore, PLA-CF samples depicted the best ILSS performance. In general, the use of short carbon fibre as reinforcement did not affect the dimensional accuracy of the PLA-CF samples, and even improved the surface roughness in certain cases, particularly in Flat and On-edge orientations.

Computational fluid dynamics simulation of the melting process in the fused filament fabrication additive manufacturing technique

Numerical simulation is used to understand the melting and pressurization mechanism in fused filament fabrication (FFF). The results show the incoming fiber melts axisymmetrically, forming a cone of unmelted material in the center surrounded by melted polymer. Details of the simulation reveal that a recirculating vortex of melted polymer is formed at the fiber entrance to the hot end. The large viscosity within this vortex acts to effectively seal the system against back-pressures of order 1000 psi (10 MPa), which are typical under standard printing conditions. The Generalized Newtonian Fluid (GNF) model was appropriate for simulation within the region that melts the fiber, however, a viscoelastic model, the Phan-Thien-Tanner (PTT) model, was required to capture flow within the nozzle. This is due to the presence of an elongational flow as molten material transitions from the melting region (diameter of 3 mm) to the nozzle at the exit (diameter of 0.5 mm). Remarkably, almost half the pressure drop occurs over the short capillary (0.5 mm in length) attached to the end of the converging flow region. Increased manufacturing rates are limited by high pressures, necessitating more consideration in the nozzle design of future FFF printers.

Multifunctional performance of carbon nanotubes and graphene nanoplatelets reinforced PEEK composites enabled via FFF additive manufacturing

The study is focused on multifunctional performance of carbon nanotubes (CNT) and Graphene nanoplatelets (GNP) reinforced PEEK composites enabled via fused filament fabrication (FFF) additive manufacturing (AM) utilizing in-house nanoengineered filaments. Thermo-physical, mechanical and wear characteristics of electro-conductive PEEK nanocomposites are reported. The coefficient of thermal expansion (CTE) is found to decrease by 26% and 18% with the incorporation of 5 wt% GNP and 3 wt% CNT into PEEK polymer, respectively. The decrease in CTE provides better dimensional stability to resulting nanocomposite structures. Due to uniform dispersion of CNT and GNP in the PEEK matrix, the crystallization temperature and degree of crystallinity are both increased. The 3D printed PEEK nanocomposites reveal interfacial voids between the beads and intra-bead pores and thus exhibit lower density compared to that of the 3D printed neat PEEK. Young's and storage moduli are found to increase by 20% and 66% for 3 wt% CNT loading and by 23% and 72% for 5 wt% GNP loading respectively. However, the PEEK nanocomposites exhibit similar tensile strength to that of neat PEEK. The coefficient of friction obtained from fretting wear tests is found to decrease by 67% and 56% for 1 wt% CNT and 3 wt% GNP loaded PEEK nanocomposites, respectively and the decrease is attributed to reduced hardness and increased porosity. Multifunctional performance of carbon nanostructures reinforced AM-enabled PEEK composites demonstrated here makes them suitable for a range of applications such as orthopedics, oil and gas, automotive, electronics and space.

3D printed polylactic acid nanocomposite scaffolds for tissue engineering applications

In this study, biodegradable polylactic acid (PLA) and PLA nanocomposite scaffolds reinforced with magnetic and conductive fillers, were processed via fused filament fabrication additive manufacturing and their bioactivity and biodegradation characteristics were examined. Porous 3D architectures with 50% bulk porosity were 3D printed, and their physicochemical properties were evaluated. Thermal analysis confirmed the presence of ~18 wt% of carbon nanostructures (CNF and GNP; nowonwards CNF) and ~37 wt% of magnetic iron oxide (Fe2O3) particles in the filaments. The in vitro degradation tests of scaffolds showed porous and fractured struts after 2 and 4 weeks of immersion in DMEM respectively, although a negligible weight loss is observed. Greater extent of degradation is observed in PLA with magnetic fillers followed by PLA with conductive fillers and neat PLA. In vitro bioactivity study of scaffolds indicate enhancement from ~2.9% (PLA) to ~5.32% (PLA/CNF) and ~ 3.12% (PLA/Fe2O3). Stiffness calculated from the compression tests showed decrease from ~680 MPa (PLA) to 533 MPa and 425 MPa for PLA/CNF and PLA/Fe2O3 respectively. Enhanced bioactivity and faster biodegradation response of PLA nanocomposites with conductive fillers make them a potential candidate for tissue engineering applications such as scaffold bone replacement and regeneration.

Prediction and Experimental Validation of Part Thermal History in the Fused Filament Fabrication Additive Manufacturing Process

Abstract Part design and process parameters directly influence the instantaneous spatiotemporal distribution of temperature in parts made using additive manufacturing (AM) processes. The temporal evolution of temperature in AM parts is termed herein as the thermal profile or thermal history. The thermal profile of the part, in turn, governs the formation of defects, such as porosity and shape distortion. Accordingly, the goal of this work is to understand the effect of the process parameters and the geometry on the thermal profile in AM parts. As a step toward this goal, the objectives of this work are two-fold. First, to develop and apply a finite element-based framework that captures the transient thermal phenomena in the fused filament fabrication (FFF) additive manufacturing of acrylonitrile butadiene styrene (ABS) parts. Second, validate the model-derived thermal profiles with experimental in-process measurements of the temperature trends obtained under different material deposition speeds. In the specific context of FFF, this foray is the critical first-step toward understanding how and why the thermal profile directly affects the degree of bonding between adjacent roads (linear track of deposited material), which in turn determines the strength of the part, as well as, propensity to form defects, such as delamination. From the experimental validation perspective, we instrumented a Hyrel Hydra FFF machine with three non-contact infrared temperature sensors (thermocouples) located near the nozzle (extruder) of the machine. These sensors measure the surface temperature of a road as it is deposited. Test parts are printed under three different settings of feed rate, and subsequently, the temperature profiles acquired from the infrared thermocouples are juxtaposed against the model-derived temperature profiles. Comparison of the experimental and model-derived thermal profiles confirms a high degree of correlation therein, with a mean absolute percentage error less than 6% (root mean squared error <6 °C). This work thus presents one of the first efforts in validating thermal profiles in FFF via direct in situ measurement of the temperature. In our future work, we will focus on predicting defects, such as delamination and inter-road porosity based on the thermal profile.

Analysis of PLA Geometric Properties Processed by FFF Additive Manufacturing: Effects of Process Parameters and Plate-Extruder Precision Motion.

The evolution of fused filament fabrication (FFF) technology, initially restricted to the manufacturing of prototypes, has led to its application in the manufacture of finished functional products with excellent mechanical properties. However, FFF technology entails drawbacks in aspects, such as dimensional and geometric precision, and surface finish. These aspects are crucial for the assembly and service life of functional parts, with geometric qualities lagging far behind the optimum levels obtained by conventional manufacturing processes. A further shortcoming is the proliferation of low cost FFF 3D printers with low quality mechanical components, and malfunctions that have a critical impact on the quality of finished products. FFF product quality is directly influenced by printer settings, material properties in terms of cured layers, and the functional mechanical efficiency of the 3D printer. This paper analyzes the effect of the build orientation (Bo), layer thickness (Lt), feed rate (Fr) parameters, and plate-extruder movements on the dimensional accuracy, flatness error, and surface texture of polylactic acid (PLA) using a low cost open-source FFF 3D printer. The mathematical modelling of geometric properties was performed using artificial neural networks (ANN). The results showed that thinner layer thickness generated lower dimensional deviations, and feed rate had a minor influence on dimensional accuracy. The flatness error and surface texture showed a quasi-linear behavior correlated to layer thickness and feed rate, with alterations produced by 3D printer malfunctions. The mathematical models provide a comprehensive analysis of the geometric behavior of PLA processing by FFF, in order to identify optimum print settings for the processing of functional components.

A manufacturing cost analysis framework to evaluate machining and fused filament fabrication additive manufacturing approaches

The goal of this research is to develop a costing framework and explore opportunities for fabricating components using additive manufacturing (AM) and machining approaches. The examples target plastic machining, and the fused deposition modeling AM process, but the general model structure is applicable for other AM processes as process knowledge is developed. The question “when is machining more cost-effective than AM” has not been comprehensively addressed. The purpose of this work is to develop a costing framework to provide insight on whether to use machining or AM. An estimation model which considers the process planning, setup, and fabrication time-cost elements is developed in a structured manner. This is challenging due to the machining process planning complexity, the decision-making, and the skill set required, especially for complex surface finish machining. This framework can be readily adapted to suit specific environments. The estimation time models are linked to a cost model, and comparisons are drawn, based on the form complexity and manufacturing quantity. An effective combination of AM and machining technologies could bring about a new hybrid manufacturing approach that meets the requirements of the next generation of production systems, especially for short-run production. A cost-benefit analysis is performed to illustrate solution approaches for designing additive, subtractive, or hybrid operation sets for a variety of components to highlight the merits of the presented approach. Exploring a larger solution space of processing options and their processing time and costs will allow designers to generate more cost-competitive solutions.

Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes from heterogeneous sensor data

ABSTRACTThe objective of this work is to realize real-time monitoring of process conditions in advanced manufacturing using multiple heterogeneous sensor signals. To achieve this objective we propose an approach invoking the concept of sparse estimation called online sparse estimation-based classification (OSEC). The novelty of the OSEC approach is in representing data from sensor signals as an underdetermined linear system of equations and subsequently solving the underdetermined linear system using a newly developed greedy Bayesian estimation method. We apply the OSEC approach to two advanced manufacturing scenarios, namely, a fused filament fabrication additive manufacturing process and an ultraprecision semiconductor chemical–mechanical planarization process. Using the proposed OSEC approach, process drifts are detected and classified with higher accuracy compared with popular machine learning techniques. Process drifts were detected and classified with a fidelity approaching 90% (F-score) using OSEC. In comparison, conventional signal analysis techniques—e.g., neural networks, support vector machines, quadratic discriminant analysis, naïve Bayes—were evaluated with F-score in the range of 40% to 70%.