Fused Filament Fabrication 3D Printing Research Articles

Multimaterial Bonding of Additively Manufactured Carbon Fiber‐Reinforced Thermoplastics/64 Titanium

The integration of lightweight materials in hybrid structures is critical for achieving energy efficiency in automotive and aerospace industries. This study presents a novel method for directly bonding carbon‐fiber‐reinforced thermoplastics to Ti6Al4V titanium alloy (64Ti) substrates using fused filament fabrication 3D printing. The technique involves 3D printing short carbon fiber‐reinforced polyamide 6 onto sandblasted 64Ti substrates, heated via a hot plate integrated into the 3D printer. Lap‐shear tests reveal that adhesion strength improves with increased fusion time, achieving a maximum shear stress of 27.3 ± 2.2 MPa for 60 min welding. Finite element analysis demonstrates stress concentrations at the adhesion edges and highlights the formation of a fracture process zone with localized plastic deformation and microcrack generation. Additionally, the feasibility of fabricating 3D structures and integrating continuous carbon fiber‐reinforced thermoplastics onto 64Ti substrates is demonstrated. This study advances hybrid material joining techniques by providing a cost‐effective, scalable method for achieving robust metal‐composite bonds suitable for structural applications.

Warpage mitigation through infill sectioning in fused filament fabrication

This study addresses the warpage issues in Fused Filament Fabrication (FFF) processes by strategically dividing infills to redistribute shrinkage forces, thereby mitigating warpage. The efficacy of these adjustments is evaluated quantitatively through 3D scanning and curvature assessment, coupled with an experimental design approach to optimize sectioning criteria. This sectioning method is particularly beneficial for translational studies where FFF is used to fabricate parts. Our case study focuses on two sample types—round cap parts and long wrench parts—to exemplify challenges related to their geometric characteristics when printed with desktop FFF 3D printers. Results show significant reductions in mean and maximum curvature and height deviations, indicating improvements in print quality and geometric fidelity. However, increased printing times and potential compromises in mechanical strength are noted as drawbacks. These findings enhance the understanding of the interplay between print parameters, part geometry, and designing for additive manufacturing, underscoring the need for further research to balance efficiency and structural integrity in additive manufacturing applications. Future research should explore the benefits of this sectioning strategy in other additive manufacturing processes beyond FFF.

A New Polymeric Hybrid Auxetic Structure Additively Manufactured by Fused Filament Fabrication 3D Printing: Machine Learning-Based Energy Absorption Prediction and Optimization.

The significance of this paper is an investigation into the design, development, and optimization of a new polymeric hybrid auxetic structure by additive manufacturing (AM). This work will introduce an innovative class of polymeric hybrid auxetic structure by the integration of an arrow-head unit cell into a missing rib unit cell, which will be fabricated using fused filament fabrication (FFF) technique, that is, one subset of AM. The auxetic performance of the structure is validated through the measurement of its negative Poisson's ratio, confirming its potential for enhanced energy absorption. A chain of regression, linear, and quadratic polynomial machine learning algorithms are used to predict and optimize the energy absorption capability at variant processing conditions. Amongst them, the polynomial regression model stands out with an R-squared value of 92.46%, reflecting an excellent predictive capability for energy absorption of additively manufactured polymeric hybrid auxetic structure. The optimization technique revealed that the printing speed of 80 mm/s and layer height of 200 µm were the critical values to achieve a maximum amount of energy absorption at 5.954 kJ/m2, achieved at a printing temperature of 244.65 °C. Such results also contribute to the development of AM, since they show not only the potential for energy absorption of polymeric hybrid auxetic structures but also how effective machine learning is in the optimization of the AM process.

Solution chemistry-assisted preparation of ZnO NPs–PLA nanocomposites and their filaments for fused filament fabrication 3D printing process

Solution chemistry-assisted preparation of ZnO NPs–PLA nanocomposites and their filaments for fused filament fabrication 3D printing process

A machine learning approach for predicting flexural strength of 3D printed hexagon lattice-cored sandwich structures

The development of sandwich structures using Fused Filament Fabrication (FFF) technology is an effective method for the rapid construction of complex profiles and is widely recognized for various structural applications. In this study, hexagon lattice-cored sandwich structures are developed by placing the lattice core in the center portion of the PLA polymeric specimen. The performance is analyzed with respect to varying levels of 3D-Printing Factors (3D-PFs) such as Nozzle Temperature (NT), Line Width (LW), Printing Speed (PS), and Layer Height (LH). The levels of the respective 3D-PFs are varied as follows: NT (180, 190, 200, and 210 °C), LH (0.15, 0.2, 0.25, and 0.3 mm), PS (15, 20, 25, and 30 mm/s), and LW (0.1, 0.2, 0.3, and 0.4 mm). The sandwich flexural samples are 3D printed using an FFF 3D printer, and their flexural properties are examined using a Universal Testing Machine (UTM). In the present work, various Machine Learning (ML) algorithms, such as Least Angle Regression (LARS), Support Vector Machine (SVM), Decision Tree (DT), and Ridge Regression (Ridge), are employed to predict the Flexural Strength (FS) of the 3D-printed sandwich structures and help in determining the optimized levels of 3D-PFs for achieving higher FS. The findings indicate that the LARS algorithm, particularly when applied within the Bagging Ensemble Learning Technique (ELT), exhibits the highest precision, showcasing a Mean Absolute Error (MAE) of 0.163, Root Mean Square Error (RMSE) of 0.153, and Median Absolute Error (MedAE) of 0.129. With the help of the LARS algorithm under Bagging ELT, the impact of 3D-PFs on the resulting FS is analyzed, which helps in determining the optimized levels of 3D-PFs concerning FS. The sandwich structures fabricated at the optimized levels show improved flexural properties, making them suitable for various structural applications.

Influence of nozzle temperatures on the microstructures and physical properties of 316L stainless steel parts additively manufactured by material extrusion

PurposeMaterial extrusion (ME) is a low-cost additive manufacturing (AM) technique that is capable of producing metallic components using desktop 3D printers through a three-step printing, debinding and sintering process to obtain fully dense metallic parts. However, research on ME AM, specifically fused filament fabrication (FFF) of 316L SS, has mainly focused on improving densification and mechanical properties during the post-printing stage; sintering parameters. Therefore, this study aims to investigate the effect of varying processing parameters during the initial printing stage, specifically nozzle temperatures, Tn (190°C–300°C) on the relative density, porosity, microstructures and microhardness of FFF 3D printed 316L SS.Design/methodology/approachCube samples (25 x 25 x 25 mm) are printed via a low-cost Artillery Sidewinder X1 3D printer using a 316L SS filament comprising of metal-polymer binder mix by varying nozzle temperatures from 190 to 300°C. All samples are subjected to thermal debinding and sintering processes. The relative density of the sintered parts is determined based on the Archimedes Principle. Microscopy and analytical methods are conducted to evaluate the microstructures and phase compositions. Vickers microhardness (HV) measurements are used to assess the mechanical property. Finally, the correlation between relative density, microstructures and hardness is also reported.FindingsThe results from this study suggest a suitable temperature range of 195°C–205°C for the successful printing of 316L SS green parts with high dimensional accuracy. On the other hand, Tn = 200°C yields the highest relative density (97.6%) and highest hardness (292HV) in the sintered part, owing to the lowest porosity content (<3%) and the combination of the finest average grain size (∼47 µm) and the presence of Cr23C6 precipitates. However, increasing Tn = 205°C results in increased porosity percentage and grain coarsening, thereby reducing the HV values. Overall, these outcomes suggest that the microstructures and properties of sintered 316L SS parts fabricated by FFF AM could be significantly influenced even by adjusting the processing parameters during the initial printing stage only.Originality/valueThis paper addresses the gap by investigating the impact of initial FFF 3D printing parameters, particularly nozzle temperature, on the microstructures and physical characteristics of sintered FFF 316L SS parts. This study provides an understanding of the correlation between nozzle temperature and various factors such as dimensional integrity, densification level, microstructure and hardness of the fabricated parts.

Machine learning-based approach for predicting the compressive strength of 3D printed hexagon lattice-cored sandwich structures

The utilization of Fused Filament Fabrication (FFF) technology for developing sandwich structures proves to be an effective approach, enabling the rapid construction of intricate profiles and gaining widespread recognition for diverse structural applications. In this study, hexagon lattice-cored sandwich structures are created by situating the lattice core at the center of the PLA polymeric specimens. The performance is assessed by varying 3D-Printing Factors (3D-PFs), including Nozzle Temperature (NT), Layer Height (LH), Printing Speed (PS), and Line Width (LW). The levels of 3D-PFs are manipulated as follows: NT (180, 190, 200, 210°C), LH (0.15, 0.2, 0.25, 0.3 mm), PS (15, 20, 25, 30 mm/sec), and LW (0.1, 0.2, 0.3, 0.4 mm). By employing a FFF 3D printer, the sandwich specimens are 3D-printed and their compression properties are assessed using a Universal Testing Machine (UTM). In this research, various Machine Learning (ML) models namely Bayesian Ridge regression (BRid), Elastic Net linear regression (EN), Quantile Regression (QR), and Support Vector Machine (SVM) are utilized to predict the compressive strength/density property of the developed sandwich structure. This aids in determining the optimal levels of 3D-PFs to achieve enhanced compressive strength/density. The results reveal that the QR model, particularly when employed in the boosting ensemble technique, exhibits superior accuracy with a Root Mean Square Error (RMSE) of 0.26 × 104, Mean Absolute Error (MAE) of 0.21 × 104, and Median Absolute Error (MedAE) of 0.16 × 104. Utilizing the QR model within the boosting ensemble technique, the influence of 3D-PFs on resulting compressive strength/density is analyzed, facilitating the identification of optimized 3D-PF levels for improved compressive strength/density. Sandwich structures fabricated at these optimized levels demonstrate enhanced compressive properties, making them suitable for a variety of structural applications.

Improving flexural performances of fused filament fabricated short carbon fiber reinforced polyamide composites with natural‐inspired structural design

AbstractBoth the nacre‐like bionic microstructure and the spiral laminated bionic configuration exhibit superior damage‐tolerance characteristics. On the basis of this observation, the design concept of the bionic helical‐interlayer configuration is innovatively integrated into the design of a bionic nacre‐like honeycomb structure. By systematically studying different spiral angles of honeycomb's interlayer stacking forms, their influence on the structural performance is deeply discussed with four‐point bending tests. Mechanical samples are carefully prepared using short carbon fiber reinforced polyamide composites (i.e., PA6‐CF) through conventional fused filament fabrication (FFF) 3D printing technology, where the accuracy and reliability of the designed bio‐inspired samples are ensured. The experimental results reveal significant improvements in bending strength and elastic modulus across various bionic nacre‐like honeycomb spiral structures compared to uniformly overlap configurations. In particular, the SH‐7.5 sample shows a remarkable 35.47% increase in bending strength and a 65.10% increase in elastic modulus over the SH‐11.25 sample. SEM‐based microstructural analyses are carried out to further explore the fracture mode of the carbon fibers, implied the helical configuration adopted in the nacre‐like honeycomb structure enhances the flexural resistant ability of the PA6‐CF composites. The findings above bear important guiding significance and reference value for the design of lightweight and high damage‐tolerance composite structures.Highlights A novel bio‐inspired structure is implemented to improve the mechanical performance of fused filament fabricated polyamide composites, where the bionic spiral helical configuration is integrated into high‐fracture‐resistance nacre‐like honeycomb structures. Mechanical testing results indicate that a helix angle under 10° results in a significant improvement in the structural performance of flexural strength. Microstructural analysis reveals that the helical configuration enhances the load‐bearing functionality of reinforcing carbon fibers in the printed polyamide composites. FFF 3D printing enables further implementation of the proposed bio‐inspired novel structure for lightweight and damage‐tolerant composite applications with high customization demands.

Application of fused filament fabrication 3D printing and molding to produce flexible, scaled neuron morphology models

PurposeThe purpose of this study is to assess a novel method for creating tangible three-dimensional (3D) morphologies (scaled models) of neuronal reconstructions and to evaluate its cost-effectiveness, accessibility and applicability through a classroom survey. The study addresses the challenge of accurately representing intricate and diverse dendritic structures of neurons in scaled models for educational purposes.Design/methodology/approachThe method involves converting neuronal reconstructions from the NeuromorphoVis repository into 3D-printable mold files. An operator prints these molds using a consumer-grade desktop 3D printer with water-soluble polyvinyl alcohol filament. The molds are then filled with casting materials like polyurethane or silicone rubber, before the mold is dissolved. We tested our method on various neuron morphologies, assessing the method’s effectiveness, labor, processing times and costs. Additionally, university biology students compared our 3D-printed neuron models with commercially produced counterparts through a survey, evaluating them based on their direct experience with both models.FindingsAn operator can produce a neuron morphology’s initial 3D replica in about an hour of labor, excluding a one- to three-day curing period, while subsequent copies require around 30 min each. Our method provides an affordable approach to crafting tangible 3D neuron representations, presenting a viable alternative to direct 3D printing with varied material options ensuring both flexibility and durability. The created models accurately replicate the fidelity and intricacy of original computer aided design (CAD) files, making them ideal for tactile use in neuroscience education.Originality/valueThe development of data processing and cost-effective casting method for this application is novel. Compared to a previous study, this method leverages lower-cost fused filament fabrication 3D printing to create accurate physical 3D representations of neurons. By using readily available materials and a consumer-grade 3D printer, the research addresses the high cost associated with alternative direct 3D printing techniques to produce such intricate and robust models. Furthermore, the paper demonstrates the practicality of these 3D neuron models for educational purposes, making a valuable contribution to the field of neuroscience education.

On the optimized fused filament fabrication of polylactic acid using multiresponse central composite design and desirability function algorithm

Efficient optimization of polymeric materials in fused filament fabrication 3D printing (FFF 3DP) is crucial for productivity, cost reduction, resource conservation, consistency, and enhanced part performance. This study employed a multiresponse central composite design of experiments (CCD-DOE) with the desirability function algorithm (DFA) to optimize printing settings on polylactic acid (PLA) using a commercial FFF 3D printer. The goal was to identify optimal parameters for faster build time and reduced material usage in PLA part fabrication. The fabrication process involved computer-aided design and modeling of standard PLA dogbone specimens, meeting ASTM-D638 Type 1 tensile test standards. These specimens were then 3D printed using Ultimaker Green RAL 6018 PLA filament and a 2+ model printer set at varying print parameters. Reduced second-order polynomial models for printing time and PLA weight were generated using stepwise regression, eliminating noninfluential parameters. The models revealed that higher layer thickness, increased print speed, and lower infill density resulted in faster printing times, while lower infill density and higher layer thickness led to lighter PLA prints. DFA analysis determined the optimal settings as a layer thickness of 0.26–0.30 mm and an infill density of 35% for minimizing printing time and PLA weight. The stress–strain curves displayed characteristic high-strength, brittle behavior under tension, while tensile testing of optimized PLA parts revealed increased strength with low strain at the break when layers were aligned parallel to the applied force. These findings advance additive manufacturing and provide practical guidelines for high-quality 3D-printed PLA components. Optimizing FFF 3DP parameters enables efficient production with reduced time and material usage, enhancing cost-effectiveness and the fabrication of high-performance 3D printed products.

Overprinting of TPU onto PA6 Substrates: The Influences of the Interfacial Area, Surface Roughness and Processing Parameters on the Adhesion between Components.

The hybridisation of injection moulding (IM) and additive manufacturing (AM) offers the opportunity to combine the high productivity of IM and the high flexibility of AM into a single process. IM parts can be overprinted through fused filament fabrication (FFF) to allow for the customisation of parts or to add new functionalities. However, the right material pair must be chosen, and processing parameters must be optimised to achieve suitable adhesion between the components. The present study dealt with the investigation of the influence of the interfacial area, substrate surface roughness and overprinting processing parameters on the adhesion between the polyamide 6 (PA6) substrate and thermoplastic polyurethane (TPU) rib overprinted via FFF. PA6 substrates were produced through the IM of plates into a mould with different textures to obtain substrates with three different surface roughnesses. The ribs with varied interfacial areas were overprinted onto produced substrates using a desktop FFF 3D printer. To study the effect of overprinting processing parameters, the ribs were overprinted under varying printing and substrate temperatures and printing speeds according to the Box-Behnken design of experiments (DoE). The chemical composition and thermal properties of used materials were determined via attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). The surface properties of prepared substrates were studied via digital optical microscopy (OM), through surface roughness measurements using a confocal microscope, through contact angle (CA) measurements and through the determination of free surface energy (SFE). The adhesion between the components was determined by evaluating the tear-off strength using a universal testing machine (UTM). With an increasing interfacial area, the tear-off strength decreased, while substrate surface roughness had no statistically significant effect. Overprinting parameters influenced the tear-off strength in the order of printing speed > printing temperature > substrate temperature. High values of tear-off strength were found for the lowest printing speed, while there were no important differences found between the middle and upper values. With increasing printing and substrate temperatures, the tear-off strength increased linearly. The highest value of tear-off strength (0.84 MPa) was observed at a printing temperature, substrate temperature and printing speed of 250 °C, 80 °C and 2 mm/s, respectively.

Manufacture of thermoplastic molds by fused filament fabrication 3D printing for rapid prototyping of polyurethane foam molded products

PurposePolyurethane (PUR) foam parts are traditionally manufactured using metallic molds, an unsuitable approach for prototyping purposes. Thus, rapid tooling of disposable molds using fused filament fabrication (FFF) with polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG) is proposed as an economical, simpler and faster solution compared to traditional metallic molds or three-dimensional (3D) printing with other difficult-to-print thermoplastics, which are prone to shrinkage and delamination (acrylonitrile butadiene styrene, polypropilene-PP) or high-cost due to both material and printing equipment expenses (PEEK, polyamides or polycarbonate-PC). The purpose of this study has been to evaluate the ease of release of PUR foam on these materials in combination with release agents to facilitate the mulding/demoulding process.Design/methodology/approachPETG, PLA and hardenable polylactic acid (PLA 3D870) have been evaluated as mold materials in combination with aqueous and solvent-based release agents within a full design of experiments by three consecutive molding/demolding cycles.FindingsPLA 3D870 has shown the best demoldability. A mold expressly designed to manufacture a foam cushion has been printed and the prototyping has been successfully achieved. The demolding of the part has been easier using a solvent-based release agent, meanwhile the quality has been better when using a water-based one.Originality/valueThe combination of PLA 3D870 and FFF, along with solvent-free water-based release agents, presents a compelling low-cost and eco-friendly alternative to traditional metallic molds and other 3D printing thermoplastics. This innovative approach serves as a viable option for rapid tooling in PUR foam molding.

The investigation of printing parameters effect on tensile characteristics for triply periodic minimal surface designs by Taguchi

AbstractThe advent of additive manufacturing also referred to as 3D printing, has brought about substantial changes in the industrial domain, as it possesses the capability to fabricate intricate items with enhanced cost‐efficiency and productivity. Fused Filament Fabrication (FFF) is a 3D printing process that has gained significant popularity due to its versatile capabilities and cost‐effectiveness. This paper investigates the impact of the printing parameters on the tensile characteristics of Triply Periodic Minimal Surface (TPMS) manufactured using FFF 3D printing. TPMS patterns have unique geometric properties and potential applications, making them an intriguing subject of study. The behavior of three different TPMS lattices with three printing parameters is investigated. Finding the best testing settings is done with the Taguchi method, and the data is analyzed with the Analysis of Variance (ANOVA) test. TPMS pattern was found to be the most effective parameter with 83.78%. While the highest strength was obtained in Schwarz diamond, the highest energy absorption was observed in the Gyroid structure. The contributions of printing parameters to tensile strength are line thickness, printing speed, and layer height, respectively. As line width and printing speed increase, both energy absorption and strength increase. Therefore, 0.40 mm line width and 60 mm/s printing speed give optimum values. When considered for energy absorption, the optimum value is 0.20 mm layer height, while when considered for strength, 0.10 mm layer height is the optimum value. The findings emphasize the importance of optimizing printing parameters for desired mechanical characteristics in 3D printed components and highlight the potential of TPMS structures in various applications. This research contributes to the growing knowledge in additive manufacturing and provides insights into optimizing FFF 3D printing for improved mechanical performance.

Fused filament fabrication in CAD education: A closed-loop approach

Integrating low-cost fused filament fabrication 3D printing as a foundation for learning 3D modelling is explored. This method blends traditional computer aided design (CAD) instruction with additive manufacturing possibilities. Experimental results demonstrate increased comprehension speed and reduced learning time. This hands-on approach empowers students by enabling direct engagement with the modelling process. Analogous to reverse engineering, the strategy instructs engineering students from final product to model creation, closing the gap between theory and practice. Incorporating 3D printing bridges this divide, enhancing understanding, creativity and problem-solving. The study underscores technology's influence on learning strategies, aligning with the surge of 3D printing in education. Results link advanced design technology usage to improved student performance, with 3D-printed materials yielding 45% higher grades and 30% faster task completion. This study advocates curricular advancement for design-focused careers through enhanced technology integration and favourable 3D printing model reception.

Tunable 3D printed composite metamaterials with negative stiffness

The paper proposes a class of tunable metamaterials that use inclined beams to achieve instability in a rigid system. Three different beam tilt angles, 25°, 45°, and 60°, are evaluated in the form of unit cells using quasi-static compression tests and numerical simulations. Snap-through behavirous are characterised by structural stiffness and buckling load. Periodic and gradient structures are assembled and analysed by arranging the unit cells in rows and columns. Size effect analyses and parametric studies are carried out on various unit-cell arrangements and different beam angles. The proposed metamaterials are manufactured through fused filament fabrication 3D printing technology with a composite material, onyx. The results from experiments, finite element analysis, and analytical models are compared and evaluated. The structural stiffness and buckling load are shown to be positively related to the inclination angle of the tilted beams. The number of rows of unit cells governs the nonlinear mechanical response (number of snap-throughs) of multiple-layered structures. By increasing the number of rows and columns of unit cells, which are less prone to manufacturing defects, the reliability and repeatability of the structural properties of periodic/gradient structures could be improved. A design plot is also provided to predict and tune the snap-through behaviour of multiple-layered structures via beam angles and unit-cell arrangements.

Study on CNT/TPU cube under the 3D printing conditions of infill patterns and density

In this study, to develop soft pressure sensor applicable to wearable robots using stretchable polymers and conductive fillers, 3.25 wt% carbon nanotubes/thermoplastic polyurethane filament with shore 94 A were manufactured. Three infill densities (20%, 50%, and 80%) and patterns (zigzag (ZG), triangle (TR), honeycomb (HN)) were applied to print cubes via fused filament fabrication 3D printing. Most suitable infill conditions were confirmed based on the slicing images, morphologies, compressive properties, electrical properties, and electrical heating properties. For each infill pattern, ZG and TR divided the layers into lines and figures, and the layers were stacked by rotation. For HN, the same layers were stacked in a hexagonal pattern. Consequently, TR divided layer in various directions, showed the strongest compressive properties with toughness 1.99 J for of infill density 80%. Especially, the HN became tougher with increased infill density. Also, the HN laminated with the same layer showed excellent electrical properties, with results greater than 14.7 mA. The electrical heating properties confirmed that ZG and HN had the high layer density, which exhibited excellent heating characteristics. Therefore, it was confirmed that performance varies depending on the 3D printing direction, and it was confirmed that HN is suitable for manufacturing soft sensors.

Influence of printing material moisture on part adhesion in fused filament fabrication 3D printing

ABSTRACT Fused filament fabrication plays an increasingly important role in modern manufacturing. Despite this, issues like deformations caused by thermal shrinkage are still common. These process failures, called warping, can easily be avoided by ensuring sufficient adhesion of the printed part to the build surface during the manufacturing process. Nevertheless, only a few is known about the factors which have an impact on the adhesion between a part to be printed and the build surface. Although the content of moisture in the used polymer plays an important role in every established processing method, its influence on adhesion is still unknown for fused filament fabrication. This publication investigates the influence of moisture in the printing material for the examples of build surfaces made from Pertinax and borosilicate glass and printing materials such as polylactide acid, polyvinyl alcohol and polyamide. These printing materials were characterized by thermogravimetric analyses and differential scanning calorimetry. Following adhesion tests showed that the moisture content of the printing material can alter the adhesion between the printed part and the build surface up to 68%. It was also shown that there is an optimum moisture content at which maximum adhesion is reached.

Simulation of temperature profile in fused filament fabrication 3D printing method

PurposeTemperature is a critical factor in the fused filament fabrication (FFF) process, which affects the flow behavior and adhesion of the melted filament and the mechanical properties of the final object. Therefore, modeling and predicting temperature in FFF is crucial for achieving high-quality prints, repeatability, process control and failure prediction. This study aims to investigate the melt deposition and temperature profile in FFF both numerically and experimentally using different Acrylonitrile Butadiene Styrene single-strand specimens. The process parameters, including layer thickness, nozzle temperature and build platform temperature, were varied.Design/methodology/approachCOMSOL Multiphysics software was used to perform numerical simulations of fluid flow and heat transfer for the printed strands. The polymer melt/air interface was tracked using the coupling of continuity equation, equation of motion and the level set equation, and the heat transfer equation was used to simulate the temperature distribution in the deposited strand.FindingsThe numerical results show that increasing the nozzle temperature or layer thickness leads to an increase in temperature at points close to the nozzle, but the bed temperature is the main determinant of the overall layer temperature in low-thickness strands. The experimental temperature profile of the deposited strand was measured using an infrared (IR) thermal imager to validate the numerical results. The comparison between simulation and observed temperature at different points showed that the numerical model accurately predicts heat transfer in the three-dimensional (3D) printing of a single-strand under different conditions. Finally, a parametric analysis was performed to investigate the effect of selected parameters on the thermal history of the printed strand.Originality/valueThe numerical results show that increasing the nozzle temperature or layer thickness leads to an increase in temperature at points close to the nozzle, but the bed temperature is the main determinant of the overall layer temperature in low-thickness strands. The experimental temperature profile of the deposited strand was measured using an IR thermal imager to validate the numerical results. The comparison between simulation and observed temperature at different points showed that the numerical model accurately predicts heat transfer in the 3D printing of a single-strand under different conditions. Finally, a parametric analysis was performed to investigate the effect of selected parameters on the thermal history of the printed strand.

Effect of TiC Content on Microstructure and Wear Performance of 17-4PH Stainless Steel Composites Manufactured by Indirect Metal 3D Printing.

In order to improve the performance of 17-4PH under wear conditions (e.g., gears, etc.) and reduce the cost of metal additive manufacturing, TiC needs to be added to 17-4PH to improve its wear resistance. Micron-sized TiC-reinforced 17-4PH stainless steel composites with different contents (0-15 wt%) have been prepared by fused filament fabrication 3D printing for the first time. The effects of TiC content on the structure and properties of composites were studied by XRD, SEM, and sliding wear. The obtained results show that the microstructure of TiC-reinforced 17-4PH stainless steel composites mainly consists of austenite. With the increase in TiC content, the grain size is obviously refined, and the average grain size decreases from 65.58 μm to 19.41 μm. The relative densities of the composites are maintained above 95% with the addition of TiC. The interfaces between TiC particles and the 17-4PH matrix are metallurgical bonds. The hardness of the composites increases and then decreases with increasing TiC content, and the maximum hardness (434 HV) is obtained after adding 10 wt.% of TiC content. The wear rate of the composites was reduced from 2.191 × 10-5 mm3 /(N‧m) to 0.509 × 10-5 mm3 /(N‧m), which is a 3.3-fold increase in wear resistance. The COF value declines with the addition of TiC. The reasons for the significant improvement in the composites' performance are fine grain strengthening, solid solution strengthening, and second phase strengthening. The wear mechanisms are mainly abrasive and adhesive wear. Compared to the 10 wt% TiC composites, the 15 wt% TiC composites show limited improvement in wear resistance due to more microcracks and TiC agglomeration.

Fabrication and tunable reinforcement of net-shaped aluminum matrix composite parts via 3D printing

Advanced materials, such as metal matrix composites (MMCs), are important for innovation, national security, and addressing climate change. MMCs are used in military, aerospace, and automotive applications because of their exceptional mechanical and thermal properties, however adoption has been slow due to costly and onerous manufacturing processes. A new process using fused filament fabrication 3D printing has been developed to make net shape MMCs without tooling or machining. The process involves printing an alumina preform and then using pressure-less infiltration with a molten aluminum alloy to form the composite. Arbitrary shapes can be formed in this process—a brake lever and a flange are demonstrated—and the properties can be tuned by varying the ceramic infill geometric pattern and ceramic loading. By using 35 vol% continuous fiber reinforcement over 800 MPa strength and 140 GPa modulus are achieved for the aluminum composite, 3.4 × and 2 × the matrix aluminum properties.