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This paper aims to investigate the current state of biocomposites used in fused deposition modelling (FDM) with a focus on their mechanical characteristics.

The study presents a variety of biocomposite materials that have been used in filaments for 3D printing by different researchers. The process of making filaments is then described, followed by a discussion of the process parameters associated with the FDM.

To achieve better mechanical properties of 3D-printed parts, it is essential to optimize the process parameters of FDM while considering the characteristics of the biocomposite material. Polylactic acid is considered the most promising matrix material due to its biodegradability and lower cost. Moreover, the use of natural fibres like hemp, flax and sugarcane bagasse as reinforcement to the polymer in FDM filaments improves the mechanical performance of printed parts.

The paper discusses the influence of critical process parameters of FDM like raster angle, layer thickness, infill density, infill pattern and extruder temperature on the mechanical properties of 3D-printed biocomposite.

The purpose of this research is present an experimental and numerical study of the mechanical properties of the acrylonitrile butadiene styrene (ABS) in the additive manufacturing (AM) by fused filament fabrication (FFF). The characterization and mechanical models obtained are used to predict the elastic behavior of a prosthetic foot and the failure of a prosthetic knee manufactured with FFF.

Tension tests were carried out and the elastic modulus, yield stress and tensile strength were evaluated for different material directions. The material elastic constants were determined and the influence of infill density in the mechanical strength was evaluated. Yield surfaces and failure criteria were generated from the tests. Failures over prosthetic elements in tridimensional stresses were analyzed; the cases were evaluated via finite element method.

The experimental results show that the material is transversely isotropic. The elasticity modulus, yield stress and ultimate tensile strength vary linearly with the infill density. The stresses and the failure criteria were computed and compared with the experimental tests with good agreement.

This research can be applied to predict failures and improve reliability in FFF or fused deposition modeling (FDM) products for applications in high-performance industries such as aerospace, automotive and medical.

This research aims to promote its widespread adoption in the industrial and medical sectors by increasing reliability in products manufactured with AM based on the failure criterion.

Most of the models studied apply to plane stress situations and standardized specimens of printed material. However, the models applied in this study can be used for functional parts and three-dimensional stress, with accuracy in the range of that obtained by other researchers. The researchers also proposed a method for the mechanical study of fragile materials fabricated by processes of FFF and FDM.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

The purpose of this study is twofold: firstly, to investigate the effect of the infill value and build orientation on the fatigue behaviour of polylactic acid (PLA) specimens made by fused filament fabrication (FFF), also known as fused deposition modelling; and secondly, to model the fatigue behaviour of PLA specimens made by FFF and similar additive manufactured parts.

A new methodology based on filament characterisation, infill measuring, axial fatigue testing and fatigue strength normalisation is proposed and implemented. Sixty fatigue FFF specimens made of PLA were fabricated and evaluated using variable infill percentage and build orientation. On the other hand, fatigue modelling is based on the normalised stress amplitude and the fatigue life in terms of number of cycles. In addition, a probabilistic model was developed to predict the fatigue strength and life of FFF components.

The infill percentage and build orientation have a great influence on the fatigue behaviour of FFF components. The larger the infill percentage, the greater the fatigue strength and life. Regarding the build orientation, the specimens in the up-right orientation showed a much smaller fatigue strength and life than the specimens in the flat and on-edge orientations. Regarding the fatigue behaviour modelling, the proposed Weibull model can predict with an acceptable reliability the stress-life performance of PLA-FFF components.

This study has been limited to axial fatigue loading conditions along three different build orientations and only one type of material.

The results of this study are valuable to predict the fatigue behaviour of FFF parts that will work under variable loading conditions. The proposed model can help designers and manufacturer to reduce the need of experimental tests when designing and fabricating FFF components for fatigue conditions.

A fatigue study based on a novel experimental methodology that considers the variation of the FFF process parameters, the measurement of the real infill value and the normalisation of the results to be comparable with other studies is proposed. Furthermore, a new fatigue model able to predict the stress-life fatigue behaviour of PLA-FFF components considering variable process parameters is also proposed.

The purpose of this paper is to analyze the effect of printing parameters on the mechanical properties of standard dog bone specimens manufactured by fused deposition modeling.

Polylactic acid (PLA) specimens were printed and tested according to the ASTM standard. The effect of five important printing parameters, layer height, raster angle, printing speed, nozzle temperature and nozzle diameter, was examined on ultimate tensile strength (UTS), elongation and apparent density. Five levels were attended for each parameter, and a high number of required experiments were reduced by applying the L₂₅ Taguchi design of the experiment.

The effect of each parameter on outputs and optimal values for maximum tensile strength were determined. The most influential parameter is the raster angle of 64.96%. Nozzle temperature has a low effect of 1.76%, but nozzle diameter contribution is 9.77%. The experiment results are validated by analysis of variance analysis, and the optimal predicted level for parameters is 90° raster angle, 0.2 mm layer height, 100 mm/s printing speed, 200°C nozzle temperature and 0.8 mm nozzle diameter. The maximum UTS observed is 48.70 MPa for 0.8 mm nozzle diameter, whereas the minimum is 18.49 for 0.2 mm nozzle diameter.

This paper is a very extensive experimental research report on the effect of the parameters for the tensile property of 3D printed PLA specimens by the Taguchi method. The documented results can be further developed for an optimization model to obtain a desired mechanical property with less variation and uncertainty in a product.

Temperature 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.

COMSOL 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.

The 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.

The 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.

The rapid development of three-dimensional (3D) printing makes it familiar in daily life, especially the fused deposition modeling 3D printers. The process planning of traditional flat layer printing includes slicing and path planning to obtain the boundaries and the filling paths for each layer along the vertical direction. There is a clear division line through the whole fabricated part, inherited in the flat-layer-based printed parts. This problem is brought about by the seam of the boundary in each layer. Hence, the purpose of this paper is to propose a novel helical filling path generation with the ideal surface-plane intersection for a rotary 3D printer.

The detailed algorithm and implementation steps are given with several worked examples to enable readers to understand it better. The adjacent points obtained from the planar slicing are combined to generate each layer's helical points. The contours of all layers are traversed to obtain the helical surface layer and helical path. Meanwhile, the novel rotary four-degree of freedom 3D printer is briefly introduced.

As a proof of concept, this paper presents several examples based on the rotary 3D printer designed in the authors’ previous research and the algorithms illustrated in this paper. The preliminary experiments successfully verify the feasibility and versatility of the proposed slicing method based on a rotary 3D printer.

This paper provides a novel and feasible slicing method for multi-axis rotary 3D printers, making manufacturing thin-wall and complex parts possible. To further broaden the proposed slicing method’s application in further research, adaptive tool path generation for flat and curved layer printing could be applied with a combination of flat and curved layers in the same layer, different layers or even different parts of structures.

The purpose of this paper is to investigate the tensile strength, Young’s modulus, dimensional stability and porosity of acrylonitrile butadiene styrene (ABS)–oil palm fiber composite filament for fused deposition modeling (FDM).

A new feedstock material for FDM comprising oil palm fiber and ABS as a matrix was developed by a twin screw extruder. The composite filament contains 0, 3, 5 and 7 Wt.% of oil palm fiber in the ABS matrix. The tensile test is then performed on the fiber composite filament, and the wire diameter is measured. In this study, the Archimedes method was used to determine the density and the porosity of the filament. The outer surface of the wire composite was examined using an optical microscope, and the analysis of variance was used to assess the significance and the relative relevance of the primary factor.

The results showed that increasing the fiber loading from 0.15 to 0.4 MPa enhanced tensile strength by 60%. Then, from 16.1 to 18.3 MPa, the Young’s modulus rose by 22.8%. The density of extruded filament decreased and the percentage of porosity increased when the fiber loading was increased from 3 to 7 Wt.%. The diameter deviation of the extruded filaments varied from −0.21 to 0.04 mm.

This paper highlights a novel natural resource-based feedstock material for FDM. Its mechanical and physical properties were also discovered.

Additive manufacturing has disadvantages, such as the maximum part size being limited by the machine’s working volume. Therefore, if a part more considerable than the working volume is required, the part is produced in parts and joined together. Among the many methods of joining thermoplastic parts, adhesives and mechanical interlocking are considered. This study aims to characterize and optimize mechanically stressed adhesive joints combined with female and male mechanical interlocking on acrylonitrile butadiene styrene (ABS) specimens made with fused filament fabrication (FFF) so that the joint strength is as close as possible to the strength of the base material.

This study characterized the subject’s state of the art to justify the decisions regarding the experimental design planned in this research. Subsequently, this study designed, executed and analyzed the experiment using a statistical analysis of variance. The output variables were yield strength and tensile strength. The input variables were two different cyanoacrylate adhesives, two different types of mechanical interlock (truncated pyramid and cylindrical pin) and the dimensions of each type of mechanical interlock. This study used simple and factorial experiments to select the best adhesive and interlocking to be optimized using the response surface and the steep ascent method.

The two adhesives have no statistical difference, but they show different data dispersion. The design or yield stress was a determining factor for selecting the optimal specimen, with cylindrical geometry exhibiting higher resistance at initial failure. Geometry type is crucial due to the presence of stress concentrators. The cylindrical geometry with fewer stress concentrators demonstrated better tensile strength. Ultimately, the specimen with a mechanically reinforced joint featuring a cylindrical pin of radius 5.45 mm and height of 4.6 mm exhibited the maximum tensile and yield strength.

Previous research suggests that a research opportunity is the combination of bonding methods in FFF or fused deposition modeling, which is not a frequent topic, and this research to enrich that topic combines the adhesive with mechanically interlocked joints and studies it experimentally for FFF materials, to provide unpublished information of the performance of the adhesive joint with mechanical interlocking, to designers and manufacturers of this technology.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.