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As newer high performance polymers in mechanical properties become available for material extrusion-based additive manufacturing, determining infill parameter settings becomes more important to achieve both operational and mechanical performance of printed outputs. For the material extrusion of carbon fiber reinforced poly-ether-ether-ketone (CFR-PEEK), this study aims not only to identify the effects of infill parameters on both operational and mechanical performance but also to derive appropriate infill settings through a multicriteria decision-making process considering the conflicting effects.

A full-factorial experimental design to investigate the effects of two major infill parameters (i.e. infill pattern and density) on each performance measure (i.e. printing time, sample mass, energy consumption and maximum tensile load) is separately performed to derive the best infill settings for each measure. Focusing on energy consumption for operational performance and maximum tensile load for mechanical performance, the technique for order preference by similarity to ideal solution is further used to identify the most appropriate infill settings given relative preferences on the conflicting performance measures.

The results show that the honeycomb pattern type with 25% density is consistently identified as the best for the operational performance measures, while the triangular pattern with 100% density is the best for the mechanical performance measure. Moreover, it is suggested that certain ranges of preference weights on operational and mechanical performance can guide the best parameter settings for the overall material extrusion performance of CFR-PEEK.

The findings from this study can help practitioners selectively decide on infill parameters by considering both operational and mechanical aspects and their possible trade-offs.

This study aims to investigate and model interrelationships between process parameters, geometrical profile characteristics and mechanical properties of industrially extruded aluminum alloys.

Statistical design of experiments (DOE) was applied to investigate and model the effects of eight factors including extrusion ratio, stem speed, billet-preheat temperature, number of die cavities, quenching media (water/air), time and temperature of artificial aging treatment and profile nominal thickness on four mechanical properties (yield strength, ultimate tensile strength, percent elongation and hardness). Experiments were carried out at an actual extrusion plant using 8-in. diameter billets on an extrusion press with 2,200 ton capacity.

Main factors and factor interactions controlling mechanical properties were identified and discussed qualitatively. Quantitative models with high prediction accuracy (in excess of 95%) were also obtained and discussed.

The obtained results are believed to be of great importance to researchers and industrial practitioners in the aluminum extrusion industry.

All practical and relevant parameters have been used to model all important mechanical properties in a collective manner in one study and within actual industrial setup. This is in contrast to all previous studies where either a partial set of parameters and/or mechanical properties are discussed and mostly under limited laboratory setup.

The purpose of this paper is to investigate the effects of parameters on the liquid phase migration (LPM) during the freeze-form extrusion fabrication (FEF) process.

To carry out this study, three factors were systematically investigated using orthogonal design of experiments. These three parameters are the extrusion velocity, the extrusion interval time and the extrusion head length. An orthogonal array with nine test units was selected for the experiments. Range analysis and analysis of variance were used to analyze the data obtained by the orthogonal experiments to identify the order of significant factors on LPM.

It was found that the LPM decreased with the increase of extrusion velocity and increased with the lengthening of extrusion interval time and the length of the extrusion nozzle. The order of significant factors for the LPM were found to be extrusion velocity > extrusion nozzle length > extrusion interval time.

Using an orthogonal design of experiments and a statistical analysis method, the liquid content of extrudate can be predicted and appropriate process parameter values can be selected. This leads to the minimization of LPM during the FEF process. Also, this analysis method could be used to study the LPM in other paste extrusion processes.

This paper suggests that the factors have significant impact on LPM during FEF process. The following analysis in this paper is useful for FEF users when prediction of LPM is needed. This methodology could be easily applied to different materials and initial conditions for optimization of other FEF-type processes. The research can also help to get better understanding of LPM during the FEF process.

This study aims to investigate rheological and extrusion behavior of thermosetting epoxy resins, which to find the universal property and printing parameters for extrusion-based rapid prototyping applications.

The thickener proportion greatly influences its viscosity and rheological behavior and therefore plays an important role in the shape of the cross-section of the extrudate.

A pseudoplastic (shear-thinning) is a basic requirement for obtaining extruded lines with plump cross-sections. In addition to the effects of the rheological behavior of the composite, shape maintenance and its wettability on the substrate, the cross-sectional geometry of the extrudate is also strongly affected by printing process parameters including the extrusion nozzle height, nozzle moving speed, extrusion rate and critical nozzle height. Proper combinations of these process parameters are necessary to obtain single-line extrudates with plump cross-sections and 3-D objects with dimensional accuracy, uniform wall thickness, good wall uprightness and no wall slumping. Formulas and procedures for determining these extrusion parameters are proposed and demonstrated in experiments.

The results obtained have been explained in terms of the interactions among the rheological properties of the composite, the shear rate imposed on the composite during extrusion, the wettability of the composite on the substrate and the shape maintenance of the composite during extrusion.

The purpose of this paper is to manufacture an aluminium square cross-sectional bar by using conventional lathe machine from aluminium scraps through friction stir back extrusion (FSBE) process and study the viability of the process to produce the square bar.

The important tasks involved in this work are as follows: designing and manufacturing the chamber and plunger components used for experimental work, experimentally studying the thermo-mechanical progression of FSBE process on adapted conventional lathe machine and analyzing the relation between controlled parameter (like rotational speed and consolidation time) and response parameter (like extrusion time, extrusion rate, grain structure and hardness).

Preliminary results show that increasing or decreasing rotational speeds results in defects. Cold crack and twisting defect were shown on square bar fabricated using low rotational speed, and hot crack defects were observed on surface of the bars produced by higher rotational speed. The manufactured square bars were tested using optical microscope and Vickers hardness tester. Microstructural studies reveal that initial grains of aluminium wire undergo significant refinement and result in equiaxed and recrystallized grains in the square bar fabricated through FSBE method. The hardness tests show almost even distribution of hardness in the specimen, but hardness was lower than parent aluminium; in comparison, uneven distribution of hardness was seen in parent aluminium.

FSBE process is the new method to produce the bars and rods with better mechanical properties. The ambition of this work is to convert the existing scrap materials to useful products. Based on the literature review, the work has planned to perform extrusion process with the minimum effort and limited sources. In this manner, the work is highly original and under scientific mandate.

This paper aims to address the development and implementation of “AltPrint,” a slicing algorithm based on a new filling process planning from a variation in the deposited material geometry. AltPrint enables changes in the extruded material flow toward local variations in stiffness. The technical feasibility evaluation was conducted experimentally by fused filament fabrication (FFF) process of snap-fit subjected to a mechanical cyclical test.

The methodology is based on the estimation of the parameter E from the mathematical relationships among the variation of the material in the material flow, nozzle geometry and extrusion parameters. Calibration, validation and analysis of the printed specimens were divided into two moments, of which the first refers to the material responses (flexural and dynamic mechanical analysis) and the second involves the analysis of the printed components with localized flow properties (for estimating the response to cyclic loading). Finite element analysis assisted in the comparison of two snap-fit geometries, one traditional and one generated by AltPrint. Finally, three examples of compliant mechanisms were developed to demonstrate the potential of the algorithm in the generation of functional prototypes.

The contribution of AltPrint is the variable fill width integrated with the slicing software that varies the print parameters in different regions of the object. The alternative extrusion method based on material rate variation was conceived as an “open software” available in GitHub platform, hence, open manufacturing with initial focus on desktop 3D printer based on FFF. The slicing method provides deposited variable-width segments in an organized and replicable filling strategy, resulting in mechanical properties variations in specific regions of a part. It was implemented and evaluated experimentally and indicated potential applications in parts manufactured by the additive process based on extrusion, which requires local flexibilities.

This paper presents a new alternative method for application in an open additive manufacturing context, specifically for additive extrusion techniques that enable local variations in the material flow. Its potential for manufacturing functional parts, which require flexibility due to cyclic loading, was demonstrated by fabrication and experimental evaluations of parts made in acrylonitrile butadiene styrene filament. The changes proposed by AltPrint enable geometric modifications in the response of the printed parts. The proposed slicing and filling control of parameters is inserted in a context of design for additive manufacturing and shows great potential in the area of product design.

Techniques of extrude and cure additive manufacturing for thermally cured, high viscosity and medical-grade silicone are investigated by using a small ram extruder and a near-infrared (IR) laser. The purpose of this study is to evaluate the process parameter effects on the stiffness of the final products.

Process parameter effects on axial stiffness values and durometer are explored. Parameters such as extrusion layer height, laser speed, laser current, laser raster spacing and multiple laser passes were investigated and compared to traditional cast and cure methods. Dimensional changes were also recorded and compared.

Tensile and durometer tests show that certain curing parameters give tensile stress and durometers within 10 per cent of bulk material specifications at 200 per cent strain. Parameters that had the highest impact on tensile stress at 200 per cent strain were layer height (0.73 per cent) followed by laser power (0.69 per cent), and then laser raster spacing (0.45 per cent). Parameters that had the highest impact on durometer were laser power (1.00 per cent), followed by layer height, (0.34 per cent) and then laser raster speed (0.32 per cent). Three-dimensional printed samples had about 11.2 per cent more shrinkage than the bulk cast samples in the longest dimension.

This paper is one of the first that demonstrates near IR laser curing parameter effects on three-dimensional printed, commercial off-the-shelf, medical-grade and viscous silicone. The ability to cure very viscous thermosets locally enables interesting technologies such as wire encapsulation, high voltage actuators and drug delivery devices.

Material extrusion is one of the most commonly used approaches within the additive manufacturing processes available. Despite its popularity and related technical advancements, process reliability and quality assurance remain only partially solved. In particular, the surface roughness caused by this process is a key concern. To solve this constraint, experimental plans have been exploited to optimize surface roughness in recent years. However, the latter empirical trial and error process is extremely time- and resource consuming. Thus, this study aims to avoid using large experimental programs to optimize surface roughness in material extrusion.

This research provides an in-depth analysis of the effect of several printing parameters: layer height, printing temperature, printing speed and wall thickness. The proposed data-driven predictive modeling approach takes advantage of Machine Learning (ML) models to automatically predict surface roughness based on the data gathered from the literature and the experimental data generated for testing.

Using ten-fold cross-validation of data gathered from the literature, the proposed ML solution attains a 0.93 correlation with a mean absolute percentage error of 13%. When testing with our own data, the correlation diminishes to 0.79 and the mean absolute percentage error reduces to 8%. Thus, the solution for predicting surface roughness in extrusion-based printing offers competitive results regarding the variability of the analyzed factors.

There are limitations in obtaining large volumes of reliable data, and the variability of the material extrusion process is relatively high.

Although ML is not a novel methodology in additive manufacturing, the use of published data from multiple sources has barely been exploited to train predictive models. As available manufacturing data continue to increase on a daily basis, the ability to learn from these large volumes of data is critical in future manufacturing and science. Specifically, the power of ML helps model surface roughness with limited experimental tests.

Throughout the world, there has been a dramatic increase in the demand for functional food products. Owing to the health benefits of barley, its utilization was explored for the development of β-glucan rich functional instant talbina (porridge). The present research was, therefore, undertaken with an aim to optimize the extrusion conditions for development of instant talbina premix and evaluate its functional and quality characteristics.

The independent variables i.e. barrel temperature (105–125°C) and feed moisture (12–20%) were studied to determine their influence on the system parameter i.e. specific mechanical energy and product characteristics i.e. bulk density, breaking strength, water absorption index, expansion ratio, water solubility index, L*, a* and b* by employing a central composite rotatable design.

All the quality parameters were significantly (p < 0.05) influenced by independent variables. The regression models obtained for all the responses showed higher coefficient of determination (R² = 0.99). The optimum extrusion conditions obtained by numerical optimization for development of extrudates used for making instant talbina were moisture content (12%) and barrel temperature (105°C). Various functional, pasting, complexing index and physicochemical properties of instant talbina were improved by the addition of honey and milk. The instant talbina prepared from barley flour with the addition of milk and honey showed increase in the β-glucan content, resistant starch, antioxidant activity and decrease in glycemic index with improved overall acceptability.

As far as we could possibly know that very limited or not many studies have been carried out wherein the possibility of developing instant talbina (porridge) have been carried out. As a result, the current study has a lot of potential for the food industry to manufacture β-glucan rich functional instant talbina with improved antioxidant characteristics and low glycemic index.

This paper aims to study the effects of inorganic CaCO₃ nanoadditives in the polylactic acid (PLA) matrix and fused filament fabrication (FFF) process parameters on the mechanical characteristics of 3D-printed components.

The PLA filaments containing different levels of CaCO₃ nanoparticles have been produced by mix-blending/extrusion process and were used to fabricate tensile and three-point bending test samples in FFF process under various sets of printing speed (PS), layer thickness (LT), filling ratio (FR) and printing pattern (PP) under a Taguchi L27 orthogonal array design. The quantified values of mechanical characteristics of 3D-printed samples in the uniaxial and the three-point bending experiments were modeled and optimized using a hybrid neural network/particle swarm optimization algorithm. The results of this hybrid scheme were used to specify the FFF process parameters and the concentration of nanoadditive in the matrix that result in the maximum mechanical properties of fabricated samples, individually and also in an accumulative response scheme. Diffraction scanning calorimetry (DSC) tests were conducted on a number of samples and the results were used to interpret the variations observed in the response variables of fabricated components against the FFF parameters and concentration of CaCO₃ nanoadditives.

The results of optimization in an accumulative scheme showed that the samples of linear PP, fabricated at high PS, low LT and at 100% FR, while containing 0.64% of CaCO₃ nanoadditives in the matrix, would possess the highest mechanical characteristics of 3D-printed PLA components.

FFF is a widely accepted additive manufacturing technique in production of different samples, from prototypes to the final products, in various sectors of industry. The incorporation of chopped fibers and nanoparticles has been introduced recently in a few articles to improve the mechanical characteristics of produced components in FFF technique. However, the effectiveness of such practice is strongly dependent on the extrusion parameters and composition of polymer matrix.