The study herein combines the use of fused filament fabrication (FFF) with finite element analysis (FEA) to enhance the understanding of certain manufacturing parameters (i.e. material, infill ...density, infill pattern, and outer vertical shell) in the design process of a lumbar fusion cage. Three FFF materials with distinct mechanical properties namely polycarbonate (PC), acrylonitrile butadiene styrene (ABS), and polylactic acid (PLA) were tested. Three infill densities (i.e. 25%, 50%, 75%) were investigated along with two different infill patterns (i.e. rectangular and honeycomb). Compressive modulus and compressive yield strength values obtained from standard mechanical analysis were used as input for FEA to assess numerically the mechanical performance of a lumbar fusion cage under physiological static loading. The findings suggest that both infill density and infill pattern influence the quality of the finished part in terms of both printing accuracy and mechanical response. FEA results indicate that both PC and ABS can be safely adopted to fabricate a porous lumbar cage with a 50% honeycomb infill density and a honeycomb infill pattern. This paper demonstrates that 3D printing assisted FEA can be used to predict the performance of a lumbar cage design with varying manufacturing parameters and potentially reduce product design and development time.
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•Porous structures printed by FFF with varying material, infill density, and infill pattern have been studied.•Compressive results were used as input for FEA to optimise the manufacturing process of a lumbar fusion cage.•Differences in dimensional accuracy, volume fraction, and compressive properties were found between patterns.•FEA allowed the selection of optimal materials and cage structure capable to withstand the maximum expected static loads.
Four-dimensional (4D) printing as a new generation additive manufacturing of smart materials has shown great potential for the intelligent development of multi-functional and customized structures. ...The microarchitecture design of printing infill patterns could bring flexible and diversified structural performances by combining the geometry design, thus opening up more possibilities for practical application. This work investigates the anisotropic characteristics of mechanical and shape memory performances induced by different infill strategies via both experimental and theoretical methods. Uniaxial tensile tests and compressive tests are performed to study the effect of infill patterns on mechanical properties. Both classical laminate plate theory and honeycomb equivalent modulus theory take into account the actual shape and dimensions of printed cross-section to improve the prediction accuracy. The viscoelasticity of each printing pattern is described by generalized Maxwell-Wiechert model and Prony Series are fitted to provide references to implement in Abaqus. With the purpose of exploring the shape memory properties including recovery speed, recovery ratio and shape fixity ratio, deformation-recovery tests are conducted under different temperatures and a 2D and 3D convertible structure with programmable Poisson's ratio are demonstrated. This work may potentially provide pattern design guidance for 4D printing structures to meet different application requirements.
The term additive manufacturing (AM) is highlighted in current scenario to develop complex and efficient parts for various applications. Fused deposition modelling (FDM) is most popular extrusion ...based AM technology which creates the 3D components through layer-by-layer manner. The aim of this paper is design and analysis of the Acrylonitrile butadiene styrene (ABS) parts for automobile application through Fused Deposition Modeling (FDM) process. It is evident that the individual or simultaneous change in FDM process parameters affects the mechanical properties of final part. In this paper the effect of some most effective parameters namely infill pattern and part build orientation have been studied. The parameters vary at three levels. It is observed that the selected parameters influence the compressive as well as bending strength of automobile parts. The rectilinear pattern with 90° orientation provided the maximum compressive strength whereas same pattern at 0° orientation shows highest bending strength.
3D printing is widely employed for a variety of applications due to its numerous advantages. The mechanical properties of a part made using 3D printing are quite important. As a result, it's critical ...to comprehend how distinct 3D printing process parameter values affect the part's mechanical qualities. However, for some applications, there needs a rigid framework that can tolerate parametric conditions. The mechanical strength of printed objects is influenced by printing parameters such as infill patterns, infill density, and print speed. The infill pattern of 3D printed material for exoskeleton application is examined in this study. In the study performed, it is attempted to find out the best infill pattern for good tensile and bending properties for a particular application. Out of the different patterns which are subjected to study in this investigation, it was found that, the maximum flexural stress value for a honeycomb type infill structure is 65 MPa but their tensile load capacity is less. Cubic type infill structure shows a highest flexural stress value of 64.8 MPa and tensile value of 20.56 MPa. Cubic type infill pattern gave the better result than other type of infill patterns which are subjected to different mechanical property evaluation in this study. According to the mechanical property investigation results obtained for the Cubic type infill pattern, it is found that, this type of infill pattern will be most suitable for exoskeleton applications.
Additive manufacturing is becoming more and more significant for the production of engineering products. The global community of designers and manufacturers will undoubtedly benefit from identifying ...the process factors that could lead to superior mechanical behaviour. The incorporation of fibres and nanoparticles in polymer matrix is one of the most significant advancements in polymer fabrication. This study intends to investigate how the flexural behaviour of dissimilar polymer composites viz carbon fibre reinforced poly lactic acid (CF-PLA), carbon fibre reinforced poly ethylene terephthalate glycol (CF-PETG) and multi walled carbon nano tubes reinforced PLA (MWCNTs-PLA) are affected by process parameters viz nozzle diameter (0.2 mm, 0.4 mm, 0.6 mm), and infill pattern (triangular, honeycomb, rectilinear) through fused filament fabrication (FFF). Flexural testing was carried out on the fabricated specimens, and post to the testing, fractography has been carried out and statistical analysis of the observations is performed utilizing analysis of variance and Taguchi method. According to the observations, flexural strength of the polymer composite specimen increases with increase in nozzle diameter (ND). The flexural behaviour is also directly impacted with the variation of infill pattern (IP). Process parameter variations affect target factors differently depending on the fabricating material. For flexural behaviour, IP contributes 68.27% to CF-PLA, while ND contributes 88.96% to CF-PETG and 77.59% to MWCNTs-PLA. Overall, the highest flexural strength, 70.372 MPa, is exhibited by the specimen with ND 0.6 mm and rectilinear IP for MWCNTs-PLA as fabricating filament material. This study will aid researchers and designers in developing FFF-fabricated electrochemical energy storage devices with improved flexural behaviour of functional parts for a wide range of applications.
This work proposes a filtering technique for the concurrent and sequential finite element-based topology and discrete fiber orientation optimization of composite structures. The proposed filter is ...designed to couple the morphology with the topology of the structural domain throughout the optimization process in a way such that it suppresses the impact of the close-to-void finite elements’ morphology on the overall morphology of the structure while steering at the same time the local fiber orientation towards the neighboring topologically dense areas of the geometry. The functionality of the filter becomes crucial at the boundaries of the optimized structure, where the fiber orientation is forced to conform to the morphology of the local boundaries. The developed filtering technique is incorporated into both the concurrent and sequential finite element-based topology and discrete fiber orientation optimization problem, and the respective optimization problems are formulated for the compliance minimization of the composite structure. To assess the efficacy of the filter, it is demonstrated in the benchmark academic case studies of the 2D Messerschmitt-Bölkow-Blohm and cantilever beams when different state-of-art interpolation techniques are employed for modeling the discrete fiber orientation optimization problem.
An air-fried 3D printed snack was developed to potentially provide consumer an alternative choice of potato snack with reduced oil. One cylindrical shape was firstly 3D printed with variable internal ...structures, followed by air-frying post-printing. The unfried printed samples well matched the designed geometry but with a slight shrinkage after air-frying process. Hardness and fracturability of air-fried potato snack, significantly affected by infill pattern, also significantly decreased when infill level decreased. The 100% infill printed and cast samples were observed with a serious puffing phenomenon during air-frying process. Finally, one kind of a food structure comprised of different parts illustrating multiple textures was developed by varying infill structure. The results suggested that the textural properties of the air-fried 3D printed potato snack could be modified by altering its internal structure, possibly offering a new way of oil reduction for potato-based snacks by creating customized internal structure by applying 3D printing.
This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional (3D) printing, particularly focusing on extrusion technology. Our primary ...objective was to develop a long short-term memory (LSTM) network capable of assessing this impact. We conducted an extensive analysis involving 12 distinct infill patterns, collecting time-series data to examine their effects on the acceleration of the printer’s bed. The LSTM network was trained using acceleration data from the adaptive cubic infill pattern, while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy. This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model. Specifically, the LSTM model was devised to predict the acceleration of a fused deposition modeling (FDM) printer using data from the adaptive cubic infill pattern. Rigorous testing yielded a root mean square error (RMSE) of 0.007144, reflecting the model’s precision. Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern, resulting in an RMSE of 0.007328. Notably, the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network (RNN) in predicting machine acceleration data. The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.
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