Effect of relative density and strut thickness on dimensional accuracy in lattice structures produced by additive manufacturing

Lattice Structures (LS) are widely recognized for their light weight and excellent mechanical properties. In this study, strut reinforcement technique is applied to enhance the energy absorption properties of 3D re-entrant auxetic (Aux) and hexagonal (Hex) LS. Numerical investigation using finite element analysis (FEA) was carried out to understand the mechanical and energy absorption properties of the novel designs through quasi-static compression test. The uniaxial loading results of the reinforced designs were compared to the traditional 3D hexagonal and re-entrant auxetic LS. In order to numerically simulate the mechanical behaviour of 3D printed LS, mechanical properties of experimentally studied PA2200 matrix material (manufactured via additive manufacturing) was used. Study of the mechanical behaviour of the 3D printed LS parts is essential because additive manufacturing has the capacity to fabricate the complex LS compared to conventional manufacturing processes, and innovative LS design can provide high specific strength of parts. The stress–strain and energy absorption curve results indicate that the purposed new designs are excellent choice for energy absorption properties at large strain. The findings of this study contribute towards the novel design of 3D hexagonal and re-entrant auxetic LS for superior mechanical strength and specific energy absorption properties.

Composite models have become commonplace for the assessment of fixation and stability of total joint replacements; however, there are no comparable models for the cervical spine to evaluate fixation. The goal of this study was to create the framework for a tunable non-homogeneous model of cervical vertebral body by identifying the relationships between strength, in-fill density, and lattice structure and creating a final architectural framework for specific strengths to be applied to the model. The range of material properties for cervical spine were identified from literature. Using additive manufacturing software, rectangular prints with three lattice structures, gyroid, triangle, zig-zag, and a range of in-fill densities were 3D-printed. The compressive and shear strengths for all combinations were calculated in the axial and coronal planes. Eleven unique vertebral regions were selected to represent the distribution of density. Each bone density was converted to strength and subsequently correlated to the lattice structure and in-fill density with the desired material properties. Finally, a complete cervical vertebra model was 3D-printed to ensure sufficient print quality. Materials testing identified a relationship between in-fill densities and strength for all lattice structures. The axial compressive strength of the gyroid specimens ranged from 1.5 MPa at 10% infill to 31.3 MPa at 100% infill and the triangle structure ranged from 2.7 MPa at 10% infill to 58.4 MPa at 100% infill. Based on these results, a cervical vertebra model was created utilizing cervical cancellous strength values and the corresponding in-fill density and lattice structure combination. This model was then printed with 11 different in-fill densities ranging from 33% gyroid to 84% triangle to ensure successful integration of the non-homogeneous in-fill densities and lattice structures. The findings from this study introduced a framework for using additive manufacturing to create a tunable, customizable biomimetic model of a cervical vertebra.

Printed circuit boards, chemical etching, and computer numerical control milling currently dominate industrial processes for manufacturing microwave components. However, these manufacturing methods do not provide the flexibility for customization possible with additive manufacturing. Additive manufacturing (AM) has the potential to fabricate microwave components for desired frequency ranges with less effort in prototyping and fabrication. Relative permittivity (εr) of materials is a critical parameter in microwave component design, yet the value changes during the AM process. This article investigates how relative permittivity for nylon substrates, created with AM, changes with different infill densities and infill patterns. The measurement method and procedure can be used to design AM microwave components like antennas or dielectric-filled waveguides with desired characteristics. The two-microstrip-line method was used and improved for the accurate and convenient measurement of the relative permittivity of AM nylon substrates. Several nylon substrates with different infill patterns, including rectangular, hexagonal, triangular, and solid, were fabricated with AM to demonstrate how the relative permittivity value changes as the infill density increases. A linear relationship between the infill density of the rectangular pattern and the substrate permittivity was found. The permittivity data were applied to the design of a rectangular patch antenna for use in the 2.5-GHz WiMAX band. The fabricated antenna with AM, which was tested using a vector network analyzer, showed good agreement with simulation results. The method and procedure of permittivity measurements are specially designed to be applied in the design of microwave components with AM dielectric substrates.

Additive manufacturing has enabled the production of individualised 3D printed shoe soles with improved properties. A promising approach is to use lattice structures that have high energy absorption properties and low weight. A design method of a cellular shoe midsole with optimised strut diameters of lattice structures is proposed. The shoe sole model is obtained by re-modelling a 3D scan of a foot to ensure a customised fit. The optimisation of strut thicknesses is based on a simplified stress distribution acting on the shoe sole during walking or running, using finite element simulations. Therefore, the optimal strut thickness for each region of the sole can be determined and adjusted. Two types of lattice structures with different topology and thus significant variations in stiffness are chosen, resulting in a wide range of required strut thicknesses. The developed design process allowed for the creation of a 3D printed shoe sole with improved strut thicknesses and a customised fit. The resulting cellular shoe soles are additively manufactured and experimental compressive tests are conducted to investigate the mechanical behaviour and differences between the shoe soles with the corresponding lattice types. The results show that both shoe soles have a similar behaviour under compression. The design tool developed has the potential to improve foot health and comfort, especially for people with foot problems, as all parameters affecting the performance of a shoe sole can be adjusted. However, more research is needed to fully understand the durability and performance of these shoe soles in real-world conditions.

Additive Manufacturing (AM) techniques, especially Fused Deposition Modelling (FDM), have generated much interest recently for their capabilities for manufacturing complex geometries using a variety of materials. In this work, a regression model has been developed for the FDM process performance enhancement and to control PETG processing. This study systematically analysed the effect of critical FDM parameters on key performance criteria such as printing time, dimensional deviation, and surface finish, including nozzle temperature, printing speed, and infill density. Experiments were carried out following a defined design of experiments to gather data which were then used to develop regression models for the prediction of printing results. A statistical treatment was done on the relationships among process variables with its impact on performance metrics. The predictive model developed showed a high level of accuracy, thus allowing for the identification of optimal levels for parameter settings conducive to PETG component efficiency, surface quality, and dimensional accuracy. Thus, the study acts as a practical guide for manufacturers willing to upgrade their additive manufacturing processes relating to process optimization, quality control, and production planning. By tying experimental inquiry with predictive modeling, this work delves deep into the dynamics of the FDM process and provides valuable insights for the mass use of PETG-based FDM in automotive, aerospace, biomedical, and other industry sectors.

Additive manufacturing (AM) plays a significant role in the 4th Industrial Revolution due to its flexibility, allowing AM equipment to be connected, monitored, and controlled in real time. In advance, the minimum waste of material, the agility of manufacturing complex geometries, and the ability to use recycled materials can provide an advantage to this manufacturing method. On the other hand, the poor strength and durability of the thermoplastics used in the manufacturing process are the major drawback that keeps AM behind common production methods such as casting and machining. Fibre-reinforced polymers can enhance mechanical properties, advance AM from the commonly used polymers, and make AM competitive against conventional production methods. The main focus of the current review is to examine the work conducted in the field of reinforced additively manufactured technologies in the literature of recent years. More specifically, this review discusses the conducted research in the composite fibre coextrusion (CFC) additive manufacturing techniques developed over the past years and the materials that can be used. In addition, this study includes an up-to-date comprehensive review of the evaluation of fibre-reinforced 3D printing along with its benefits in terms of mechanical response, namely tensile, flexural, compression and energy absorption, anisotropy, and dynamic properties. Finally, this review highlights possible research gaps regarding fibre-reinforced AM and proposes future directions, such as deeper investigations into energy absorption and anisotropy, to position fibre-reinforced AM as a preferred fabrication method for ready-to-use parts in cutting-edge industries, including automotive, aerospace, and biomedical sectors.

Manufacturing significantly contributes to global warming due to its substantial carbon emissions. United Nations sustainable development goals support the reduction of carbon emissions in the manufacturing sector, which can be accomplished by making the manufacturing process sustainable with a minimal carbon footprint. This is also appropriate for novel manufacturing processes such as additive manufacturing. This study introduces the investigation of the additively manufactured specimen. Prior research delves into examining the impact on the energy consumption of solid specimens under distinct printing process parameters. Nonetheless, the influence of electrical energy consumption and total carbon footprint for the additively manufactured solid and lattice structure has yet to be investigated. The current study fills the research gap by assessing layer thickness and infill density on both specimens’ electrical energy consumption and total carbon footprint. The presented study offers insight into the impact of layer thickness and infill density for the solid and lattice structure specimens and their comparison of electrical energy consumption and total carbon footprints. The results demonstrated that a rise in an infill density directly correlates with increased energy consumption and carbon footprints. However, rising layer thickness resulted in a reduction in both power consumption and carbon footprints. Furthermore, it was observed that the triangular, octagonal, and hexagonal cellular structures manifest higher power consumption when the infill density is set at 50% and 80%, respectively. Moreover, when assessing a solid specimen at 100% infill density, the total carbon footprint exhibits increases of 12%, 21%, 23%, and 41% in comparison to triangular, octagonal, hexagonal, and square lattice structures, respectively.

This study investigates the prediction of fused deposition modeling (FDM) process parameters for manufacturing biomedical implants with high dimensional accuracy and surface quality. Biomedical implants were fabricated in circular, triangular, and pentagonal geometries to accommodate different anatomical requirements, using three materials selected for their biomedical applicability and mechanical properties. These materials are polylactic acid (PLA), polyethylene terephthalate glycol, and thermoplastic polyurethane (TPU). This research utilizes the Taguchi L27 orthogonal array methodology to analyze the influence of five critical printing parameters: material type, layer thickness (200–300–400 µm), infill density (30%–60%–90%), infill pattern (zigzag, cubic, and triangle), and wall thickness (1–2–3 mm). The analysis of variance demonstrated that material type and layer thickness are the most significant factors, contributing 49.25% and 17.97%, respectively, to dimensional accuracy in circular geometries. Surface roughness measurements showed that layer thickness (30.95%) and material type (31.28%) are dominant factors affecting surface quality. The optimum parameters for dimensional accuracy were determined as PLA material, zigzag infill pattern, 2 mm wall thickness, 30% infill density and 200 µm layer thickness, while the highest surface quality was achieved with PLA material, triangle infill pattern, 3 mm wall thickness, 90% infill density and 200 µm layer thickness. An artificial neural network model was developed to predict dimensional accuracy and surface quality, achieving high correlation coefficients (R2 > 0.96) between predicted and experimental results across all geometric configurations. These findings offer valuable guidelines for predicting and optimizing parameters in FDM-based biomedical implant manufacturing, advancing precision medicine by enhancing additive manufacturing processes and implant performance.

Dimensional accuracy and internal fit of anterior 3-unit fixed dental prostheses fabricated by additive and subtractive manufacturing using polymer-based and ceramic restorative materials.

Additive manufacturing (AM) or 3D printing technology creates a tangible object by adding successive layers of materials. Nowadays, 3D printing is used for developing both metal and non-metal products. In the advancement of 3D printing technology, material specimen design, modification, and testing become very simple, especially for non-metal materials, such as hyperelastic, thermoplastic, or rubber-like materials. However, proper material modeling and validation are required for the analysis of these types of materials. In this study, 3D printed poly lactic acid (PLA+) material behavior is analyzed numerically for validation in the counterpart of experimental analysis to evaluate their behavior in both cases. The specimen was designed in SolidWorks by following ASTM D638 dimension standards with proper infill densities and raster angle or infill orientation angle. These infill layer densities and angles of orientation play an important role in the mechanical behavior of the specimen. This paper aims to present a numerical validation of five infill densities (20%, 40%, 60%, 80%, and 100%) for a ±45-degree infill angle orientation by incorporating a nonlinear hyperelastic model. Results indicate that infill densities affect the mechanical behavior of PLA+ material. The result also suggested that neo-Hookean and Mooney–Rivlin are the best-fitted hyperelastic material models for these five separate linear infill densities. However, neo-Hookean is easier to analyze, as it has only one parameter and a new equation is developed in this study for determining the parameter for different infill densities.

Additive manufacturing has become increasingly popular for rapid prototyping and industrial applications, particularly in the context of Industry 4.0. However, given the vast design parameter space, there remains a lack of characterisation of the mechanical properties for varying design parameters. Consequently, flexural tests are undertaken following the ISO 178:2019 for fused deposition modelling (FDM) samples, with three infill patterns (lines, gyroid and triangles), four thermoplastic materials, namely acrylonitrile butadiene styrene (ABS), polylactic acid (PLA and PLA+), and polyethylene terephthalate glycol (PETG), and infill densities (print material to part volume ratio) ranging from 0.10 to 1.00. Here we show that (i) the modulus and strength are independent of the tested infill types; (ii) the mechanical properties increase linearly with infill density; and (iii) considering mechanical properties, mass and cost, PLA+ appears as the most suitable overall material choice, with PETG appropriate for strength-driven, low-cost applications. Ultimately, PLA+ is applied to the design for manufacturing and assembly (DFMA) case study of an Iron Man helmet. These findings provide novel insights into the variations of mechanical properties with infill type, density and material for 3D printing applications and may contribute to future development in lightweight and cost-effective additive manufacturing.

To address the anisotropy of mechanical properties and the challenge of removing support materials in lattice structures fabricated using fused deposition modeling (FDM), this study is inspired by traditional woodworking mortise and tenon joints. A hexagonal interlocking mortise lattice structure was designed, and mortise and tenon lattice structures (MTLSs) with various parameters were fabricated. Compared with the traditional integrated forming lattice structure (IFLS), the MTLS exhibits maximum reductions in side surface roughness (Ra), printing time, and material consumption of 74.87%, 25.55%, and 52.21%, respectively. In addition to enhancing surface quality and printing efficiency, the MTLS also exhibited superior mechanical properties. The uniaxial compression test results show that the specific strength, energy absorption (EA), and specific energy absorption (SEA) of the MTLS exhibit maximum increases of 51.22%, 894.59%, and 888.39%, respectively, compared with the IFLS. Moreover, the effects of strut angle and thickness on the lattice structure were analyzed. Smaller strut angles and larger strut thicknesses endowed greater strength, while smaller angles contributed to higher energy absorption. This study proposes a novel approach for designing lattice structures in additive manufacturing.

The advent of additive manufacturing has revolutionized the prototyping, design, and manufacturing of new and innovative products. Through the use of Fused Deposition Modeling (FDM), parts can be manufactured using different sparse infill percentages (the percentage of void space within a printed object) and sparse infill patterns (the lattice structure of material within the object). Through the manipulation of these variables, the same material and part can have different compressive and flexural properties. In addition to sparse infill, the thermoplastics used in fused deposition modeling can be reinforced with carbon fiber strands to create a printable composite material capable of strengthening and stiffening parts without increasing the amount of material used. This experiment evaluated the yield stress performance of 3DXTech’s CarbonX PETG+CF filament printed at varying infill densities and infill patterns in hopes of characterizing the minimum structural properties of commonly used lattice structures. The infill patterns were evaluated at 15%, 35%, 50%, and 75% along with a baseline 100% infill with no internal pattern. The two-dimensional infill patterns (infill patterns that do not vary along the z-axis) rectilinear and triangles along with the three-dimensional infill patterns (infill patterns that vary along the z-axis) cubic, gyroid, and 3D honeycomb were tested on the surface perpendicular to the print orientation in order to test the minimum compressive strength of each specimen. All test specimens used were derived from the same STL document and manufactured by Bambo Lab’s X1 Carbon at a resolution of 0.2mm with a nozzle temperature of 260 oC, and the printer was programmed using the g-code created by Bambu Lab’s Bambu Studio slicing software. For each infill density and infill pattern, four test specimens were tested to failure using the Shimadzu Autograph AGX-V2 and its associated software. The yield strength, absolute strength, and stress vs. strain graph of each specimen were derived from the data collected along with photography and projectional radiography of the failure mode. The findings of this research can be summarized as three-dimensional infill patterns being characterized as having larger yield stresses than two dimensional infill patterns along their weakest face, increasing infill density universally increases the yield stress of the object, and objects of higher infill densities buckle at the middle of the specimen, acting as uniform bodies, while lower infill densities tend to buckle at the top and bottom of the specimen. More research is needed to further characterize the effects of stress on PETG-CF lattice structures, but this research can be used to guide the selection of print parameters depending on expected compressive loads.

Additively manufactured injection mould tooling incorporating gradient density lattice structures for mass and energy reduction

Additive manufacturing (AM) has begun to replace traditional fabrication because of its advantages, such as easy manufacturing of parts with complex geometry, and mass production. The most important limitation of AM is that dimensional accuracy cannot be achieved in all parts. Dimensional accuracy is essential for high reliability, high performance, and useful final products. This study investigates the impact of printing parameters on the dimensional accuracy of samples fabricated through fused deposition modeling (FDM), an additive manufacturing (AM) method utilizing polylactic acid (PLA) material. The experimental design process was performed using Taguchi methodology. ANOVA was used to determine the most important parameter affecting accuracy. Based on experimental studies, the optimal printing parameters for parts are determined as follows: concentric infill pattern, 3 mm wall thickness, 70% infill density, and a layer thickness of 200 μm. Artificial neural network (ANN) was used in the evaluation and prediction of the results. The R‐square (R2) performance evaluation criterion was above 95% from the ANN results. This value shows that the results are significant. The data acquired from this study may assist in identifying optimal parameters that contribute to the fabrication of samples with high dimensional accuracy using the FDM method.