Compression Performance and Deformation Behavior of 3D-Printed PLA-Based Lattice Structures

Thermo-Mechanical Behavior and Strain Rate Sensitivity of 3D-Printed Polylactic Acid (PLA) below Glass Transition Temperature (Tg)

This study investigated the thermomechanical behavior of 4D-printed polylactic acid (PLA), focusing on its response to varying temperatures and strain rates in a wide range below the glass transition temperature (Tg). The material was characterized using tension, compression, and dynamic mechanical thermal analysis (DMTA), confirming PLA's strong dependency on strain rate and temperature. The glass transition temperature of 4D-printed PLA was determined to be 65 °C using a thermal analysis (DMTA). The elastic modulus changed from 1045.7 MPa in the glassy phase to 1.2 MPa in the rubber phase, showing the great shape memory potential of 4D-printed PLA. The filament tension tests revealed that the material's yield stress strongly depended on the strain rate at room temperature, with values ranging from 56 MPa to 43 MPA as the strain rate decreased. Using a commercial FDM Ultimaker printer, cylindrical compression samples were 3D-printed and then characterized under thermo-mechanical conditions. Thermo-mechanical compression tests were conducted at strain rates ranging from 0.0001 s-1 to 0.1 s-1 and at temperatures below the glass transition temperature (Tg) at 25, 37, and 50 °C. The conducted experimental tests showed that the material had distinct yield stress, strain softening, and strain hardening at very large deformations. Clear strain rate dependence was observed, particularly at quasi-static rates, with the temperature and strain rate significantly influencing PLA's mechanical properties, including yield stress. Yield stress values varied from 110 MPa at room temperature with a strain rate of 0.1 s-1 to 42 MPa at 50 °C with a strain rate of 0.0001 s-1. This study also included thermo-mechanical adiabatic tests, which revealed that higher strain rates of 0.01 s-1 and 0.1 s-1 led to self-heating due to non-dissipated generated heat. This internal heating caused additional softening at higher strain rates and lower stress values. Thermal imaging revealed temperature increases of 15 °C and 18 °C for strain rates of 0.01 s-1 and 0.1 s-1, respectively.

Investigation of Additive-Manufactured Carbon Fiber-Reinforced Polyethylene Terephthalate Honeycomb for Application as Non-Pneumatic Tire Support Structure

A non-pneumatic tire (NPT) overcomes the shortcomings of a traditional pneumatic tire such as wear, punctures and blowouts. In this respect, it shows great potential in improving driving safety, and has received great attention in recent years. In this paper, a carbon fiber-reinforced polyethylene terephthalate (PET/CF) honeycomb is proposed as a support structure for NPTs, which can be easily prepared using 3D printing technology. The experimental results showed that the PET/CF has high strength and modulus and provides excellent mechanical properties. Then, a finite element (FE) model was established to predict the compression performance of auxetic honeycombs. Good agreement was achieved between the experimental data and FE analysis. The influence of the cell parameters on the compressive performance of the support structure were further analyzed. Both the wall thickness and the vertically inclined angle could modulate the mechanical performance of the NPT. Finally, the application of vertical force is used to analyze the static load of the structure. The PET/CF honeycomb as the support structure of the NPT showed outstanding bearing capacity and stiffness in contrast with elastomer counterparts. Consequently, this study broadens the material selection for NPTs and proposes a strategy for manufacturing a prototype, which provides a reference for the design and development of non-pneumatic tires.

The Structural, Thermal and Morphological Characterization of Polylactic Acid/Β-Tricalcium Phosphate (PLA/Β-TCP) Composites upon Immersion in SBF: A Comprehensive Analysis

Biocomposite films based on PLA reinforced with different β-TCP contents (10%, 20%, and 25%wt.) were fabricated via solvent casting and immersed in SBF for 7, 14, and 21 days. The bioactivity, morphological, and thermal behavior of composites with immersion were studied using scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) microanalysis, weight loss (WL), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and gel permeation chromatography (GPC). This broad analysis leads to a deeper understanding of the evolution of the polymer-filler interaction with the degradation of the biocomposites. The results showed that β-TCP gradually evolved into carbonated hydroxyapatite as the immersion time increased. This evolution affected the interaction of β-TCP with PLA. PLA and β-TCP interactions differed from PLA and carbonated hydroxyapatite interactions. It was observed that β-TCP inhibited PLA hydrolysis but accelerated the thermal degradation of the polymer. β-TCP retarded the cold crystallization of PLA and hindered its crystallinity. However, after immersion in SBF, particles accelerated the cold crystallization of PLA. Therefore, considering the evolution of β-TCP with immersion in SBF is crucial for an accurate analysis of the biocomposites' degradation. These findings enhance the comprehension of the degradation mechanism in PLA/β-TCP, which is valuable for predicting the degradation performance of PLA/β-TCP in medical applications.

Effects of void defects on the mechanical properties of biphasic calcium phosphate nanoparticles: A molecular dynamics investigation

Porous biphasic calcium phosphate (BCP) ceramics are widely used in bone tissue engineering, and the mechanical properties of BCP implants must be reliable. However, the effects of pore structure (e.g., shape and size) on the mechanical properties are not well understood. In this study, we used molecular dynamics simulations to investigate the influence of pore shape and size on the mechanical behavior of BCP nanoparticles. BCP void models with cylindrical and cuboid pores ranging from 2 to 16 nm in diameter were constructed, and the elastic moduli were calculated. In addition, uniaxial tensile and compressive tests were performed on the models. We found that the pore size had a more significant impact on the mechanical properties of BCP than pore shape. Further, the elastic moduli decreased nonlinearly with increasing pore size. In addition, the tensile and compressive strength also decreased with the increase in pore size, but the ductility improved. Furthermore, deformation and fracture were more likely to occur near the pores and at the phase interfaces as a result of high atomic local strain in the calcium-deficient hydroxyapatite area. The results of this work reveal the effects of pore parameters on the mechanical properties of porous BCP at the nanometer level, which may aid the design of improved porous and multiphase CaP-based biomaterials for bone regeneration.

Machine Learning Algorithms for Predicting Mechanical Stiffness of Lattice Structure-Based Polymer Foam

Polymer foams are extensively utilized because of their superior mechanical and energy-absorbing capabilities; however, foam materials of consistent geometry are difficult to produce because of their random microstructure and stochastic nature. Alternatively, lattice structures provide greater design freedom to achieve desired material properties by replicating mesoscale unit cells. Such complex lattice structures can only be manufactured effectively by additive manufacturing or 3D printing. The mechanical properties of lattice parts are greatly influenced by the lattice parameters that define the lattice geometries. To study the effect of lattice parameters on the mechanical stiffness of lattice parts, 360 lattice parts were designed by varying five lattice parameters, namely, lattice type, cell length along the X, Y, and Z axes, and cell wall thickness. Computational analyses were performed by applying the same loading condition on these lattice parts and recording corresponding strain deformations. To effectively capture the correlation between these lattice parameters and parts' stiffness, five machine learning (ML) algorithms were compared. These are Linear Regression (LR), Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). Using evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), all ML algorithms exhibited significantly low prediction errors during the training and testing phases; however, the Taylor diagram demonstrated that ANN surpassed other algorithms, with a correlation coefficient of 0.93. That finding was further supported by the relative error box plot and by comparing actual vs. predicted values plots. This study revealed the accurate prediction of the mechanical stiffness of lattice parts for the desired set of lattice parameters.

Prediction of Mechanical Properties for Carbon fiber/PLA Composite Lattice Structures Using Mathematical and ANFIS Models

This study investigates the influence of design, relative density (RD), and carbon fiber (CF) incorporation parameters on mechanical characteristics, including compressive modulus (E), strength, and specific energy absorption (SEA) of triply periodic minimum surface (TPMS) lattice structures. The TPMS lattices were 3D-printed by fused filament fabrication (FFF) using polylactic acid (PLA) and carbon fiber-reinforced PLA(CFRPLA). The mechanical properties of the TPMS lattice structures were evaluated under uniaxial compression testing based on the design of experiments (DOE) approach, namely, full factorial design. Prediction modeling was conducted and compared using mathematical and intelligent modeling, namely, adaptive neuro-fuzzy inference systems (ANFIS). ANFIS modeling allowed the 3D printing imperfections (e.g., RD variations) to be taken into account by considering the actual RDs instead of the designed ones, as in the case of mathematical modeling. In this regard, this was the first time the ANFIS modeling utilized the actual RDs. The desirability approach was applied for multi-objective optimization. The mechanical properties were found to be significantly influenced by cell type, cell size, CF incorporation, and RD, as well as their combination. The findings demonstrated a variation in the E (0.144 GPa to 0.549 GPa), compressive strength (4.583 MPa to 15.768 MPa), and SEA (3.759 J/g to 15.591 J/g) due to the effect of the studied variables. The ANFIS models outperformed mathematical models in predicting all mechanical characteristics, including E, strength, and SEA. For instance, the maximum absolute percent deviation was 7.61% for ANFIS prediction, while it was 21.11% for mathematical prediction. The accuracy of mathematical predictions is highly influenced by the degree of RD deviation: a higher deviation in RD indicates a lower accuracy of predictions. The findings of this study provide a prior prediction of the mechanical behavior of PLA and CFRPLA TPMS structures, as well as a better understanding of their potential and limitations.

Preparation of 3D printed calcium sulfate filled PLA scaffolds with improved mechanical and degradation properties

Scaffold is one of the key components for tissue engineering application. Three-dimensional (3D) printing has given a new avenue to the scaffolds design to closely mimic the real tissue. However, material selection has always been a challenge in adopting 3D printing for scaffolds fabrication, especially for hard tissue. The fused filament fabrication technique is one of the economical 3D printing technology available today, which can efficiently fabricate scaffolds with its key features. In the present study, a hybrid polymer-ceramic scaffold has been prepared by combining the benefit of synthetic biodegradable poly (lactic acid) (PLA) and osteoconductive calcium sulphate (CaS), to harness the advantage of both materials. Composite PLA filament with maximum ceramic loading of 40 wt% was investigated for its printability and subsequently scaffolds were 3D printed. The composite filament was extruded at a temperature of 160 °C at a constant speed with an average diameter of 1.66 ± 0.34 mm. PLA-CaS scaffold with ceramic content of 10%, 20%, and 40% was 3D printed with square pore geometry. The developed scaffolds were characterized for their thermal stability, mechanical, morphological, and geometrical accuracy. The mechanical strength was improved by 29% at 20 wt% of CaS. The porosity was found to be 50-60% with an average pore size of 550 µm with well-interconnected pores. The effect of CaS particles on the degradation behaviour of scaffolds was also assessed over an incubation period of 28 days. The CaS particles acted as porogen and improved the surface chemistry for future cellular activity, while accelerating the degradation rate.

On the Behavior of Honeycomb, Grid and Triangular PLA Structures under Symmetric and Asymmetric Bending

Additive manufacturing technologies enable the production of components with lightweight cores, by means of infills with various patterns and densities. Together with reduced mass and material consumption, infill geometries must ensure that strength and stiffness conditions are fulfilled. For the proper correlation of the infill type with the loading case of the part, the mechanical behavior of the infill along all three principal axes of inertia has to be known. In this paper, the behavior in symmetric and asymmetric bending of three infill geometries, commonly used in 3D printing processes (honeycomb, grid and triangles) is analyzed. The variations of deflections as a function of force orientation are presented, showing that honeycomb and triangular structures exhibit similar behaviors along the Y and Z principal axes of inertia. Furthermore, the displacements obtained for the three types of structures are compared, in relation to the consumed volume of material. The larger displacements of the grid structure compared to the honeycomb and triangular structures are highlighted.

Evaluation and Comparison of Traditional Plaster and Fiberglass Casts with 3D-Printed PLA and PLA–CaCO3 Composite Splints for Bone-Fracture Management

Bone fractures pose a serious challenge for the healthcare system worldwide. A total of 17.5% of these fractures occur in the distal radius. Traditional cast materials commonly used for treatment have certain disadvantages, including a lack of mechanical and water resistance, poor hygiene, and odors. Three-dimensional printing is a dynamically developing technology which can potentially replace the traditional casts. The aim of the study was to examine and compare the traditional materials (plaster cast and fiberglass cast) with Polylactic Acid (PLA) and PLA–CaCO₃ composite materials printed using Fused Filament Fabrication (FFF) technology and to produce a usable cast of each material. The materials were characterized by tensile, flexural, Charpy impact, Shore D hardness, flexural fatigue, and variable load cyclic tests, as well as an absorbed water test. In addition, cost-effectiveness was evaluated and compared. The measured values for tensile strength and flexural strength decreased with the increase in CaCO₃ concentration. In the fatigue tests, the plaster cast and the fiberglass cast did not show normal fatigue curves; only the 3D-printed materials did so. Variable load cyclic tests showed that traditional casts cannot hold the same load at the same deflection after a higher load has been used. During these tests, the plaster cast had the biggest relative change (−79.7%), compared with −4.8 % for the 3D-printed materials. The results clearly showed that 3D-printed materials perform better in both static and dynamic mechanical tests; therefore, 3D printing could be a good alternative to customized splints and casts in the near future.

Sensitivity Analysis of a Bench-Scale Pyrolysis Model for Composite Materials: A Case Study of Poly(lactic acid)/Melamine/Ammonium Polyphosphate

On the basis of a well-developed bench-scale pyrolysis model that relates material composition to flammability, this paper applied mathematical simulations to explore the model sensitivity for the prediction of fire behavior of composite materials. A pyrolysis model for poly(lactic acid) blended with melamine and ammonium polyphosphate as the reference material was selected as the case for analysis. The model input parameters for simulations include the heat of reaction, apparent activation energy, and pre-exponential factor of 15 reactions, as well as the thermal conductivity, emissivity coefficient, absorption coefficient, and density of 17 condensed-phase components. Each reaction-related or component-related parameter was adjusted from 80% of the model value to 120% with a 5% or 10% gradient. Finally, 826 simulation cases in total were calculated for analysis. Both the mass loss rate and the heat release rate of each case were calculated to characterize the sensitivity, which showed the same pattern. Finally, seven primary reactions and five key condensed-phase components with high sensitivity were identified. The predicted fire behaviors are highly related to the kinetics of the reactions between virgin components or reactions where virgin components play an important role in, including the pyrolysis of melted poly(lactic acid), the first step in the pyrolysis of melamine, the first step in the pyrolysis of ammonium polyphosphate, the reaction between melted poly(lactic acid) and melamine, the reaction between ammonium polyphosphate and melamine, and further decomposition of the generated new condensed-phase component. Particularly, the activation energy of these reactions is of sensitivity larger than 5% or 15%. The heat of decomposition of pyrolysis of melted poly(lactic acid) also showed a sensitivity of 2%-5%. The pre-exponential factor of all reactions showed a sensitivity of less than 2%, which can be ignored. Inputting the proper density is important for the prediction of fire behavior as the sensitivity is larger than 2%. The sensitivity of the milligram-scale model was also processed and compared. These simulations provided a fundamental understanding of the sensitivity of thermophysical and chemical properties and thus provide advanced insights into fire behavior modeling and new composite material design.