The wall thickness and build direction of PA 12 parts manufactured via SLS are currently being investigated for applications requiring low weight and good mechanical properties (ultimate tensile strength, elongation at break, elastic modulus, and hardness). Current design guidelines for the SLS process include recommendations about the influence of build direction on the mechanical properties of the part; however, the recommended minimum wall thickness only considers the process's manufacturability, so this study aims to determine the wall thickness and build orientation conditions that present the slightest difference in mechanical properties, considering different conditions as vertical, horizontal, and transverse build directions and wall thicknesses of 2.0, 2.5 and 3.0 mm. Statistical differences were found between build direction, ultimate tensile stress, and elongation at break and hardness. It was observed that the significant differences in the direction of impression were between the vertical direction versus the transverse and horizontal directions. The differences were between the values of 2.0 mm versus 2.5 and 3.0 mm for the wall thickness. The mechanical properties between these last two thicknesses do not present significant differences, so it is suggested that parts manufactured with PA 12 by SLS with thicknesses of 2.5 mm could have the same tensile mechanical properties as those of 3.0 mm. The horizontal and transverse directions with a 2.5–3.0 mm thickness showed the highest mechanical properties with an ultimate tensile stress of ≈ 43 MPa, a modulus of elasticity of 2.2 GPa, and an elongation at break of 16%–18% and a hardness of ≈ 75 Shore D.

This study aims to determine the mechanical properties of parts manufactured by Fused Deposition Modeling (FDM) using three biocompatible polymer materials: Polylactic Acid (PLA), Polycaprolactone (PCL) and Lay-Fomm 40. Also, it was analyzed the influence of different printing parameters, material selection, infill percentage, and raster angle, over the mechanical properties. The samples were subjected to tension and compression tests using a universal testing machine, and elastic modulus, yield stress, and ultimate stress were obtained from the stress-strain curves. PLA samples have the highest elastic modulus, yield stress and ultimate stress for both compression and tension tests, for example, the ultimate tensile stress with infill percentage of 30 % and raster angle of 0-90° has an average value of 41.20 MPa, while PCL samples had an ultimate tensile stress average value of 9.68 MPa. On the other hand, Lay-Fomm40 samples had the highest elongations, with percentage values between 300 and 600 %. Finally, ANOVA analysis showed that the choice of the material is the leading printing parameter that contributes to the mechanical properties, with percentages of 84.20% to elastic modulus, 93.30% to yield stress, and 82.44% to ultimate stress. The second important factor is the raster angle, with higher strengths for the 0-90° when compared to 45-135°. On the other hand, the contribution of the infill percentage to the mechanical properties was no statistically significant. The obtained results could be useful for material selection and 3D printing parameters definition for additive manufacturing of scaffolds, implants, and other structures for biomedical and tissue engineering applications.

Machine learning techniques are increasingly employed to predict material properties and reduce mechanical testing costs. This study uses tensile load‒displacement data to predict the breaking force, maximum force, ultimate tensile strength (UTS), and breaking strength of nylon PA 12 specimens manufactured via selective laser sintering (SLS). To address variations in specimen ductility, linear and fourth-degree polynomial equalisations were applied to the data. The results indicate that polynomial equalisation generally achieves superior predictive performance across various machine learning models. While all the models exhibit a decline in performance metrics with a reduction in the observation matrix data, several demonstrate strong predictive capabilities even with reduced data sets. Specifically, when using 20% of the observation matrix data (corresponding to the elastic zone) are used, the kernel ridge, elastic net, and Lasso regression models achieve R ² values of 0.987, 0.931, and 0.987, respectively, for predicting breaking strength. For maximum force prediction with 20% of the data, the kernel ridge, support vector machine, and Lasso regression models resulted in R ² values of 0.975, 0.960, and 0.930, respectively. However, the prediction accuracy for UTS and ultimate strength at break is lower, with the Elastic Net model achieving R ² values of 0.703 and 0.682, respectively, with 20% of the data. These findings suggest that while certain mechanical properties can be accurately predicted using data solely from the elastic zone, minimising specimen waste and testing time, the prediction of other properties requires data beyond the elastic limit. This study highlights the potential of machine learning to optimise and streamline the quality control process in additive manufacturing.

We used a three-dimensional rigid body spring model (RBSM) to compare the contact force distributions on the acetabular surface of the infant hip joint that are produced by three orthopedic treatments for developmental dysplasia of the hip (DDH). We analyzed treatments using a Pavlik harness, a generic rigid splint, and a spica cast. The joint geometry was modeled from tomography images of a 1-year-old female. The articular cartilage was modeled as linear springs connecting the surfaces of the acetabulum and the femoral head, whereas the femur and the hip bone were considered as rigid bodies. The hip muscles were modeled as tensile-only preloaded springs. The treatments with the Pavlik harness and the generic rigid splint were modeled for an infant in supine position with a hip flexion angle of 90 deg. Also, since rigid splints are often recommended when children are initiating their gait phase, we modeled the treatment with the infant in standing position. For the spica cast, we only considered the infant in standing position with a flexion angle of 0 deg, and the fixation bar at two heights: at the ankle and at the knee. In order to analyze the effect of the hip abduction angle over the contact force distribution, different abduction angles were used for all the treatments modeled. We have found that the treatments with the infant in supine position, with a flexion angle of 90 deg and abduction angles between 60 deg and 80 deg, produce a more homogenous contact force distribution compared to those obtained for the treatments with the infant in standing position.

Additive manufacturing via selective laser sintering (SLS) enables the rapid production of geometrically complex polyamide 12 (PA12) components. However, conventional pointwise analysis techniques often overlook the full depth of continuous experimental datasets, thus limiting the interpretation of structure–function relationships that are essential to high-performance design. This study employs functional data analysis (FDA) to elucidate the microstructural, chemical, thermal, and mechanical behaviours of SLS-fabricated PA12, focusing on the effects of build orientation (horizontal, transverse, vertical) and wall thickness (2.0–3.0 mm). The samples were produced via a commercial SLS platform and characterised via X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and tensile testing. The FDA was applied to raw, normalised, and first derivative datasets via Python’s Scikit-FDA package, increasing the sensitivity to latent material variations. The findings demonstrate that the build orientation has a marked influence on the crystallinity and mechanical performance: horizontal builds yield narrower gamma-phase XRD peaks, greater structural order, and enhanced tensile properties, whereas vertical builds exhibit broader peak dispersion and greater thermal sensitivity. The wall thickness effects were minor, with only isolated flux-related anomalies. The FTIR spectra confirmed the consistent chemical stability across all the conditions. The FDA successfully identified subtle transitions and anisotropies that eluded traditional methods, underscoring its methodological strength for advanced polymer characterisation. These insights offer practical guidance for refining SLS process parameters and improving predictive design strategies in polymer-based additive manufacturing.

This study investigated the effects of multiple laser sintering (LS) cycles on a polyamide 12 powder mixture of 70% recycled material and 30% virgin polymer. This research aimed to understand how repeated LS processing influences this specific powder mixture’s thermal and structural properties, which is crucial for optimising its performance in additive manufacturing applications. A thermal analysis revealed significant changes in the thermal behaviour of the powder over successive build cycles. Specifically, there was an observed increase in both the melting temperature and the crystallinity of the powder, alongside a notable decrease in the crystallisation temperature. These alterations suggest that the repeated LS cycles affect the thermal profiles and potentially enhance the material’s stability and usability in additive manufacturing processes. Additionally, a particle size distribution analysis indicated statistically significant differences between the initial and post-sintering states of the powder. These differences are significant as they can influence factors such as flowability and packing density, which are critical for the efficiency of additive manufacturing applications. Microscopic observations further revealed a strong correlation between the crystal morphologies and particle shapes, indicating that the structural changes occurring during processing are inter-related. The relationship underscores the importance of understanding microstructural evolution and the mechanical properties of the final printed products. These findings provide crucial insights into the microstructural evolution and thermal behaviour of recycled PA12 powder during multiple LS processing cycles. This study aids in developing practical strategies for sustainable and efficient powder recycling within the realm of additive manufacturing. By examining the intricate dynamics at play, the research opens avenues for enhancing the performance and environmental sustainability of 3D printing technologies, making them more accessible for various industrial applications.