Predicting trabecular arrangement in the proximal femur: An artificial neural network approach for varied geometries and load cases.

Machine learning (ML) and deep learning (DL) approaches can solve the same problems as the finite element method (FEM) with a high degree of accuracy in a fraction of the required time, by learning from previously presented data. In this work, the bone remodelling phenomenon was modelled using feed-forward neural networks (NN), by gathering a dataset of density distribution comprising several geometries and load cases. The model should output for some point in the domain the its apparent density, taking into consideration the geometric and loading parameters of the model . After training. the trabecular distribution obtained with the NN was similar to the FEM while the analysis was performed in a fraction of the time, having shown a reduction from 1020 s to 0.064 s. It is expected that the results can be extended to different structures if a different dataset is acquired. In summary, the ML approach allows for significant savings in computational time while presenting accurate results.

Multiscale Homogenization Techniques for TPMS Foam Material for Biomedical Structural Applications.

Multiscale techniques, namely homogenization, result in significant computational time savings in the analysis of complex structures such as lattice structures, as in many cases it is inefficient to model a periodic structure in full detail in its entire domain. The elastic and plastic properties of two TPMS-based cellular structures, the gyroid, and the primitive surface are studied in this work through numerical homogenization. The study enabled the development of material laws for the homogenized Young's modulus and homogenized yield stress, which correlated well with experimental data from the literature. It is possible to use the developed material laws to run optimization analyses and develop optimized functionally graded structures for structural applications or reduced stress shielding in bio-applications. Thus, this work presents a study case of a functionally graded optimized femoral stem where it was shown that the porous femoral stem built with Ti-6Al-4V can minimize stress shielding while maintaining the necessary load-bearing capacity. It was shown that the stiffness of cementless femoral stem implant with a graded gyroid foam presents stiffness that is comparable to that of trabecular bone. Moreover, the maximum stress in the implant is lower than the maximum stress in trabecular bone.

Melt Electrospinning Writing of Mesh Implants for Pelvic Organ Prolapse Repair.

Over the past decade, melt electrospinning writing has attracted renewed attention. When combined with three-dimensional (3D) printing capabilities, complex 3D structures can be produced, from ultrafine fibers in the absence of toxic solvents, making it particularly attractive to fabricate customized scaffolds and implants for medical applications. This research aimed to develop novel less stiff vaginal mesh implants for pelvic organ prolapse (POP) repair, matching the physiological biomechanics of vaginal tissues. The main objectives, to attain that goal, were: development of a melt electrospinning writing prototype, with additive manufacturing capability, to produce complex structures from micrometer scale fibers, in a direct 3D printing mode; and design and validate new concepts of biodegradable meshes/scaffolds with new geometries, for POP repair. The melt electrospinning writing prototype was built based on different modules. Biodegradable polycaprolactone was used to produce novel implants: three geometries and two fiber configurations were employed. The commercially available Restorelle® (Coloplast) mesh was used as a benchmark. Printed implants were analyzed via scanning electron microscopy (SEM) and uniaxial tensile testing. The SEM images showed that the geometry is generally well produced; however, some minor deviations are visible due to charge interactions. The tensile test results indicated that, regardless of the geometry, the samples showed an elastic behavior for smaller displacements; aplastic behavior dominates later stages. In the physiological range of deformation, the novel meshes (80 µm fiber diameter) matched the tissue properties (p > 0.05). The Restorelle mesh was significantly stiffer than vaginal tissue (p < 0.05) and novel meshes. The precision of the various geometrical patterns and fiber diameters produced highlights the success of the designed and built prototype equipment. Results showed that the biodegradable meshes produced are biomechanically more compatible with native tissue than commercial implants.

Screw-assisted 3D printing with granulated materials: a systematic review.

This paper presents a systematic review on extrusion additive manufacturing (EAM), with focus on the technological development of screw-assisted systems that can be fed directly with granulated materials. Screw-assisted EAM has gained importance as an enabling technology to expand the range of 3D printing materials, reduce costs associated with feedstock fabrication, and increase the material deposition rate compared to traditional fused filament fabrication (FFF). Many experimental printheads and commercial systems that use some screw-processing mechanism can be found in the literature, but the design diversity and lack of standard terminology make it difficult to determine the most suitable solutions for a given material or application field. Besides, the few previous reviews have offered only a glimpse into the topic, without an in-depth analysis about the design of the extruders and associated capabilities. A systematic procedure was devised to identify the screw-assisted EAM systems that can print directly from granulated materials, resulting in 61 articles describing different pieces of equipment that were categorized as experimental printheads and commercial systems, for small- and large-scale applications. After describing their main characteristics, the most significant extruder modifications were discussed with reference to the materials processed and performance requirements. In the end, a general workflow for the development of 3D printers based on screw extrusion was proposed. This review intends to provide information about the state-of-the-art screw-assisted EAM and help the academy to identify further research opportunities in the field.

Effect of Scan Strategies and Use of Support Structures on Surface Quality and Hardness of L-PBF AlSi10Mg Parts.

Additive manufacturing allows for a great degree of design freedom and is rapidly becoming a mainstream manufacturing process. However, as in all manufacturing processes, it has its limitations and specificities. Equipping engineers with this knowledge allows for a higher degree of optimization, extracting the most out of this technology. Therefore, a specific part design was devised and created via L-PBF (Laser Powder Bed Fusion) using AlSi10Mg powder. Certain parameters were varied to identify the influence on material density, hardness, roughness, residual stress and microstructures. It was found that on heat treated parts laser pattern strategy is one of the most influential aspects, showing that chessboard and stripes 67° improved outcome; average Ra roughness varied between 8-12 µm, residual stress was higher on vertical surfaces than horizontal surfaces, with the combination of support structures and stripes 67° strategies generating the lowest residual stress (205 MPa on a lateral/vertical face), hardness was non-orientation dependent and larger on samples with chessboard fabrication strategies, while microstructures were composed of α-Al dendrites surrounded by Si particles. The distribution and grain size of the microstructure is dependent on location regarding melt pool and HAZ area. Furthermore, Al-Mg oxides were encountered on the surface, along with pores generating from lack of fusion.