The influence of manufacturing parameters of filament extrusion and extrusion-based Additive Manufacturing (AM), as well as different post processing techniques, on the electrical conductivity of 3D printed parts of graphene nanoplatelets (GNP)-reinforced acrylonitrile butadiene styrene (ABS) has been analyzed. The key role of the manufacturing parameters to obtain electrically conductive filaments and 3D printed parts has been demonstrated. Results have shown that an increase in extrusion speed, as well as lower land lengths, induces higher extrudate swelling, with the consequent reduction of the electrical conductivity. Additionally, filaments with lower diameter values, which result in a higher surface-to-cross-section ratio, have considerably lower electrical conductivities. These factors tune the values of the volume and surface electrical conductivity between 10⁻⁴–10⁰ S/m and 10⁻⁸–10⁻³ S/sq, respectively. The volume and surface electrical conductivity considerably diminished after 3D printing. They increased when using higher printing layer thickness and width and were ranging between 10⁻⁷–10⁻⁴ S/m and 10⁻⁸–10⁻⁵ S/sq, respectively. This is attributed to the higher cross section area of the individual printed lines. The effect of different post processing (acetone vapor polishing, plasma and neosanding, which is a novel finishing process) on 3D printed parts in morphology and surface electrical conductivity was also analyzed.

Digital technology has brought about an important evolution in the dental field, especially in diagnostic, treatment/surgery planning and design/manufacturing processes. From early developments in computer-aided design and computer-aided manufacturing (CAD/CAM) systems to the integration of Artificial Intelligence (AI) and Additive Manufacturing (AM), this review aims to provide an overview of major advancements which have formed digital dentistry. This review synthesizes historical and recent literature on digital technologies used in dentistry, categorizing them into six key domains: diagnostic tools, scanning systems, design and manufacturing technologies, treatment planning and simulation, patient management, and artificial intelligence/machine learning applications. The study highlights the applications, benefits, and current limitations of each category. Digital technologies have significantly improved patient comfort, clinical efficiency andtreatment accuracy.Tools such as cone-beam computed tomography (CBCT), intraoral scanners, CAD/CAM systems, and digital workflow software allow for more predictable and personalized care. AI-driven diagnostics and treatment planning, combined with additive manufacturing, further optimize outcomes. Despite these advances, challenges such as high initial costs, technical barriers, and training needs still block full acceptance in some practices. Digital dentistry has revolutionized dental care delivery and continues to advance rapidly. Its integration offers numerous clinical and operational advantages, yet further innovation and strategic implementation are required to ensure equitable access and sustainable assumption across the dental field.

Hybrid Manufacturing combines the advantages of Additive Manufacturing (AM) and Subtractive Manufacturing on a single machine. Although previous research has provided a good background of the manufacturing parameters, the analysis is often carried out separately and there is a lack of combined knowledge. The purpose of this research is to examine the influence of the main manufacturing parameters involved in AM and subtractive processes using different variables. Particularly, the study is focused on the Material Extrusion process concerning the layer height, fill angle and fill density; as well as two factors related to machining, including stepover and pass direction. The parameters that were examined include the dimension, hardness, flatness, weight and roughness. A multifactorial Design of Experiments was proposed with a total of 64 samples. Next, a statistical analysis was carried out to assess the influence of the different groups on the response variables. Finally, a decision table presented and facilitated the selection of parameters depending on the desired objectives, leading to a framework that was applied to a case study for validation. This decision guide could enable designers and engineers to select the best strategy for a specific application, leading to a more efficient approach for manufacturing.

Neurosurgery is one of the medical specialties in which the practical training of students is more limiting since it requires a high degree of preparation for the interventions to be satisfactory. That is why the manufacture of synthetic models through additive manufacturing (AM) arises to develop the skills that the neurosurgeon requires. The present work is aimed at validating the use of AM for the neurosurgery training. To this regard, a meningioma case study was considered, and suitable materials and more appropriate AM technology were identified for a low-cost production of synthetic models of both skulls and brains with tumors. The skull was manufactured by material extrusion AM with two materials, a commercial composite filament composed of polylactic acid (PLA) with calcium carbonate (used in the area to be treated during the cutting process, due to its mechanical properties more comparable to those of the native bone, with 30% infill density) and standard PLA without additives (used in the rest of the model, with 20% infill density). On the other hand, different casting silicones in different proportions were tested under compression molding to find the best combination to mimic the brain and tumor. Ten synthetic models of a real-case meningioma were manufactured and used as training material by students in the neurosurgery sector, who rated the proposed training approach very highly, considering the employment of printed models as a key resource for improving their surgical skills.

The manufacturing industry, in general, and the plastic industry, in particular, have been developing new materials and process methods that need a correct study and optimization. Nowadays, the main approach to optimize these processes is using numerical methods and, in the case of particulate materials, the Discrete Elements Method to estimate the particles interactions. But those mathematical models use some parameters that depend on the material and must be calibrated, thus requiring an important computational and experimental cost. In this study, we integrate different speed-up procedures and present a general calibration method of Low-Density Polyethylene particles, to obtain the calibrated solid density and Poisson’s ratio of the material, the restitution, static and rolling friction factors in the particle-to-particle and particle-to-wall interactions, and the contact model variables (damping factor, stiffness factor, and energy density). For this calibration, four different tests were carried out, both experimentally and with simulations, obtaining the bulk density, the repose and shear angles, and the dropped powder. All these response variables were compared between simulations and experimental tests, and using genetic algorithms, the input parameters (design variables) were calibrated after 85 iterations, obtaining a Mean Absolute Percentage Error of the response variables lower than 2% compared to the experimental results.

Vaginal delivery assisted with forceps (FAVD) is an obstetric technique that involves the extraction of the fetus by traction and/or expansion of the birth canal with an instrument. Currently, the use of forceps allows for the resolution of pathological situations that would otherwise require a cesarean section with the risks of morbidity and mortality that this surgery entails. For this reason, there is a growing interest in increasing the percentage of FAVD, after obstetricians feel more confident after practicing in simulation workshops with mannequins and acquiring the necessary competencies for their correct use. The forceps is an instrument that requires a precise introduction and placement, so an electromagnetic tracking tool has been developed that knows, with high precision, the position of the instrument both inside the vagina and around the fetal head. With this, childbirth training is complemented by a virtual simulation, in real-time, that allows the internal position of the forceps to be seen, thus facilitating the learning and improvement of the introduction and placement technique. For this, the instrument must not contain any ferrous residue that interferes electromagnetically, so commercial forceps (made of stainless steel) are not valid. In this context, the reproduction of forceps has been proposed through additive manufacturing based on material extrusion (MEX). To obtain forceps that are as realistic as possible in terms of rigidity compared to commercial models, a nylon filament reinforced with carbon microfibers (Onyx) has been used to which continuous carbon fiber is added in successive layers. Given the limitations in the printing volume of the technology used (Mark Two), numerical simulations were carried out by finite element analysis and different proposals for dividing each piece and subsequent union by adhesives were made, finally developing the final solution. Keywords: additive manufacturing; carbon fiber extrusion; vaginal delivery forceps; medical training; electromagnetic tracking.