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This paper aims to investigate the influence of post-curing temperature, post-curing time and gamma ray irradiation dose upon the tensile and compressive mechanical properties of the medical graded vat photopolymerization parts.

Medical graded vat photopolymerization specimens, made from photopolymer resin, were fabricated using bottom-up vat photopolymerization machine. Tensile and compressive tests were conducted to assess the mechanical properties. The specimens were categorized into uncured and post-curing groups. Temperature post-processing and/or gamma irradiation exposure were for post-curing specimens. The post-curing parameters considered included temperature levels of 50°C, 60°C and 70°C, with 1, 2, 3 and 4 h periods. For the gamma irradiation, the exposure doses were 25, 50, 75 and 100 kGy.

Post-curing improved the mechanical properties of medical graded vat photopolymerization parts for both tensile and compressive specimens. Post-curing temperature greater than 50°C or a prolonged post-curing period of more than 1 h made insignificant changes or deterioration in mechanical properties. The optimal post-curing condition was therefore a 50°C post-curing temperature with 1 h post-curing time. Exposure to gamma ray improved the compressive mechanical properties, but deteriorated tensile mechanical properties. Higher gamma irradiation doses could decrease the mechanical properties and also make the part more brittle, especially for doses more than 25 kGy.

The obtained results would be beneficial to the medical device manufacturer who fabricated the invasive temporary contact personalized surgical instruments by vat photopolymerization technique. In addition, it also raised awareness in excessive gamma sterilization in the medical graded vat photopolymerization parts.

The industrial application of continuous glass fabric-reinforced polymer composites (GFRPCs) is growing; however, the manufacturing boundedness of complex structures and the high cost of molds restrict their use. This research proposes a three-dimensional (3 D) printing process for GFRPCs that allows low-cost and rapid fabrication of complex composite parts.

The composite is manufactured using a digital light processing (DLP) based Vat-photopolymerization (VPP) process. For the composites, suitable resin material and glass fabrics are chosen based on their strength, stiffness, and printability. Jacob's working curve characterizes the curing parameters for adequate adhesion between the matrix and fabrics. The tensile and flexural properties were examined using UTM. The fabric distribution and compactness of the cured resin were analyzed in scanning electron microscopy.

The result showed that the object could print at a glass fabric content of 40 volume%. In DLP-based VPP printing technology, the adequate exposure time was found to be 30 seconds for making a GFRPC. The tensile strength and Young's modulus values were increased by 5.54 and 8.81 times, respectively than non-reinforced cured specimens. The flexural strength and modulus were also effectively increased to 2.8 and 3 times more than the neat specimens. In addition, the process is found to help fabricate the functional component.

The experimental procedure to fabricate GFRPC specimens through DLP-based AM is a spectacular experimental approach.

Three-dimensional (3D) printing is popular for many applications including the production of photocatalysts. This paper aims to focus on developing of 3D-printed photocatalyst-nano composite lattice structure. Digital light processing (DLP) 3D printing of photocatalyst composites was performed using photosensitive resin mixed with 0.5% Wt. of TiO₂ powder and varying amounts (0.025% Wt. to 0.2% Wt.) of graphene nanoplatelet powder. The photocatalytic efficiency of DLP 3D-printed photocatalyst TiO₂ composite was investigated, and the effects of nano graphite powder incorporation on the photocatalytic activity, thermal and mechanical properties were investigated.

Methods involve 3D computer-aided design modeling, printing parameters and comprehensive characterization techniques such as structural equation modeling, X-ray diffraction, thermogravimetric analysis, Fourier-transform infrared (FTIR) and mechanical testing.

Results highlight successful dispersion and characteristics of TiO₂ and graphene nanoplatelet (GNP) powders, intricate designs of 3D-printed lattice structures, and the influence of GNPs on thermal behavior and mechanical properties.

The study suggests applicability in wastewater treatment and environmental remediation, showcasing the adaptability of 3 D printing in designing effective photocatalysts. Future research should focus on practical applications and the long-term durability of these 3D-printed composites.

The use of continuous fiber-reinforced filaments improves the mechanical properties obtained with the fused filament fabrication (FFF) process. Yet, there is a lack of simulation tailored tools to assist in the design for additive manufacturing of continuous fiber composites. To build such models, a precise elastic model is required. As the porosity caused by interbead voids remains an important flaw of the process, this paper aims to build an elastic model integrating this aspect.

To study the amount of porosity, which could be a failure initiator, this study proposes a two step periodic homogenization method. The first step concerns the microscopic scale with a unit cell made of fiber and matrix. The second step is at the mesoscopic scale and combines the elastic material of the first step with the interbead voids. The void content has been set as a parameter of the model. The material models predicted with the periodic homogenization were compared with experimental results.

The comparison between periodic homogenization results and tensile test results shows a fair agreement between the experimental results and that of the numerical simulation, whatever the fibers’ orientations are. Moreover, a void content reduction has been observed by increasing the crossing angle from one layer to another. An empiric law giving the porosity according to this crossing angle was created. The model and the law can be further used for design evaluation and optimization of continuous fiber-reinforced FFF.

A new elastic model considering interbead voids and its variation with the crossing angle of the fibers has been built. It can be used in simulation tools to design high performance fused filament fabricated composite parts.

The purpose of this study is to determine the material extrusion (MEX) printability envelope of a new kind of low-viscosity powder-binder feedstocks using rheological properties.

Formulation of 13 feedstocks (variation of solid loading 60–67 Vol.% and thickening agent proportion 3–15 Vol.%) that were characterized and printed at different temperatures.

Three rheological models were successfully used to define the viscosity envelope, producing stable and defect-free printing. At a shear deformation rate experienced by the feedstock in the nozzle ranging from 100 to 300 s^–1, it was confirmed that metal injection molding (MIM) feedstocks exhibiting a low viscosity between 100 and 150 Pa s could be printed using an extrusion temperature as low as 85 °C.

MEX can be used in synergy with MIM to accelerate mold development for a new injected part or simply as a replacement for MIM when the cost of the mold becomes too high for very small production volumes.

Correlation between the rheological properties of this new generation of low-viscosity feedstocks and MEX printability has been demonstrated for the first time.

Polymer laser powder bed fusion (PBF-LB/P) is an additive manufacturing technology that is sustainable due to the possibility of recycling the powder multiple times and allowing the fabrication of gears without the aid of support structures and subsequent assembly. However, there are constraints in the process that negatively affect its adoption compared to other additive technologies such as material extrusion to produce gears. This study aims to demonstrate that it is possible to overcome the problems due to the physics of the process to produce accurate mechanism.

Technological aspects such as orientation, wheel-shaft thicknesses and degree of powder recycling were examined. Furthermore, the evolving tooth profile was considered as a design parameter to provide a manufacturability map of gear-based mechanisms.

Results show that there are some differences in the functioning of the gear depending on the type of powder used, 100% virgin or 50% virgin and 50% recycled for five cycles. The application of a groove on a gear produced with 100% virgin powder allows the mechanism to be easily unlocked regardless of the orientation and wheel-shaft thicknesses. The application of a specific evolutionary profile independent of the diameter of the reference circle on vertically oriented gears guarantees rotation continuity while preserving the functionality of the assembled mechanism.

In the literature, there are various studies on material aging and reuse in the PBF-LB/P process, mainly focused on the powder deterioration mechanism, powder fluidity, microstructure and mechanical properties of the parts and process parameters. This study, instead, was focused on the functioning of gears, which represent one of the applications in which this technology can have great success, by analyzing the two main effects that can compromise it: recycled powder and vertical orientation during construction.

This study aims to focus on how reactive diluents with mono- and di-functionalities affect the properties of resin formulation developed from bioderived precursors. A hydroxyethyl methacrylate (HEMA) terminated urethane acrylate oligomer was synthesized and characterized to study its application in stereolithography 3D printing with different ratios of isobornyl acrylate and hexanediol diacrylate.

Polyester polyol was synthesized from suberic acid and butanediol. Additionally, isophorone diisocyanate, polyester polyol and HEMA were used to create urethane acrylate oligomer. Fourier transform infrared spectroscopy and ¹H NMR were used to characterize the polyester polyol and oligomer. Various formulations were created by combining oligomer with reactive diluents in concentrations ranging from 0% to 30% by weight and curing with ultraviolet (UV) radiation. The cured coatings and 3D printed specimens were then evaluated for their properties.

The findings revealed an improvement in thermal stability, contact angle value, tensile strength and surface properties of the product which indicated its suitability for use as a 3D printing material.

This study discusses how oligomers that have been cured by UV radiation with mono- and difunctional reactive diluents give excellent coating characteristics and demonstrate suitability and stability for 3D printing applications.

The purpose of this study, most recent advancements in threedimensional (3D) printing have focused on the fabrication of components. It is typical to use different print settings, such as raster angle, infill and orientation to improve the 3D component qualities while fabricating the sample using a 3D printer. However, the influence of these factors on the characteristics of the 3D parts has not been well explored. Owing to the effect of the different print parameters in fused deposition modeling (FDM) technology, it is necessary to evaluate the strength of the parts manufactured using 3D printing technology.

In this study, the effect of three print parameters − raster angle, build orientation and infill − on the tensile characteristics of 3D-printed components made of three distinct materials − acrylonitrile styrene acrylate (ASA), polycarbonate ABS (PC-ABS) and ULTEM-9085 − was investigated. A variety of test items were created using a commercially accessible 3D printer in various configurations, including raster angle (0°, 45°), (0°, 90°), (45°, −45°), (45°, 90°), infill density (solid, sparse, sparse double dense) and orientation (flat, on-edge).

The outcome shows that variations in tensile strength and force are brought on by the effects of various printing conditions. In all possible combinations of the print settings, ULTEM 9085 material has a higher tensile strength than ASA and PC-ABS materials. ULTEM 9085 material’s on-edge orientation, sparse infill, and raster angle of (0°, −45°) resulted in the greatest overall tensile strength of 73.72 MPa. The highest load-bearing strength of ULTEM material was attained with the same procedure, measuring at 2,932 N. The tensile strength of the materials is higher in the on-edge orientation than in the flat orientation. The tensile strength of all three materials is highest for solid infill with a flat orientation and a raster angle of (45°, −45°). All three materials show higher tensile strength with a raster angle of (45°, −45°) compared to other angles. The sparse double-dense material promotes stronger tensile properties than sparse infill. Thus, the strength of additive components is influenced by the combination of selected print parameters. As a result, these factors interact with one another to produce a high-quality product.

The outcomes of this study can serve as a reference point for researchers, manufacturers and users of 3D-printed polymer material (PC-ABS, ASA, ULTEM 9085) components seeking to optimize FDM printing parameters for tensile strength and/or identify materials suitable for intended tensile characteristics.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.