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This study aims to evaluate the enhancement in prosthetic supply chain capabilities resulting from the implementation of additive manufacturing (AM) technologies. The study presents an emerging model outlining the key areas that undergo changes when integrating 3D printing technologies into the prosthetic supply chain.

Employing a qualitative approach, data were collected through field observations and 31 in-depth interviews conducted within various Jordanian organizations associated with the prosthetic industry and 3D printing technologies.

The findings suggest that the adoption of 3D printing technologies improves the prosthetic supply chain’s capabilities in terms of customization, responsiveness, innovation, environmental sustainability, cost minimization and patient empowerment. The study sheds light on the specific areas affected in the prosthetic supply chain following the adoption of 3D printing technologies, emphasizing the overall improvement in supply chain capabilities within the prosthetic industry.

This study provides recommendations for governmental bodies and prosthetic organizations to maximize the benefits derived from the use of 3D printing technologies.

This study contributes as the first of its kind in exploring the impact of 3D printing technology adoption in the Jordanian prosthetic industry, elucidating the effects on the supply chain and identifying challenges for decision-makers in an emerging market context.

New sustainable approaches to fashion products are needed due to the demand for customization, better quality and cost reduction. Therefore, the decoration of fashion products using 3D printing technology can create a new direction for manufacturing science.

This study aims to optimize the 3D printing of soft TPU material on textiles. In the past decade, trials of using 3D printing in tailored fashion products have been done due to the 3D printing simplicity, low cost of materials and time reduction. Therefore, soft polymers can be multi-layer stepped-deposited smoothly with the fused filament fabrication process.

Even though there have been many attempts in the literature to 3D print multilayer polymer filaments directly onto textile fabrics by special-purpose 3D printers, only a few reports of decorative or personalized artefact 3D printing using open-platform filament material extrusion 3D printers. Printing speed, nozzle Z distance, textile fabric thickness and deposited strand height significantly affect 3D printing on textile fabric.

This study investigates the potential of 3D printing on textiles by changing the printing speed, nozzle hot end, Z distance and layer thickness. It presents two critical case studies of 3D printing soft thermoplastic polyurethane material on a cotton T-shirt and on a tulle textile to reveal the 3D printing on textile fabrics manufacturing challenges.

The purpose of this study is to identify and assess new risks in construction projects that use 3D printing.

A mixed approach of both qualitative and quantitative methods was used. Literature review was conducted to extract 30 risks of 3D printing in construction. A survey was then developed to assess the probability and impact of these risks. In total, 37 respondents, who have experience and/or knowledge of 3D printing, completed the survey. The risk priority was calculated using a fuzzy logic approach. The main benefit of the proposed model is being able to use numerical and linguistic data in the risk assessment model.

The results show that the main risks, in terms of priority, are lack of codes and regulations for 3D printing in construction, delay in government approvals, shortage in labour skilled in 3D printed construction, lack of knowledge and information of 3D printed design concepts and changes in 3D construction codes and regulations.

This paper fills an identified gap in the literature related to 3D printing in construction and provides insights into the key risks affecting this disruptive technology.

This study aims to explore the impact of experiencing virtual reality (VR) and three-dimensional (3D) printing during the design process on the creativity of interior design students in a luminaire design project.

This study used the case-study approach within the context of a nine-week luminaire design project. Collected data included self-reported interest and engagement of students from a Qualtrics questionnaire and the ratings of their creativity via the Creative Product Semantic Scale (CPSS) with two judges.

Descriptive statistics from the Qualtrics questionnaire indicated an overall high level of student interest and engagement with the VR and 3D printing learning experience. Paired t-tests from CPSS ratings of the two judges showed a moderate increase in novelty and a significant increase in style with the introduction of VR and 3D printing technologies, respectively.

Spearman’s correlations (rho) showed no statistical evidence for the relationships between CPSS ratings for creativity and students’ self-reported interest and engagement in VR and 3D printing learning experience.

Ample access time to VR technology and sufficient control over the 3D printing process are important for effective applications of Industry 4.0 technologies in organizations.

This study dissected the confounding variables in its results as practical considerations for intergrading VR and 3D printing technologies for organizations in Industry 4.0.

This study acknowledged VR and 3D printing technologies as simulants for interest and engagement, which benefit creativity.

This paper aims to investigate the application of 3D printing technology, particularly using sand-type materials, in the creation of artificial rock models for rock mechanics experimentation.

Using a comprehensive analysis, this research explores the utilization of 3D printing technology in rock mechanics. Sand-type materials are specifically investigated for their ability to replicate natural rock characteristics. The methodology involves a review of recent achievements and experimentation in this field.

The study reveals that sand-type 3D printing materials demonstrate comparable properties to natural rocks, including brittle characteristics, surface roughness, microstructural features and crack propagation patterns.

While the research establishes the viability of sand-type 3D printing materials, it acknowledges limitations such as the need for further exploration and validation. Generalizability may be constrained, warranting additional research to address these limitations.

This research contributes insights into the potential application of sand-type 3D printing materials in indoor rock physics experiments. The findings may guide future endeavors in fabricating rock specimens with consistent structures for practical rock mechanics applications.

The purpose of this study is to assess a novel method for creating tangible three-dimensional (3D) morphologies (scaled models) of neuronal reconstructions and to evaluate its cost-effectiveness, accessibility and applicability through a classroom survey. The study addresses the challenge of accurately representing intricate and diverse dendritic structures of neurons in scaled models for educational purposes.

The method involves converting neuronal reconstructions from the NeuromorphoVis repository into 3D-printable mold files. An operator prints these molds using a consumer-grade desktop 3D printer with water-soluble polyvinyl alcohol filament. The molds are then filled with casting materials like polyurethane or silicone rubber, before the mold is dissolved. We tested our method on various neuron morphologies, assessing the method’s effectiveness, labor, processing times and costs. Additionally, university biology students compared our 3D-printed neuron models with commercially produced counterparts through a survey, evaluating them based on their direct experience with both models.

An operator can produce a neuron morphology’s initial 3D replica in about an hour of labor, excluding a one- to three-day curing period, while subsequent copies require around 30 min each. Our method provides an affordable approach to crafting tangible 3D neuron representations, presenting a viable alternative to direct 3D printing with varied material options ensuring both flexibility and durability. The created models accurately replicate the fidelity and intricacy of original computer aided design (CAD) files, making them ideal for tactile use in neuroscience education.

The development of data processing and cost-effective casting method for this application is novel. Compared to a previous study, this method leverages lower-cost fused filament fabrication 3D printing to create accurate physical 3D representations of neurons. By using readily available materials and a consumer-grade 3D printer, the research addresses the high cost associated with alternative direct 3D printing techniques to produce such intricate and robust models. Furthermore, the paper demonstrates the practicality of these 3D neuron models for educational purposes, making a valuable contribution to the field of neuroscience education.

The current study aims to develop a 3D-printed continuous metal fiber-reinforced recycled thermoplastic composite using an in-nozzle impregnation technique.

Recycled acrylonitrile butadiene styrene (RABS) plastic was blended with virgin ABS (VABS) plastic in a ratio of 60:40 weight proportion to develop a 3D printing filament that was used as a matrix material, while post-used continuous brass wire (CBW) was used as a reinforcement. 3D printing was done by using a self-customized print head to fabricate the flexural, compression and interlaminar shear stress (ILSS) test samples to evaluate the bending, compressive and ILSS properties of the build samples and compared with VABS and RABS-B samples. Moreover, the physical properties of the samples were also analyzed.

Upon three-point bend, compression and ILSS testing, it was found that RABS-B/CBW composite 3D printed with 0.7 mm layer width exhibited a notable improvement in maximum flexural load (L_max), flexural stress at maximum load (sf_max), flex modulus (E_f) and work of fracture (WOF), compression modulus (E_c) and ILSS properties by 30.5%, 49.6%, 88.4% 13.8, 21.6% and 30.3% respectively.

Limited research has been conducted on the in-nozzle impregnation technique for 3D printing metal fiber-reinforced recycled thermoplastic composites. Adopting this method holds the potential to create durable and high-strength sustainable composites suitable for engineering applications, thereby diminishing dependence on virgin materials.

Herein, the authors report the effects of printing parameters, joining method, and annealing conditions on the structural performance of fusion-joined short-beam sections produced by additive manufacturing.

The authors first identified appropriate printing parameters for joining segmented short beams and then used those parameters to print and fusion-join segments with different configurations of stiffeners to form a longer section of a wing or small wind turbine blade structure.

It was found that the beams with three lateral and three base stiffening ribs give the highest flexural strength among the three beams investigated. Results on joined beams annealed at different conditions showed that annealing at 70 °C for 0.5 h yields higher performance than annealing at the same temperature for longer times. It is also found that in the case of the hot-plate-welded three-dimensional (3D)-printed structures, no annealing is needed for reaching a high strength-to-weight ratio, but annealing is helpful for maximizing the modulus-to-weight ratio. Both thermal buckling and edge wrapping were observed under annealing at 70°C for 0.5 h for 3D-printed beams comprising two lateral and four base stiffening plates.

Fusion-joining of additively manufactured segments is needed owing to the constraint in building volume of a typical commercial 3D-printer. However, study of the effect of process parameters is needed to quantify their effect on mechanical performance. This investigation has therefore identified key printing parameters and annealing conditions for fusion-joining short segments to form larger structures, from multiple 3D-printed sections, such as wind blade structures.

This study aims to evaluate the low energy impact characteristics of 3D printed carbon fiber thermoplastic and thermoset polymer composite using the Izod impact test. The effects of infill density are examined on the Izod impact properties of 3D printed thermoset polymer and thermoplastic composite specimens. Furthermore, a thorough investigation is conducted into the effect of heat treatment using a hot-air oven on both types of 3D printed composite specimens. To characterize the impact characteristics of each specimen, the fracture surfaces caused by impact load are inspected, and the fracture mechanism is studied using scanning electron micrographs.

Izod Impact specimens of thermoset (epoxy resin) and thermoplastic carbon fiber of different infill density (70, 75, 80, 85, 90 and 100%) are fabricated using the different fiber impregnation 3D printing process. To carry out the heat treatment process, printing of composites is done for each infill design from both thermoset and thermoplastic composites and the impact characteristics of specimens are evaluated on a pendulum test-rig using the ASTM D-256 standard. Using a scanning electron microscope, each fracture zone underwent four separate scanning processes, ranging in size from 2 µm to 100 µm.

The impact resistance of the 3D printed thermoset and thermoplastic composite material is significantly influenced by the type of fiber placement and infill density in the matrix substrate. Because of the weak interfacial strength between the layers of fiber and polyamide 6, the specimen printed with continuous fiber implanted at the part exhibited reduced impact resistance. At 75% infill density, the impact specimen printed with coextruded fiber showed the highest impact resistance with a 367.02% greater magnitude than the continuous fiber specimen with the same infill density.

This work presents a novel approach to analyze the low energy impact characteristics and three-dimensional printing of carbon fiber reinforced thermoplastic and carbon fiber reinforced thermoset and thermoplastic composite material.

This paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.

The experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.

The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.

3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.