Printing Time Research Articles

Maximizing performance and efficiency in 3D printing of polylactic acid biomaterials: Unveiling of microstructural morphology, and implications of process parameters and modeling of the mechanical strength, surface roughness, print time, and print energy for fused filament fabricated (FFF) bioparts

Medical stents, artificial teeth, and grafts are just some of the many applications for additive manufacturing techniques like bio-degradable polylactic acid 3D printing. However, there are drawbacks associated with fused filament fabrication-fabricated objects, including poor surface quality, insufficient mechanical strength, and a lengthy construction time for even a relatively small object. Thus, this study aims to identify the finest polylactic acid 3D printing parameters to maximize print quality while minimizing energy use, print time, flexural and tensile strengths, average surface roughness, and print time, respectively. Specifically, the infill density, printing speed, and layer thickness are all variables that were selected. A full-central-composite design generated 20 samples to test the prediction models' experimental procedures. Validation trial tests were used to show that the experimental findings agreed with the predictions, and analysis of variance was used to verify the importance of the performance characteristics (ANOVA). At layer thickness = 0.26 mm, infill density = 84 %, and print speed = 68.87 mm/s, the following optimized values were measured for PLA: flexural strength = 70.1 MPa, tensile strength = 39.2 MPa, minimum surface roughness = 7.8 μm, print time = 47 min, and print energy = 0.18 kwh. Firms and clinicians may benefit from utilizing the developed, model to better predict the required surface characteristic for various aspects afore trials.

Machine learning‐enabled optimization of melt electro‐writing three‐dimensional printing

AbstractMelt electrowriting (MEW) is a solvent‐free (i.e., no volatile chemicals), a high‐resolution three‐dimensional (3D) printing method that enables the fabrication of semi‐flexible structures with rigid polymers. Despite its advantages, the MEW process is sensitive to changes in printing parameters (e.g., voltage, printing pressure, and temperature), which can cause fluid column breakage, jet lag, and/or fiber pulsing, ultimately deteriorating the resolution and printing quality. In spite of the commonly used error‐and‐trial method to determine the most suitable parameters, here, we present a machine learning (ML)‐enabled image analysis‐based method for determining the optimum MEW printing parameters through an easy‐to‐use graphical user interface (GUI). We trained five different ML algorithms using 168 MEW 3D print samples, among which the Gaussian process regression ML model yielded 93% accuracy of the variability in the dependent variable, 0.12329 on root mean square error for the validation set and 0.015201 mean square error in predicting line thickness. Integration of ML with a control feedback loop and MEW can reduce the error‐and‐trial steps prior to the 3D printing process, decreasing the printing time (i.e., increasing the overall throughput of MEW) and material waste (i.e., improving the cost‐effectiveness of MEW). Moreover, embedding a trained ML model with the feedback control system in a GUI facilitates a more straightforward use of ML‐based optimization techniques in the industrial section (i.e., for users with no ML skills).

TPP-Based Microfluidic Chip Design and Fabrication Method for Optimized Nerve Cells Directed Growth

Microfluidic chips offer high customizability and excellent biocompatibility, holding important promise for the precise control of biological growth at the microscale. However, the microfluidic chips employed in the studies of regulating cell growth are typically fabricated through 2D photolithography. This approach partially restricts the diversity of cell growth platform designs and manufacturing efficiency. This paper presents a method for designing and manufacturing neural cell culture microfluidic chips (NCMC) using two-photon polymerization (TPP), where the discrete and directional cell growth is optimized through studying the associated geometric parameters of on-chip microchannels. This study involves simulations and discussions regarding the effects of different hatching distances on the mold surface topography and printing time in the Describe print preview module, which determines the appropriate printing accuracy corresponding to the desired mold structure. With the assistance of the 3D maskless lithography system, micron-level rapid printing of target molds with different dimensions were achieved. For NCMC with different geometric parameters, COMSOL software was used to simulate the local flow velocity and shear stress characteristics within the microchannels. SH-SY5Y cells were selected for directional differentiation experiments on NCMC with different geometric parameters. The results demonstrate that the TPP-based manufacturing method efficiently constructs neural microfluidic chips with high precision, optimizing the discrete and directional cell growth. We anticipate that our method for designing and manufacturing NCMC will hold great promise in construction and application of microscale 3D drug models.

Impact of support structure on surface roughness and mechanical properties of PLA 3D printed parts by FDM

This study investigates the effect of support infill angle and its density, transverse at 90°, inclined at 45°, axial direction at 0° and crossed filament by (0°/90°), (45°/-45°) and (0°/45°) on surface quality and mechanical properties using three different strategies. The surface roughness and flexural properties of the specimens are analysed and compared as well as the material waste and printing time. According to results of this study, the variations in the support infill angle resulted in diverse flexural strength and surface quality.

Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments

Abstract Additive manufacturing (AM) has transformed job shop production and catalysed the growth of Makerspaces, FabLabs, Hackspaces, and Repair Cafés. AM has enabled the handling and manufacturing of a wide variety of components, and its accessibility has enabled more individuals to make. While smaller than their production-scale counterparts, the objectives of minimizing technician overhead, capital expenditure, and job response time remain the same. The typical First-Come First-Serve (FCFS) operating model, while functional, is not necessarily the most efficient and makes responding to a-typical or urgent demand profiles difficult. This article reports a study that investigated how AM machines configured with Minimally Intelligent agents can support production in these environments. An agent-based model that simulated 5, 10, 15, and 20 AM machines operating a 9 am−5 pm pattern and experiencing a diverse non-repeating demand profile was developed. Machines were configured with minimal intelligence – FCFS, First-Response First-Serve (FRFS), Longest Print Time (LPT), Shortest Print Time (SPT), and Random Selection logics – that governed the selection of jobs from the job pool. A full factorial simulation totaling 15,629 configurations was run until convergence to a ranked list of production performance – min Job Time-in-System. Performance changed as much as 200%. Performant configurations featured a variety of logics, while the least performant were dominated by FCFS and LPT. All FCFS (a proxy for today’s operations) was one of the least performant configurations. The results provide an optimal set of logics and performance bands that can be used to justify capital expenditure and AM operations in Makerspaces.

Additive technologies use to create structures for technical fabric replacement

The production technology of one of the essential structural parts of rubber-textile conveyor belts, the textile carcass, has not changed much since the start of their use. Specific changes occurred only in the material used when various synthetic fibres gradually replaced cotton. However, with the development of additive technologies, the possibility of changing the production technology is coming to the fore, when industrial textiles produced by classic weaving will not be used to make the carcass but a structure built by 3D printing. Confirmation of this change would represent a revolutionary breakthrough in the technology for rubber-textile conveyor belt production. Based on these facts, the possibility of using continuous 3D printing technology was verified to print a structure that would replace the technical fabric used in the conveyor belt carcass. As part of the research, the Selective Laser Sintering (SLS), Fused Deposition Modeling (FDM), and Stereolithography (SLA) technologies were verified. Experimental specimens with the dimensions of 145 x 145 x 185 mm were produced in three different positions: at an angle (45°), horizontally, and vertically. The specimens were made of three types of filament: SLA (Elastic 50A), SLS (TPU 1301), and FDM (Flexfill 92A); for each of the three positions of the specimen, filament consumption and printing time were determined due to the use of support material. Created specimens were then assessed regarding their production possibility and achievement of the desired structure.

Исследование возможности использования методов аддитивной технологии при ремонте деталей сельскохозяйственной техники

Currently, maintaining agricultural machinery in a constant working condition is an urgent task. Studies carried out to study the state of the problem in the conditions of agricultural enterprises of the Republic of Kazakhstan have shown that the lack of centralized repair and restoration bases and production facilities has a negative impact on the quality organization of agricultural machinery. Because of this, it is impossible to carry out repair and restoration work in a timely and high-quality manner, as well as technical maintenance of agricultural machinery and equipment. To solve this problem, it is proposed to use АM technologies in the repair and manufacture of parts of agricultural machinery and machines. To this end, the existing methods of additive technology were investigated. An experimental study was conducted on the manufacture of a «shaft-gear» part using a Stratasys F170 3D printer in the laboratory of the Department of «Technological Machines and Equipment» of the Kazakh Agrotechnical Research University named after S. Seifullin. The results showed that the time of 3D printing directly depends not only on the size of the model, but also on the density and shape of the filling, as well as on the thickness of the printing layer. It was found that an increase in the thickness of the layer has a twofold effect on the 3D printing process - it has a positive effect on reducing the printing time, but it has a negative effect on the rigidity of the printed sample.

PISA printing from CTA functionalized polymer scaffolds

This study investigates DLP 3D printing using RAFT polymerization-induced self-assembly (RAFT PISA) printing, focusing on the impact of CTA (chain transfer agent) groups per scaffold on PISA printing times and...

Analysis of Energy Consumption for Hollow Structure of 3D Printed Part Reinforced by Polymer Resin

Manufacturing time and energy consumption are two important parameters to evaluate the manufacturing process. The main purpose of this study was to analyze the energy consumption and printing time of 3D printed part between hollow and without any hollow structure. The specimen models were designed according to ASTM D368-IV standards and printed using a 3D printer device. During the printing process, the wall thickness of specimen was varied into three different types, 0.4 mm; 0.8 mm and 0.12 mm. Furthermore, the cavity was filled by polymer resin as main material for strengthening the specimen. Time and energy consumption of manufacturing process was recorded. In addition, the strength of specimens was observed using universal tensile test machine. The results showed that the highest manufacturing speed was occurred on the specimen that had wall thickness of 0.4 mm. Besides that, its parameter had a lowest energy consumption than specimen that had wall thickness 0.12 mm. The wall thickness showed the significant parameter to reduce the energy consumption, printing speed and material usage..

An Optimized 3D Printer by Enhancing Material Properties and Surface Finish of PLA Through Modifications

This research investigates the optimization of 3D printer settings to enhance the mechanical properties of polylactic acid (PLA) printed parts. A fundamental aspect of this study involves the design and development of a 3D printer capable of accommodating varied printing parameters. The parameters under consideration include extruder temperature, layer height, print speed, and infill density. Through experimentation, the tensile strength and the bending strength of PLA material are systematically evaluated against these printing parameters. The results unveil significant correlations between printing parameters and mechanical properties. It is observed that increasing the extruder temperature up to 220°C increases the material strength, beyond which a decline is evident due to overextrusion and material degradation. Conversely, increasing the layer height leads to a reduction in strength, attributed to diminished layer adhesion and structural integrity. Moreover, decreasing print speed is found to enhance strength at the expense of increased print time, suggesting a trade‐off between efficiency and mechanical performance. Furthermore, the study demonstrates that augmenting infill density contributes to a notable increase in material strength, underscoring the importance of internal reinforcement in enhancing structural integrity. These findings provide valuable insights into the optimization of 3D printing processes for PLA materials, offering guidelines for achieving superior mechanical properties through precise calibration of printing parameters.

Research on Optimization of 3D Printing Model Based on Deep Learning

3D printing is a process of creating three-dimensional physical objects through a series of layering techniques. It is a relatively new technology, but it has already brought a lot of innovation and change to manufacturing and other fields. This paper introduces the basic mechanism of 3D printer and summarizes several advanced 3D printing model optimization methods based on deep learning and their application scenarios. The proposed approach involves training a deep neural network on a large dataset of 3D models and their corresponding printing parameters. The trained model is then used to detect printing parameters of different 3D models, such as lattice defect detection, laser temperature detection, and fault detection. This method improves printing quality and reduces printing time and material consumption. The experimental results show that these methods are superior to traditional methods and achieve a good balance between print quality and efficiency. The proposed method has great application potential in various fields such as manufacturing, prototyping, and art design.

Tensile response of additively manufactured carbon/nylon joints using optical fibers and digital image correlation

ABSTRACT Additive manufacturing (AM) has gained popularity due to reduced production times of low-cost and geometrically complex structures. Due to technological constraints such as print volume, time, and thermal stability of large AM parts, adhesive joints are commonly used in assembling smaller components in large-scale polymeric structures. This work investigates the feasibility of using embedded optical fibers to monitor strain distributions within AM composite (carbon fiber/nylon) single-lap shear (SLS) joints. Internal strain distributions within the adhesive and surface strain fields of the SLS joints with AM adherends under tensile loads were obtained using optical fibers and digital image correlation, respectively. Results indicate negligible influence of optical fibers on the failure strength and axial stiffness of 3D-printed SLS joints under quasi-static loading. Higher strains were observed at the bond edges within the adhesive (using optical fiber) and the surface (using DIC) of the joints due to the rotation of the bond under loading. All specimens failed in the adherends beyond the bonded region due to their low interlaminar strength.

Optimizing PEEK impact strength through multi-objective FDM 3D printing

Thermoplastic materials such as Polylactic acid, Acrylonitrile Butadiene Styrene, Polyethylene terephthalate glycol, Nylon, and Thermoplastic polyurethane are favoured in Fused deposition modeling 3D printing due to their cost-effectiveness and versatile properties. However, with the introduction of high grade thermoplastic material poses compatibility challenges with existing machines and processes, impeding widespread adoption in FDM 3D printing. Incorporating new materials into 3D printing requires adjustments to hardware, software, and settings, leading to potential expenses and time investments. Maintaining quality control and consistency becomes complex as each material demands specific parameters and processing conditions. This variability hinders achieving consistent part quality in 3D printing. Moreover, achieving optimal FDM parameters for high-grade polymers (HGPs) like Polyether ether ketone (PEEK) is a challenge due to the distinctive nature of the property, requiring specialized careful considerations during its optimization process. The considerable thermal gradient and heat distribution during printing can lead to residual stresses and deformations, significantly affecting the quality and, in particular, its impact strength. This article optimizes an industry grade 3D printing PEEK based on the limited number of process parameters, namely, build orientation, in-fill density and chamber temperature. Further, the research tries to derive a predictive model for Impact Strength (IS), which is an important consideration for the 3D printed object. In this article, along with the Impact Strength, Printing Time and Material Usage are also studied to find empirical evidence of association between these output variables or response variables. The result indicates that there is a positive significant correlation or association between them. When utilizing a specific parameter setup, the resulting IS of 86.5 kJ/m², a print time of 89 minutes, and a material usage of 3.26 grams are achieved. Notably, there is a measurable reduction of 9.18% in printing time and a 11.66% decrease in material usage when the print density is set to 100% to optimize impact strength. This optimization approach proves the use of composite desirability is a better approach where multiple objectives need to be achieved. The proposed regression model predicts the impact strength with coefficient of determination value more than 50%.

Base shape generation and optimization for multi-axis hybrid additive manufacturing

Hybrid additive manufacturing (HAM) processes combine the advantages of both additive and non-AM processing to achieve an improvement on quality, cost, and a good quality-cost balance. The non-additive manufacturing process is able to build the physical component of a computer-aided design model from zero or an existing relatively simple subvolume, called base shape in this paper. Hence, if the processing start point is an existing subvolume, how to determine an optimal base shape to save printing time, avoid manufacturing constraints and ensure component quality is an open question in the process planning. Nevertheless, this topic has rarely been investigated. Therefore, in this paper, we propose an optimization method using model skeleton-based decomposition and evolutionary computation. A set of generic evaluation criteria are defined for alternative evaluation. We also present two case studies in this paper for validating the proposed method and conclude that sequential HAM processes have a wide application potential.

Dr. Gyárfás Elemér státusgyűlési tag beszámoló jelentése egyházi és iskolai ügyekben Bukarestben folytatott tárgyalásainak eredményéről

In communist Romania, building a church was an almost impossible undertaking, and building permits were rarely granted to anyone who wanted to build a church. The state law that stipulated that a new church could only be built if an existing one ceased to exist provided the opportunity taken by the Diocese of Timisoara to build the new church in Orsova. The state authorities allowed the new church to be constructed in Orsova because of the number of believers (there were Hungarian, German and Czech believers living in the town at the time) and because the construction costs were entirely financed from abroad. The construction of the new Roman Catholic parish church and the priest’s residence in Orsova was one of the greatest achievements of the administration of Konrád Kernweisz, Ordinary of the Diocese of Timisoara – and perhaps of Communist Romania. The speech published here for the first time in print is not a sermon but was certainly delivered at the end of the consecration mass of the church in Orsova. The interesting detail about this text is that it is the first time that the symbolism of the church in Orsova was mentioned.

3D printing of self-healing personalized liver models for surgical training and preoperative planning

3D printing can produce intuitive, precise, and personalized anatomical models, providing invaluable support for precision medicine, particularly in areas like surgical training and preoperative planning. However, conventional 3D printed models are often significantly more rigid than human organs and cannot undergo repetitive resection, which severely restricts their clinical value. Here we report the stereolithographic 3D printing of personalized liver models based on physically crosslinked self-healing elastomers with liver-like softness. Benefiting from the short printing time, the highly individualized models can be fabricated immediately following enhanced CT examination. Leveraging the high-efficiency self-healing performance, these models support repetitive resection for optimal trace through a trial-and-error approach. At the preliminary explorative clinical trial (NCT06006338), a total of 5 participants are included for preoperative planning. The primary outcomes indicate that the negative surgery margins are achieved and the unforeseen injuries of vital vascular structures are avoided. The 3D printing of liver models can enhance the safety of hepatic surgery, demonstrating promising application value in clinical practice.

Impact of Fused Deposition Modeling Process Parameters and Heat Treatment on Mechanical Characteristics and Product Quality: A Review

Fused Deposition Modeling (FDM) is indeed one of the most commonly used methods of additive manufacturing, particularly for printing polymers and fiber-reinforced polymer composites. When compared to more conventional production meth ods like injection molding, the key benefits of FDM include producing components with intricate shapes, minimal material wastage, shorter production times, and lower costs because no tooling is needed. However, the strength and surface quality of the product produced using this technique are lower, that can be improved by selecting the optimized design variable and applying heat treatment depending on how the product will be used in an industry. This review paper provides an overview of the effect of different process parameters on mechanical properties, print time, and surface characteristics of the parts made of polymers and fiber-reinforced polymer composites in addition to the challenges encountered during the printing of composites. It also discusses the new material’s development, such as natural fiber-reinforced polymer composites, the impact of heat treatment, and the void formation influence on the mechanical properties, build time, dimensional accuracy, and surface finish of 3D-printed parts.

Multivariate evaluation of the printing process on 3D printing of rice protein

The combination of printing parameters appropriately improves the printability of 3D-printed foods. In this regard, the present study aims to evaluate the effect of 3D printing process parameters on protein food generation. Printability of a cylinder 3 cm in diameter and 1 cm in height using a protein mixture of rice water and xanthan gum with a ratio of 30:70:0.5 was evaluated in an extrusion printer with an XYZ system. A ½ fractional factorial design was used with three factors: nozzle diameter (1.2 – 2.2 mm), layer height (1.0 – 2.0 mm), and print speed (20 – 50 mm/s). Each combination of factor levels was performed in triplicate for 12 runs plus three central points. Print time (min), sample weight, change in diameter (%), change in height (%), change in volume (%), mass flow rate (mg/s), appreciation (qualitative variable), and textural and rheology characters were obtained as response variables. The linear effects of the factors and combination factors were evaluated by analysis of variance. Additionally, a principal component analysis was performed to visualize the similarity between the observations and the relationship between the variables. The results showed that the layer height and nozzle diameter affect the printing accuracy concerning surface quality, shape stability, resolution, and layer layout. The nozzle with a diameter of 1.7 mm combined with speeds between 35 and 50 mm/s allowed the effects of overextrusion to be overcome, generating a better flow of the material. Low scores in the printability variable were related to low-speed values (20 mm/s) and a high nozzle diameter (2.2 mm), which generated higher deformations in the printed protein cylinder. Additionally, some printing conditions affected the textural and rheological characteristics, which allowed inferring that the capacity of the protein mass to store and recover energy in compression processes is conditioned by the printing parameters.

Density-Based Topological Optimization of 3D-Printed Casts for Fracture Treatment with Freefem Software

Abstract 3D printed plastic casts can be used for healing bone fractures. The main requirements for these cases are: they should be light, require little printing time, have good mechanical properties, and ensure proper skin ventilation. We present a density-based topology optimization algorithm for obtaining optimal cast shapes that fulfil these requirements. The algorithm uses a linear stress model and simplified boundary conditions to model the contact problems. The cast shapes were optimized against the influence of several sharp corners. The parametric studies showed that the mass of optimized casts was reduced by 20 %–25 % in comparison with original industrial casts, and the printing time is reduced by 1.4–1.7 h for the largest cast. A major model drawback is the use of 3D numerical volume to model the density distribution. The density distribution should be homogenized across the cast layer. The overhang problem should also be addressed. We also suggest that the cast producers collect more experimental data on the cast breakages for a better calibration of the numerical model.

Applications of hybrid structure light three‐dimensional scanning in neurodegenerative disease autopsy brain banking

AbstractBackgroundPostmortem magnetic resonance imaging (MRI) and three‐dimensional (3D) printing has been demonstrated to be a feasible approach for the creation of tissue matrices to aid in postmortem brain dissection. We aimed to extend this approach using hybrid structure light technology (visible and infrared) 3D scanning to generate brain models as a digital resource and operational tool for human neurodegenerative disease brain banks.MethodA consecutive series of 56 brains accessioned into the Glenn Biggs Institute Brain Bank were imaged using an EinScan H handheld 3D scanner. Five brains were selected for generation of tissue matrices, with infratentorial structures removed using 3D object post‐processing software tools. Matrices were printed using a Raise3D Pro2 printer and 1.75‐mm polylactic acid premium filament.ResultEach scan was approximately an hour to complete, including capture and alignment of a lateral and a medial surface scan for each brain. Raw scan files (DAT and BRAP‐PRJ) averaged 5.47 GB and 3D object files were 12,991 KB for 3MF and 18,067 KB for OBJ file types. For cases with tissue matrices, average print time was 42 hours with an average filament consumption of 338 g. Brain cutting with the tissue matrices was performed in a standardized manner, with superb control for section thickness and planar orientation compared to brains cut without a matrix.Conclusion3D scanning of postmortem autopsied brains is a simple and useful strategy to generate models with utility in guided tissue sectioning and storage (practice), surface area volumetric analysis (research) and virtual reality/augmented reality neuroanatomy training (education) for Alzheimer’s disease and related dementias.