Digital Processing Research Articles

Accuracy and clinical fit of milled versus rapid prototyped orthognathic surgical splints

Three-dimensional (3D) printing has become an integral part of orthognathic surgery. However, there is a lack of studies evaluating accuracy of orthognathic surgical splints fabricated from subtractive milling versus additive 3D printing. The primary aim of this in-vitro study was to compare the differences in trueness between milled and 3D-printed splints, while the secondary aim was to compare the differences in clinical fit of these splints. A sample of eight patients was selected, and STL files of the final orthognathic surgical splint were used to fabricate three splints for each of the eight cases. The first splint was fabricated by subtractive milling (SM), whereas the second and third splints were 3D printed with Digital Light Processing (DLP) and Laser Stereolithography (SLA), respectively. Paired superimposition of scans was performed using a reference model. The clinical fit of the splints to the printed models was also assessed. The mean root mean square (RMS) deviations for the SM, SLA, and DLP were 0.11 ± 0.02, 0.16 ± 0.02 and 0.14 ± 0.02 respectively. The post-hoc analysis showed that the SM splints had the highest accuracy (p < 0.01). However, DLP splints showed the best clinical fit, followed by SM and SLA. In conclusion, splints fabricated by SM were more accurate than those fabricated by 3D printing, although this difference may not be clinically significant. The site, rather than the magnitude of the errors, may have a greater effect on the clinical usability of splints. In general, SM and DLP splints demonstrated a good clinical fit and were suitable for the fabrication of surgical splints.

The role of public-sector-led inclusive instant payment systems for growing the digital payments industry in Africa

Digital payments are the most popular financial service in Africa, having been adopted by half of all adults. Nevertheless, there remain significant barriers to access in Africa’s digital payment sector, stymieing growth. Cost and distance to a branch or agent are chief among them, exacerbated by the fact that many economies in Africa have small financial services markets, with payment sectors dominated by private sector financial service and payment system providers that have invested in proprietary, closed-loop digital payments processing infrastructure. These closed-loop systems can be inefficient and therefore expensive to operate, leaving payment services out of reach for millions of end users. With the goal of calling out fruitful approaches for expanding and maturing Africa’s payments market, this paper highlights lessons from four public or public–private instant payment systems (IPS) initiatives that aimed to build affordable and inclusive digital public infrastructure to enable broader access and therefore growth in Africa’s digital payments sector. Using case studies from Ghana, Malawi, Zambia and the Central African Economic and Monetary Community, this paper highlights lessons from these initiatives, including the need for enabling regulations, the need to craft a compelling business case for incumbent payment service providers, central bank involvement to ensure a focus on inclusive access, and customer-centricity to ensure the IPS can process high-demand payment types through the channels that end users prefer.

3D printed multi-coupled bioinspired skin-electronic interfaces with enhanced adhesion for monitoring and treatment

Skin-electronic interfaces have broad applications in fields such as diagnostics, therapy, health monitoring, and smart wearables. However, they face various challenges in practical use. For instance, in wet environments, the cohesion of the material may be compromised, and under dynamic conditions, maintaining conformal adhesion becomes difficult, leading to reduced sensitivity and fidelity of electrical signal transmission. The key scientific issue lies in forming a stable and tight mechanical-electronic coupling at the tissue-electronic interface. Here, inspired by octopus sucker structures and snail mucus, we propose a strategy for hydrogel skin-electronic interfaces based on multi-coupled bioinspired adhesion and introduce an ultrasound (US)-mediated interfacial toughness enhancement mechanism. Ultimately, using digital light processing micro-nano additive manufacturing technology (DLP 3D), we have developed a multifunctional, diagnostic-therapeutic integrated patch (PAMS). This patch exhibits moderate water swelling properties, a maximum deformation of up to 460%, high sensitivity (GF = 4.73), and tough and controllable bioadhesion (shear strength increased by 109.29%). Apart from outstanding mechanical and electronic properties, the patch also demonstrates good biocompatibility, anti-bacterial properties, photothermal properties, and resistance to freezing at −20 °C. Experimental results show that this skin-electronic interface can sensitively monitor temperature, motion, and electrocardiogram signals. Utilizing a rat frostbite model, we have demonstrated that this skin-electronic interface can effectively accelerate the wound healing process as a wound patch. This research offers a promising strategy for improving the performance of bioelectronic devices, sensor-based educational reforms and personalized diagnostics and therapeutics in the future. Statement of significanceEstablishing stable and tight mechanical-electronic coupling at the tissue-electronic interface is essential for the diverse applications of bioelectronic devices. This study aims to develop a multifunctional, diagnostic-therapeutic integrated hydrogel skin-electronic interface patch with enhanced interfacial toughness. The patch is based on a multi-coupled bioinspired adhesive-enhanced mechanism, allowing for personalized 3D printing customization. It can be used as a high-performance diagnostic-therapeutic sensor and effectively promote frostbite wound healing. We anticipate that this research will provide new insights for constructing the next generation of multifunctional integrated high-performance bioelectronic interfaces.

Метод обработки сигналов токов статора асинхронного двигателя для диагностики обрыва стержня ротора

The study is relevant due to high requirements to the reliability of the operation of auxiliary mechanisms of thermal power plants (TPP). The main source of mechanical power of auxiliary mechanisms of TPPs is high-voltage induction motor with squirrel cage rotor (IMSC). One of the most difficult causes of emergency shutdown to detect early is a broken rotor bar. The breakage of IMSC rotor bar is of latent character. It can be for a long time without considerable influence on the machine operation mode. Nevertheless, the fact of the breakage can be considered as an emergency condition of IMSC, and the consequences of leaving the groove of the magnetic core are irreversible. Scientifically, early diagnostics of IMSC rotor bars breakage of TPP auxiliary needs is both a difficult and extremely urgent task. The existing methods of diagnostics of such damage are based on measurement and analysis of vibration and acoustic physical quantities. The most common electrical parameter, used as a source of information, as a rule, is current consumption. And most of methods are based on Fourier analysis. The purpose of the study is to develop a mathematical algorithm to process digitized curves of stator phase currents to diagnose high-voltage IMSC. The theory of electric machines, the method of analytical approximation of experimental data, the method of regression analysis have been used to solve the problem. Experimental data is obtained, and testing of the algorithm is conducted on an experimental installation with a 0,4 kV induction motor with a modified design of the active part of the rotor developed for physical simulation of IMSC broken rotor bar. This paper proposes a new method of digital processing of stator current signals based on the regression analysis method for diagnostics of IMSC rotor winding. The authors have developed an algorithm for mathematical processing of digitized stator current curves based on regression analysis in the cosine-sine basis to identify the diagnostic sign of failure of one rotor bar of IMSC rotor. The authors have determined the number of harmonic components that best describe the initial signal from the point of view of the balance between saving computational resources and accuracy of approximation. A mathematical algorithm to detect the rotor bar breakage of an induction motor based on regression analysis of stator currents has been obtained and tested. It allows to detect rotor circuit damage with 97 % accuracy. At the same time the diagnostic sign changes its level when one bar breaks by 5 %. It is recommended to adapt the algorithm to the programming languages of microprocessor relay protection units.

Synthesis and Solvent Free DLP 3D Printing of Degradable Poly(Allyl Glycidyl Ether Succinate).

Digital light processing (DLP) printing forms solid constructs from fluidic resins by photochemically crosslinking polymeric resins with reactive functional groups. DLP is used widely due to its efficient, high-resolution printing, but its use and translational potential has been limited in some applications as state-of-the-art resins experience unpredictable and anisotropic part shrinkage due to the use of solvent needed to reduce resin viscosity and layer dependent crosslinking. Herein, poly(allyl glycidyl ether succinate) (PAGES), a low viscosity, degradable polyester, was synthesized by ring opening copolymerization and used in combination with degradable thiol crosslinkers to afford a solvent free resin that can be utilized in DLP printing. Varying resin formulations of PAGES polymer are shown to decrease part shrinkage from 14% to 0.3%. Photochemically printed parts fabricated from PAGES possess tensile moduli between 0.43 and 6.18 MPa and degradation profiles are shown to vary between 12 and 40 days under accelerated conditions based on degree of polymerization and crosslink ratio.

Microstructural evolution, enhanced mechanical properties, and complex shapes design in high solid content alumina ceramics through additive manufacturing

Manufacturing industrially profitable high solid-loading ceramic materials with finely designed complex shapes is an exciting yet challenging in sustainable additive manufacturing. Here, we propose a resin strategy to achieve a high solid-loading content of 62 vol% alumina slurry for 3D printing high-quality complex shapes using digital light processing (DLP) technology. Remarkably, both high solid-loading content and tuned sintering temperature emerge as key factors in ensuring optimal print quality and mechanical performance. With this strategy, the ceramic slurry containing 62 vol% exhibits lower shear rate and improved layer adhesion. The burnout treatment was designed based on the DSC-TGA results, followed by sintering in an air atmosphere. Comprehensive physical measurements were achieved, alongside precise structural characterization at multilength scales in situ. In depth exploration of post-sintering analysis revealed no changes in phase composition or chemical bonding. The optimum overall performance of sintered specimen exhibited shrinkage of 8.3 %, 8.8 %, and 11.5 % in the X, Y, and Z directions, with a bulk density of 3.76 g/cm3. Mechanical testing demonstrated superior flexural strength of 247.23 MPa, nanoindentation hardness of 30.9 GPa, and modulus of 428.35 GPa. This high-precision printing strategy may significantly contribute to understanding the structure-property relationship of DLP-3D printed Al2O3 ceramic and opening avenue for the applications of high-strength parts in demanding environments.

Zirconia specimens printed by vat photopolymerization: Mechanical properties, fatigue properties, and fractography analysis.

The mechanical and fatigue properties of zirconia specimens printed by vat photopolymerization (VPP) were evaluated and compared with those of zirconia specimens milled by computer numerical control (CNC). Bar-shaped specimens were printed by stereolithography (SL) and digital light processing (DLP). CNC-milled specimens were used as control samples. The fracture toughness, hardness, and flexural strength properties of the zirconia specimens were evaluated via single edge V-notch beam tests, Vickers hardness tests, and 3-point bending tests. Dynamic fatigue tests were carried out in distilled water using a step-stress test. After static bending and dynamic step-stress testing, fractography analysis was performed. Statistical analysis was carried out to compare the fracture toughness, hardness, flexural strength, and fatigue cycle results of each group (α = 0.05). The fracture toughness values did not significantly differ among the groups (p>0.05). The flexural strength was 894.10MPa for SL, 831.46MPa for DLP, and 1140.39MPa for CNC. The flexural strength of CNC was greater than that of SL and DLP (p<0.01). The mean fatigue cycles were 23498.07 for SL, 19858.60 for DLP, and 31566.80 for CNC. The mean fatigue failure strength was 643.13MPa for SL, 530.63MPa for DLP, and 903.75MPa for CNC. The fatigue failure strength of CNC was greater than that of SL and DLP (p<0.05). Fractography analysis revealed material defects at the fracture origin for each group. A partially fused structure of the incompletely debonded resin could be observed in SL, and a porous region of incompletely sintered zirconia grains could be observed in CNC. The fracture toughness and hardness of zirconia printed by VPP are comparable to those of zirconia milled by CNC. However, zirconia milled by CNC has superior static flexural strength and dynamic fatigue resistance. Further studies are needed to explore the clinical applications of VPP-printed zirconia.

Photocurable Foam for Three-Dimensional-Printed Porous Structures.

In this research, a foam three-dimensional (3D) printing method using digital light processing (DLP) technology was developed to fabricate 3D-printed porous structures. To address the challenges in preparing DLP precursor foam fluid, we designed a specialized foaming device. This device enables the precursor solution to be blended with air, resulting in a stable foam precursor with an adjustable air/liquid fraction and suitable fluidity, crucially enhancing the gas-liquid contact time for the printing process. By manipulation of fluid flow rates, cycle counts, and gas/liquid ratios, one can easily prepare uniform foams with precise control over the pore size and porosity. To avoid significant volume reduction during ultraviolet (UV) curing, nanoparticle fillers were introduced into the network to prevent collapse of the foam structure. Furthermore, the inclusion of an UV absorber enhanced the quality of the printing process by addressing the limitations associated with particle scattering and reflection. The DLP process can readily fabricate intricate structures, featuring a planar resolution below 30 μm and a printing accuracy of less than 1%. Several examples were also demonstrated to highlight the advantages of this technology and its ability to directly print custom foam structures, thereby saving time and material resources.

Research Status and Development Trend of Wire Arc Additive Manufacturing Technology for Aluminum Alloys

It is difficult for traditional aluminum alloy manufacturing technology to meet the requirements of large-scale and high-precision complex shape structural parts. Wire Arc additive manufacturing technology (WAAM) is an innovative production method that presents the unique advantages of high material utilization, a large degree of design freedom, fast prototyping speed, and low cast. As a result, WAAM is suitable for near-net forming of large-scale complex industrial production and has a wide range of applications in aerospace, automobile manufacturing, and marine engineering fields. In order to serve as a reference for the further development of WAAM technology, this paper provides an overview of the current developments in WAAM both from the digital control system and processing parameters in summary of the recent research progress. This work firstly summarized the principle of simulation layering and path planning and discussed the influence of relative technological parameters, such as current, wire feeding speed, welding speed, shielding gas, and so on. It can be seen that both the welding current and wire feeding speed are directly proportional to the heat input while the travel speed is inversely proportional to the heat input. This process regulation is an important means to improve the quality of deposited parts. This paper then summarized various methods including heat input, alloy composition, and heat treatment. The results showed that in the process of WAAM, it is necessary to control the appropriate heat input to achieve minimum heat accumulation and improve the performance of the deposited parts. To obtain higher mechanical properties (tensile strength has been increased by 28%–45%), aluminum matrix composites by WAAM have proved to be an effective method. The corresponding proper heat treatment can also increase the tensile strength of WAAM Al alloy by 104.3%. In addition, mechanical properties are always assessed to evaluate the quality of deposited parts. The mechanical properties including the tensile strength, yield strength, and hardness of the deposited parts under different processing conditions have been summarized to provide a reference for the quality evaluation of the deposition. Examples of industrial products fabricated by WAAM are also introduced. Finally, the application status of WAAM aluminum alloy is summarized and the corresponding future research direction is prospected.

Dual-Mode Embedded Impulse-Radio Ultra-Wideband Radar System for Biomedical Applications.

This paper presents a real-time and non-contact dual-mode embedded impulse-radio (IR) ultra-wideband (UWB) radar system designed for microwave imaging and vital sign applications. The system is fully customized and composed of three main components, an RF front-end transmission block, an analog signal processing (ASP) block, and a digital processing block, which are integrated in an embedded system. The ASP block enables dual-path receiving for image construction and vital sign detection, while the digital part deals with the inverse scattering and direct current (DC) offset issues. The self-calibration technique is also incorporated into the algorithm to adjust the DC level of each antenna for DC offset compensation. The experimental results demonstrate that the IR-UWB radar, based on the proposed algorithm, successfully detected the 2D image profile of the object as confirmed by numerical derivation. In addition, the radar can wirelessly monitor vital sign behavior such as respiration and heartbeat information.

Size shrinkage and compressive behaviour of Al2O3 honeycomb structures fabricated via Digital Light Processing technology

Al2O3 ceramic have excellent chemical inertness, superior heat resistance, and excellent corrosion resistance, so they are considered ideal materials for manufacturing high-performance components such as thermal end devices in aerospace, heat dissipation panels in electronic communication, and exhaust filters in the automotive industry. Digital Light Processing (DLP) is a useful technology to fabricate complex Al2O3 ceramic components. However, the size shrinkage effect of DLP-fabricated components need to be investigated deeply before its further application. In this work, we utilized DLP technology to fabricate Al2O3 ceramic honeycomb structures and systematically investigated their microstructure, size shrinkage, and mechanical properties. Our findings revealed that the Al2O3 ceramic grains exhibit flake-like structures with an average diameter of 1–2 μm, and the ceramic particles are tightly bonded. The shrinkage rates in the X and Y axes were 7.54 % and 7.42 %, respectively, while the Z-axis exhibited a more significant shrinkage of 13.04 %. This anisotropic shrinkage in different direction was attributed to accumulation of the interlayer bonding under gravity action during debinding and sintering process. The compressive stress-strain curve of the honeycomb structures demonstrated a typical brittle compressive behavior, including elastic, stress plateau, and brittle fracture stages. The honeycomb structures exhibited an elastic modulus of 54.49 ± 1.22 MPa and a compressive strength of 10.22 ± 0.42 MPa. The fracture analysis indicated that the fractures region of honeycomb structures initiated at the corners of the honeycomb structures, indicating their easy-broken compared to the walls. These findings provide valuable guidance for the high-precision and high-performance manufacturing of complex Al2O3 ceramic components.

A Highly Sensitive 3D-Printed Flexible Sensor for Sensing Small Pressures in Deep-Sea High-Pressure Environment.

The origin of life on Earth is believed to be from the ocean, which offers abundant resources in its depths. However, deep-sea operations are limited due to the lack of underwater robots and rigid grippers with sensitive force sensors. Therefore, it is crucial for deep-sea pressure sensors to be integrated with mechanical hands for manipulation. Here, a flexible stress sensor is presented that can function effectively under high water pressure in the deep ocean. Inspired by biological structures found in the abyssal zone, our sensor is designed with an internal and external pressure balance structure (hollow interlocking spherical structure). The digital light processing (DLP) three-dimensional (3D) printing technology is utilized to construct this complex structure after obtaining optimized structural parameters using finite element simulation. The sensor exhibits linear sensitivity of 0.114 kPa-1 within the range of 0-15 kPa and has an extremely short response time of 32 ms, good dynamic-static load response capability, and excellent resistance cycling stability. It shows high sensitivity of 1.74 kPa-1 even under 30 MPa static water pressures and the theoretical working pressure can exceed 110 MPa, which provides a new solution for deep-sea robots.

Preparing the Electrical Signal Data of the Heart by Performing Segmentation Based on the Neural Network U-Net

Research on the automated extraction of essential data from an electrocardiography (ECG) recording has been a significant topic for a long time. The main focus of digital processing processes is to measure fiducial points that determine the beginning and end of the P, QRS, and T waves based on their waveform properties. The presence of unavoidable noise during ECG data collection and inherent physiological differences among individuals make it challenging to accurately identify these reference points, resulting in suboptimal performance. This is done through several primary stages that rely on the idea of preliminary processing of the ECG electrical signal through a set of steps (preparing raw data and converting them into files that are read and then processed by removing empty data and unifying the width of the signal at a length of 250 in order to remove noise accurately, and then performing the process of identifying the QRS in the first place and P-T implicitly, and then the task stage is determining the required peak and making a cut based on it. The U-Net pre-trained model is used for deep learning. It takes an ECG signal with a customisable sampling rate as input and generates a list of the beginning and ending points of P and T waves, as well as QRS complexes, as output. The distinguishing features of our segmentation method are its high speed, minimal parameter requirements, and strong generalization capabilities, which are used to create data that can be used in diagnosing diseases or biometric systems.

(Alumina and graphene)/ PLA nanocomposites manufactured by digital light processing 3D printer

In this study, the variant effects of alumina and graphene reinforcing nanoparticle percentages on the tensile and wear properties of the nanocomposites, produced using digital light processing (DLP), were determined by employing SEM images of fracture and worn surfaces to introduce a hybrid (alumina + graphene)/PLA nanocomposite with desirable wear and mechanical properties. With the addition of alumina up to 2 wt%, the tensile strength was initially decreased exhibiting a rapid brittle fracture over the flat fracture surface. However, with a continued increase in the amount of reinforcing particles, the tensile strength eventually increased by 16% compared to the pure resin sample at 8 wt% of alumina while changing the fracture mode with plenty of cleavages accompanied by crack deflections. The specific wear rate in the 8 wt% alumina sample decreased by almost 62% compared to the pure resin sample. Graphene caused a significant drop in tensile strength due to the generation of cavities and porosities around the graphene agglomerates, but it greatly improved the wear properties, with the specific wear rate decreasing by almost 77% at just 1 wt%. Then hybrid nanocomposites of alumina and graphene reinforcements were produced with the aim of optimal tensile and wear properties. The 0.5% graphene +3.5% alumina improved the wear resistance and provided moderate tensile properties; however, the 1% graphene +3% alumina could not increase the wear resistance due to the weakening of graphene plates sticking to the polymer substrate.

Fabrication of 3D printed mutable drug delivery devices: a comparative study of volumetric and digital light processing printing.

Mutable devices and dosage forms have the capacity to dynamically transform dimensionally, morphologically and mechanically upon exposure to non-mechanical external triggers. By leveraging these controllable transformations, these systems can be used as minimally invasive alternatives to implants and residence devices, foregoing the need for complex surgeries or endoscopies. 4D printing, the fabrication of 3D-printed structures that evolve their shape, properties, or functionality in response to stimuli over time, allows the production of such devices. This study explores the potential of volumetric printing, a novel vat photopolymerisation technology capable of ultra-rapid printing speeds, by comparing its performance against established digital light processing (DLP) printing in fabricating hydrogel-based drug-eluting devices. Six hydrogel formulations consisting of 2-(acryloyloxy)ethyl]trimethylammonium chloride solution, lithium phenyl-2,4,6-trimethylbenzoylphosphinate, varying molecular weights of the crosslinking monomer, poly(ethylene glycol) diacrylate, and paracetamol as a model drug were prepared for both vat photopolymerisation technologies. Comprehensive studies were conducted to investigate the swelling and water sorption profiles, drug release kinetics, and physicochemical properties of each formulation. Expandable drug-eluting 4D devices were successfully fabricated within 7.5s using volumetric printing and were shown to display equivalent drug release kinetics to prints created using DLP printing, demonstrating drug release, swelling, and water sorption properties equivalent to or better than those of DLP-printed devices. The reported findings shed light on the advantages and limitations of each technology for creating these dynamic drug delivery systems and provides a direct comparison between the two technologies, while highlighting the promising potential of volumetric printing and further expanding the growing repertoire of pharmaceutical printing.

An integrated coupled oscillator network to solve optimization problems

Solving combinatorial optimization problems is essential in scientific, technological, and engineering applications, but can be very time and energy-consuming using classical algorithms executed on digital processors. Oscillator-based Ising machines offer a promising alternative by exploiting the analog coupling between electrical oscillators to solve such optimization problems more efficiently. Here we present the design and the capabilities of our scalable approach to solve Ising and quadratic unconstrained binary optimization problems. This approach includes routable oscillator connections to simplify the time-consuming embedding of the problem into the oscillator network. Our manufactured silicon chip, featuring 1440 oscillators implemented in a 28 nm technology, demonstrates the ability to solve optimization problems in 950 ns while consuming typically 319 μW per node. A frequency, phase, and delay calibration ensures robustness against manufacturing variations. The system is evaluated with multiple sets of benchmark problems to analyze the sensitivity for parameters such as the coupling strength or frequency.

Evaluation of zirconia ceramics fabricated through DLP 3d printing process for dental applications

Zirconia ceramics are versatile materials with remarkable properties such as a high thermal resistance, high fracture strength, and low thermal conductivity. They are chemically inert and highly wear- and corrosion-resistant, making them ideal for a wide range of applications in the aerospace, automotive, and biomedical fields. In dentistry, zirconia ceramics are used for veneers, crowns, bridges, and implants because of their biocompatibility. Despite the various benefits of zirconia ceramics, they are difficult to process because of their high hardness and brittleness. Additive manufacturing (AM) has proven to be a viable alternative to conventional fabrication processes, particularly for the processing of difficult-to-cut materials. AM of ceramics has gained significant attention in recent years because of its flexibility and ability to produce customized geometries rapidly and economically. In this study, the digital light processing (DLP) technique was employed to 3D print yttria-stabilized zirconia. The fabricated zirconia was evaluated and characterized for use in dental applications. Thermogravimetric analysis (TGA) and differential thermogravimetry (DTG) were performed on the green body to assess the decomposition of the additives in the slurry and determine the debinding temperatures. The as-built parts were subjected to debinding and sintering to obtain fully dense zirconia parts. The parts tended to shrink after sintering; therefore, the shrinkage ratios were evaluated and found to be 1.2817, 1.2900, and 1.3388 in the x-, y-, and z-directions, respectively. The average density after sintering was 6.031 g/cc. The flexural strength determined using four-point bending tests was 451.876 MPa, and the tensile and compressive strengths were 143 MPa and 298.4 MPa, respectively.

3D-printed zirconia orthodontic brackets: Effect of printing method on dimensional accuracy.

This study investigated the effect of additive manufacturing (AM) methods on the slot height dimensions and accuracy of 3D-printed orthodontic brackets. A 3D model of a standard Mclaughlin Bennett Trevisi bracket was used as a reference to print the ceramic bracket in a 90° orientation using two representative AM methods: digital light processing (DLP) and material jetting (MJ). The dimensional accuracy and slot heights were determined using a scanning electron microscope and an optical scanner. Also, all specimens were analysed using the Geomagic Control X 3D inspection software. The root mean square (RMS) values were used for trueness and precision assessment. Statistical analyses were performed using an independent sample t-test. Slot height dimensions, trueness RMS, and precision RMS were statistically affected by different AM methods (p < .01). There was a significant difference between the different printing methods, with DLP meeting the tolerance requirements (mean slot height = 0.557 ± 0.018 mm) and MJ being slightly below them (mean slot height = 0.544 ± 0.021 mm). However, MJ significantly outperformed DLP in terms of accuracy. Among the two printing methods, MJ was associated with higher trueness (RMS = 0.025 ± 0.004 mm) and precision (RMS = 0.038 ± 0.005 mm). Both tested AM methods yielded clinically acceptable outcomes, with the RMS range set to ±100 μm and the slot height tolerance established at 0.549-0.569 mm. The MJ technology achieved the highest accuracy.

Visible-Light-Induced Silk Fibroin Hydrogels with Carbon Quantum Dots as Initiators for 3D Bioprinting Applications.

Digital light processing (DLP) 3D bioprinting technology has attracted increasing attention in tissue engineering in recent years. However, it still faces significant technical and operational challenges such as cell carcinogenesis caused by prolonged exposure to ultraviolet light and the presence of heavy metal ions in complex photoinitiator systems. In this study, a novel strategy is designed to introduce carbon quantum dots into visible-light-induced silk fibroin bioink as initiators (CDs/SilMA) applied for DLP 3D bioprinting technology. The incorporation of carbon quantum dots facilitates the formation of precise hydrogel structures at 415 nm visible wavelength, enabling the creation of brain, bronchus, spine, and ear models. Replacing heavy metal photoinitiators with carbon quantum dots imparts fluorescence properties to the bioink and enhances its mechanical properties. Meanwhile, the fibroin protein-based hydrogel exhibits favorable properties, such as drug loading, slow release, degradability, and biocompatibility. This is the first study to propose the application of carbon quantum dots in silk fibroin-based bioink. Moreover, the resulting product demonstrates excellent compatibility with the DLP printing process, making it promising for practical applications in various tissue engineering scenarios with specific requirements.

Random memristor-based dynamic graph CNN for efficient point cloud learning at the edge

The broad integration of 3D sensors into devices like smartphones and AR/VR headsets has led to a surge in 3D data, with point clouds becoming a mainstream representation method. Efficient real-time learning of point cloud data on edge devices is crucial for applications such as autonomous vehicles and embodied AI. Traditional machine learning models on digital processors face limitations, with software challenges like high training complexity, and hardware challenges such as large time and energy overheads due to von Neumann bottleneck. To address this, we propose a software-hardware co-designed random memristor-based dynamic graph CNN (RDGCNN). Software-wise, we transform point cloud into graph, and propose random EdgeConv for efficient hierarchical and topological features extraction. Hardware-wise, leveraging memristor’s intrinsic stochasticity and in-memory computing capability, we achieve significant reductions in training complexity and energy consumption. RDGCNN demonstrates high accuracy and efficiency across various point cloud tasks, paving the way for future edge 3D vision.