Additive Manufacturing Technology Research Articles

Real-time monitoring and control of the layer height in laser metal deposition with coaxial wire feeding using optical coherence tomography

Laser metal deposition (LMD) with coaxial wire feeding is an additive manufacturing technology in which a metal wire is fed into a laser-induced melt pool. The repeated deposition of weld beads allows three-dimensional geometries to be created that can be used for manufacturing, repair, and modification of metal components. However, the process is highly sensitive to disturbances because the fed wire must always be fully melted, and no self-regulating effects as in powder-based LMD exist. The layer height is particularly important for process stability, as even small deviations accumulate over many layers and, ultimately, lead to the termination of the process. Therefore, monitoring and closed-loop control of the layer height during the deposition process are crucial. Due to process emissions, an interruption of the process is usually necessary for the accurate optical measurement of the layer height, which negatively affects the overall productivity. In order to overcome this drawback, an in-axis optical coherence tomography (OCT) sensor was employed in this work, which enabled real-time measurements of the layer height. It was found that positioning the OCT measurement spot as close as possible to the center of the wire provided the highest signal quality. Based on the real-time height data, a closed-loop layer height control was implemented, applying the wire feed rate as the manipulated variable. The experimental results showed that the proposed system was able to compensate for significant disturbances, ensuring dimensional accuracy and process stability.

Examining the influence of sintering temperatures on the efficiency of 3D-printed natural zeolite for methylene blue dye adsorption

Additive manufacturing technology, with its potential for improved material efficiency, cost reduction, and capability to fabricate complex structures, is increasingly being leveraged in environmental engineering applications. This study harnesses this technology for fabricating natural zeolite adsorbents using an extrusion-based 3D printer, examining the influence of different sintering temperatures (300, 600, 900, and 1200 °C) on the adsorbents' properties. The study finds that samples sintered at 300 °C disintegrated in water, while those at 1200 °C underwent melting, changing their shape. Focus was then shifted to samples sintered at 600 °C (3D-Ze-600) and 900 °C (3D-Ze-900), with the former displaying a remarkable methylene blue (MB) adsorption efficiency of 82.5%, significantly higher than the latter's 20.1%. This disparity in adsorption performance is closely linked to the difference in porosity; the 600 °C samples exhibited a notably higher apparent porosity, which was instrumental in their enhanced adsorption capability. In contrast, the 900 °C samples, despite their lower porosity, showed diminished adsorption efficiency. XRD analysis further revealed that the superior performance of the 600 °C samples could be attributed to their preserved crystalline structure, which was altered in the 900 °C samples. These findings highlight the critical role of sintering temperature in determining the structural and functional properties of 3D-printed zeolites, demonstrating their potential as efficient adsorbents in wastewater treatment and offering valuable insights for optimizing the fabrication process for environmental applications.

Effect of microstructural anisotropy and heat treatment on the corrosion behaviour of additively manufactured Ti-6Al-2Sn-4Zr-6Mo alloy

The electrochemical behaviour of Ti-6Al-2Sn-4Zr-6Mo alloy produced by Powder Bed Fusion – Laser Based/Metals has been investigated to identify the corrosion mechanism characteristic of the material processed by additive manufacturing technology. A microstructural characterisation of the material in as-built and heat-treated conditions was carried out to identify the different phases and assess the degree of anisotropy of the additive manufactured material. After that, the corrosion behaviour was studied by potentiodynamic polarisation and electrochemical impedance spectroscopy. Results showed that a homogeneous and compact oxide layer characterises the as-built material due to the presence of a finely dispersed α’’ phase in the β grains. Conversely, in the heat-treated alloy, the corrosion attack proceeds preferentially on the large α phase domains, where the oxide layer is less protective. Despite the anisotropy in the microstructure of the as-built material, the electrochemical behaviour was revealed to be the same for the section parallel and perpendicular to the building direction, noting the less influential factor of grain orientation on corrosion mechanisms.

Repair of heat load damaged plasma–facing material using the wire-based laser metal deposition process

Due to its unique properties tungsten is a promising candidate as plasma-facing-material (PFM) in future nuclear fusion reactors. Tungsten features an exceptionally high melting point, high thermal conductivity, low tritium inventory and comparatively low erosion rate under plasma loading [1]. But given the extreme loads on the PFM during operation of a fusion reactor, the lifetime of plasma-facing components (PFC)s is limited. Currently, it is planned to replace damaged PFCs when they reach the end of their service life. However, the lifetime of PFCs could be increased by in situ repair using additive manufacturing technology (AM) in the form of direct-energy-deposition (DED). The wire–based laser metal deposition process (LMD-w) meets several necessary conditions for operation in the vessel and could be used for performing such in situ repairs.It was investigated if the LMD-w process is able to heal thermal induced surface cracks and roughening by remelting the substrate during deposition of tungsten. For this purpose, tungsten samples of 12 × 12 × 5mm3, which later served as substrate plates for the LMD-w experiments, were treated with combined steady-state and transient thermal loads in the electron beam facility JUDITH 2. These samples were brazed to a copper cooling structure and exposed to 105 thermal shocks of 0.5 ms duration and an intensity of Labs = 0.55 GW m−2 (FHF = 12 MW s0,5 m−2) at a base temperature of Tbase = 700 °C. This way, edge localized mode (ELM) like thermal load damage was induced on the tungsten samples. On these samples, different LMD-w and laser remelting process strategies were performed. Subsequently, these samples were analyzed, and it was examined that the healing of the pre-damaged substrate material was successful. In parallel, the laser remelting process was modeled in a thermal transient finite element method(FEM) simulation in order to gain an insight into the temperatures prevailing in the material during the process.

On the cyclic deformation behavior of wire-based directed energy deposited Fe-Ni Invar alloy

Fe-basis alloys with Ni concentrations of approximately 36 wt.-% are widely used for components with high requirements on mechanical properties and dimensional accuracy. Due to the remarkably low coefficient of thermal expansion, these so-called Invar alloys provide for dimensional accuracy over a wide temperature range. Generally, additive manufacturing technologies have already been qualified for processing of Invar alloys in terms of maintaining mechanical as well as functional properties. However, the mechanical behavior under cyclic loading still is not comprehensively understood. Therefore, the present study focuses on the fatigue properties of the Fe-Ni36 Invar alloy manufactured by wire-based directed energy deposition (DED) employing either an electric arc (DED-Arc) or a laser beam (DED-LB) as energy source. Here, a wire-based DED-LB Invar condition is comprehensively studied for the first time in open literature. For initial assessment, specimens were analyzed using optical and scanning electron microscopy as well as computed tomography and tensile testing. Here, the DED-LB material is characterized by a heterogeneous grain size distribution, less pronounced crystallographic texture as well as increased tensile strength and ductility compared to the DED-Arc counterparts. Despite increased strength and ductility, the DED-LB Invar consistently shows inferior fatigue properties. This behavior is discussed based on microstructural heterogeneity, cyclic deformation response, process-induced porosity and the contribution of plastic strain. Furthermore, investigations on the thermal expansion behavior show that the DED Invar retains its outstanding thermal properties, respectively.

Pore-based prediction of crack initiation life in very-high-cycle fatigue

The porosity of the material produced by additive manufacturing technology gives rise to a notable dispersion of the crack initiation life in the very-high-cycle fatigue regime. The crack initiation life in the very high cycle fatigue regime can be divided into the initial crack initiation life and early microcrack growth life. This paper proposed a model considering the effect of pore morphology and location to predict the initial crack initiation life. The average local stress in a grain near the pore is modified by considering the relationship between pore roundness, inclination, position, and stress concentration factor. The growth life of early microcrack is determined by integrating empirical formulas based on dislocation theory. Subsequently, the probability distribution of crack initiation life is obtained, which is in good agreement with the experimental results. The competition factor is proposed to quantitatively evaluate the tendency of crack initiation from the surface or the interior, taking into account the influence of local average stress and grain size. The predicted load corresponding to the shift in crack initiation position is in accordance with the experimental results.

The development and characterisation of 3D-printed multi-material thermistor

This paper introduces a multi-material, low-cost, and highly sensitive thermistor concept developed for body temperature monitoring. The designed thermistor employs poly(3,4-ethylenedioxythophene):poly(4-styrenesulfonate) (PEDOT:PSS) for the temperature sensing layer, silver (Ag) for the contact electrodes, and polycarbonate (PC) as the substrate. In contrast to traditional printed electronics substrates used in thermistor development, the utilisation of Material Extrusion via Fused Deposition Modelling (FDM) for the manufacture of PC substrates is the distinct feature of the work. For the manufacture of multi-material thermistors two different Additive Manufacturing (AM) methods: FDM for substrates and micro dispensing for PEDOT:PSS sensing layer and Ag electrodes, were utilised. Two thermistors with varying sensing areas, namely D1 and D2, were designed and fabricated. The thermistors demonstrated a high degree of linearity and repeatability within the temperature range of 25–45°C with a viable hysteresis maximum around 3%. The measurement sensitivity of thermistors was assessed based on Temperature Coefficient of Resistance (TCR) values, which were –0.68 ±0.057% and –0.49 ±0.078% per °C for the D1 design and –0.53 ±0.078% and –0.40 ±0.069% per °C for the D2 design, during heating and cooling, respectively. Comparable average response and recovery times to the literature were also acquired, which were 31.47 ±1.02 and 54.42 ±0.70seconds for D1 and 27.38 ±0.96 and 48.45 ±1.69seconds for D2, during heating and cooling, respectively. It was evident that the sensing area had an impact on thermistor TCR as well as response and recovery times, where higher TCR and faster response and recovery times were recorded with the small sensing area. It was also observed that humidity had a significant impact on thermistor reliability, exhibiting a normalised resistance change of ∼12% and ∼9% of the initial measurement at 50 and 75% RH, respectively. When compared to PEDOT:PSS-based thermistors reported in the literature, the obtained results in this research have demonstrated that our multi-material thermistor concept is a promising candidate for the low-cost and highly sensitive multi-material thermistor that can be manufactured in a fully additive manner using AM technologies.

Build orientation optimization considering thermal distortion in additive manufacturing

Additive manufacturing (AM) technology enables the fabrication of three-dimensional objects with complex shapes and has been extensively applied in various industries. AM is a layer-wise fabrication process where a variety of factors affect manufacturing performance and product quality. One of the most important factor is the thermal distortion, which is caused by the high temperature gradients in the fabrication process. The thermal distortion is also influenced by the support structure of the printing object, and this distortion varies depending on the chosen build orientation. Additionally, for the overhang regions, extra supports are required for printing and will be removed in the post-processing. For the 3D printing process, the thermal distortion and support requirements are interconnected and linked to build orientation. To investigate suitable build orientation, the thermal distortion and support are quantified, and a multi-objective build orientation optimization method is proposed to obtain representative orientations. Based on the proposed method, 9 typical 3D shapes are evaluated. In addition, the single-objective build orientation optimization problem is studied and compared, and the influence of slicing layers per stage on the simulation accuracy and efficiency is discussed. The effectiveness and applicability of the method are verified, and representative directions can be obtained for different fabrication purposes.

Machine learning predictions of spatter behavior in LPBF additive manufacturing

The adoption of additive manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (LPBF), has been rapidly increasing in industries requiring high precision and complex geometries. Despite its advantages, LPBF faces challenges related to defects that affect material quality, with spatter formation being a significant concern. Spatters—tiny particles ejected during the printing process—can adversely affect the final product’s integrity by altering surface roughness and contributing to defects. This study introduces a comprehensive approach to predict the ejection velocity and direction of spatter particles using a suite of machine learning (ML) algorithms, including Random Forest, Gaussian Process Regression, Support Vector Machine, Regularized Linear Regressions, Gradient Boosting Trees, and Neural Networks. Our analysis reveals that the Neural Network model outperforms others, achieving prediction accuracies of 97.58% for spatter velocity and 88.22% for ejection direction, thus offering a substantial improvement in understanding and controlling spatter-related defects in LPBF processes. The practical implications of these predictions are profound, enabling manufacturers to adjust AM parameters in real time to minimize defects and enhance product quality. This study not only fills a gap in the current literature by providing a detailed comparative analysis of multiple ML algorithms for spatter ejection prediction but also paves the way for future research into real-time monitoring and control systems in AM.

A stethoscope-guided interpretable deep learning framework for powder flow diagnosis in cold spray additive manufacturing

Cold spray (CS) particle deposition, also known as cold spray additive manufacturing, has garnered great attention as an advanced additive manufacturing (AM) and surface deposition technology, facilitating rapid and scalable production of functional parts and surfaces in a solid-state manner. In CS, consistent and precise feeding of functional feedstock powders is crucial for achieving effective particle deposition. However, vibratory-based powder feeders often face challenges associated with powder delivery and powder segregation. This underscores the critical need for a precise diagnostic framework to effectively control powder flow during cold spraying. To this end, the present study proposes a powder flow monitoring framework for the CS process using a stethoscope sound-guided interpretable deep learning (IDL) model. Internal sound data from the vibrated powder feeder is collected through a stethoscope sensor to train a two-stage model. In the first stage, a convolutional autoencoder (CAE) is trained to build an unsupervised learning-based anomaly detector, which identifies classification thresholds based on the receiver operation characteristic curve. In the second stage, a convolutional neural network (CNN) model is trained as the powder flow diagnostic tool by considering process anomalies, namely (i) no powder flow; (ii) feeder clogging; and (iii) no gas flow. The results reveal that the stethoscope sound-guided model achieves a classification accuracy of 95% on the test set, significantly outperforming benchmark utilizing typical external-sound recording microphones in diagnosing CS powder flow. Furthermore, the model is visualized and interpreted by employing t-distribution stochastic neighbor embedding and integrated gradients techniques to enhance reliability of CS powder flow diagnosis. This research highlights the effectiveness of the stethoscope sound-guided IDL model for in situ powder flow monitoring and process diagnosis in the domain of cold spray additive manufacturing, contributing to effective particle deposition.

AFSD-Physics: Exploring the governing equations of temperature evolution during additive friction stir deposition by a human-AI teaming approach

This paper presents a modeling effort to explore the underlying physics of temperature evolution during additive friction stir deposition (AFSD) by a human-AI teaming approach. AFSD is an emerging solid-state additive manufacturing technology that deposits materials without melting. However, both process modeling and modeling of the AFSD tool are at an early stage. In this paper, a human-AI teaming approach is proposed to combine models based on first principles with AI. The resulting human-informed machine learning method, denoted as AFSD-Physics, can effectively learn the governing equations of temperature evolution at the tool and the build from in-process measurements. Experiments are designed and conducted to collect in-process measurements for the deposition of aluminum 7075 with a total of 30 layers. The acquired governing equations are physically interpretable models with low computational cost and high accuracy. Model predictions show good agreement with the measurements. Experimental validation with new process parameters demonstrates the model’s generalizability and potential for use in tool temperature control and process optimization.

Synergistic effects of calcium and zinc on bio-functionalized 3D Ti cancellous bone scaffold with enhanced osseointegration capacity in rabbit model

The present research aims to develop a Ca-Zn ion-incorporated surface functionalized 3D Ti cancellous bone scaffold for bone defect repair. The scaffold is designed to mimic human cancellous bone architecture through selective laser melting-based additive manufacturing. The chemical-based surface modification approach employed here created a Ca and Zn ions incorporated nano-porous surface layer with enhanced surface roughness and hydrophilicity. The modified biomimetic scaffold improved corrosion resistance behaviour with ICORR and ECORR values of 0.174 and 0.0097 respectively. It is learned that incorporating Zn as ZnO over the scaffold has antibacterial activity against Staphylococcus aureus and Escherichia coli. The cellular response of MG-63 to the modified scaffold was evaluated through in-vitro studies which focus on the cytocompatible properties. The intra-osseous biomimetic Ti-Na-Ca:Zn 3D scaffold revealed significant improvement in the osseointegration capabilities in terms of bone mineral density (BMD) and bone volume/total volume (BV/TV) in the rabbit model. The osseointegration potential at the Ti-Na-Ca:Zn interface was evidenced by histological analysis and micro-CT imaging. In addition to this, the remarkable upregulation of osteogenic genes such as OCN, COL1A1, OPN, ALP, RUNX2, and OSX evidences the dynamics of the osseointegration process at each surgical period. This Ca and Zn surface functionalised porous architecture of the 3D Ti cancellous bone scaffold similar to bone with enhanced biological response and bone integration can potentially allow implant customisation by utilizing additive manufacturing technology with improved clinical outcomes.

Application of metal additive testing and thermal conductivity technology in optimizing product appearance design in intelligent manufacturing industry

Metal additive manufacturing technology has the advantages of rapid manufacturing and personalized production, but due to the complexity of the manufacturing process and differences in material characteristics, products made with metal additives often have appearance defects and process performance issues. Therefore, the purpose of this study is to explore the application of metal additive detection technology in product appearance design and process performance optimization. This study proposes a comprehensive detection and optimization method that utilizes non-destructive testing techniques to comprehensively inspect metal components. By identifying and locating these issues, the quality status of the components can be accurately understood. Based on the test results, a series of appropriate optimization measures have been taken to improve the appearance and process performance of the product, adjust process parameters, improve the forming quality and surface smoothness of the components, thereby achieving better appearance effects. Metal additive testing technology can effectively detect and locate appearance defects in products, and by optimizing process parameters and material selection, it can significantly improve the appearance quality and process performance of products, providing support for improving the reliability and production efficiency of industrial manufacturing.

High power laser powder bed fusion of Ti6Al4V alloy: The control of defects, microstructure, and mechanical properties

Laser powder bed fusion (LPBF) is the most widely used metal additive manufacturing technology, but it still faces challenges in manufacturing efficiency. To address the issue, high-power LPBF (HP-LPBF) which employs kilowatt-level lasers emerges in recent years. In this study, a 4 kW Flat-top laser was used for the HP-LPBF of Ti6Al4V alloy. The samples in as-built state and annealed states (750°C/2h, 850°C/2h, 950°C/2h, 1050°C/2h) were investigated in terms of defects, microstructure, and mechanical properties. Results show that keyhole mode melting is avoided by adopting the Flat-top laser, so the relative density of as-built Ti6Al4V is generally positively correlated with the employed laser energy density. The minimum laser energy density to obtain the high-density (≥ 99.9%) sample is 50.0 J/mm3, and the highest build rate is 288 cm3/h. The microstructure in as-built state exhibits a unique alternating pattern of "bright bands / dark bands". The dark bands consist of needle-shaped α' martensite, while the bright bands consist of needle-shaped α+β, which results from the decomposition of α' under the in-situ annealing effect of the 4 kW Flat-top laser. After annealing at 750°C, most of the residual α' decomposes into needle-shaped α+β. As the annealing temperature increases from 750°C to 1050°C, the microstructure undergoes an evolution from needle-shaped α+β to lamellar α+β then to lamellar α+β with some globular α and finally to duplex α+β. An optimum strength-ductility balance is achieved after annealing at 850°C, both the tensile strength and the elongation exceed the standard values of Ti6Al4V forgings.

A Critical Review on Polymer Composites Reinforced with Artificial Fibers using Fused Deposition Modelling

The practical application of Additive Manufacturing (AM) technology expands constantly in the automotive, aerospace, and biomedical sectors, among others. Additive manufacturing technology has been categorized by ASTM into seven groups according to the type of material and process used to fabricate the parts. Intellectual property rights of manufacturer restrict the users to process few materials in a machine. Nevertheless, compared to extrusion or injection molding, the mechanical strength and robustness of three dimensional (3D) printed items are significantly lower. First, some researchers have looked into optimizing process factors such build orientation, layer height, infill density, and air gap with the purpose of enhancing the mechanical properties. Secondly, altering the material with reinforcement improves mechanical properties compared with un-reinforced material. Fusion Deposition Modelling (FDM) technology based on 3D printer retains a market share of 40% globally. This article reviews the mechanical performance of FDM feed stock filament of thermo-plastic polymers ABS/PLA/PP with a reinforcement of CF/CNF/CNT/JFRT/CPT. The researchers reported improvements in mechanical characteristics such as strength, strain failure rate, and Young's modulus.

Evaluation of Fused Deposition Modelling process parameters influence on the porosity of printed parts

Fused Deposition Modelling (FDM) is an additive manufacturing technology that is rapidly gaining popularity due to its ability to produce complex shaped parts in a short time. However, parts produced by means of FDM have porosity that results from the printing process. The mechanical properties of printed parts depend on the FDM process parameters and porosity. This study investigates the effect of FDM process parameters on the porosity of FDM parts. The results of the study show that properly selected FDM process parameters can significantly minimize the percentage of part porosity. The effect of FDM process parameters on porosity was determined by analyzing nine process variables: layer height, extrusion temperature, printing speed, extrusion multiplier, bed temperature, infill pattern, deposited strand width, number of shells, number of solid top and bottom layers. The percentage of samples porosity was measured by means of the hydrostatic weighing method. Using the analysis of variance, statistically significant factors and their combinations affecting the percentage of parts porosity were determined. It was shown that the extrusion multiplier had the greatest influence on the percentage of parts porosity. A significant contribution to the formation of porosity was made by the infill pattern, layer heights, printing speed, number of shells, number of solid top and bottom layers and their interaction. A regression model has been developed that allows predicting the porosity of parts achieved by different combinations of FDM process parameters.

Crashworthiness Investigations for 3D-Printed Multi-Layer Multi-Topology Engineering Resin Lattice Materials.

In comparison to monolithic materials, cellular solids have superior energy absorption capabilities. Of particular interest within this category are the periodic lattice materials, which offer repeatable and highly customizable behavior, particularly in combination with advances in additive manufacturing technologies. In this paper, the crashworthiness of engineering multi-layer, multi-topology (MLMT) resin lattices is experimentally examined. First, the response of a single- and three-layer single topology cubic and octet lattices, at a relative density of 30%, is investigated. Then, the response of MLMT lattices is characterized and compared to those single-topology lattices. Crashworthiness data were collected for all topology arrangements, finding that while the three-layer cubic and octet lattices were capable of absorbing 9.8 J and 7.8 J, respectively, up to their respective densification points, the unique MLMT lattices were capable of absorbing more: 19.0 J (octet-cube-octet) and 22.4 J (cube-octet-cube). These values are between 94% and 187% greater than the single-topology clusters of the same mass.

Evaluation of Additives on the Cell Metabolic Activity of New PHB/PLA-Based Formulations by Means of Material Extrusion 3D Printing for Scaffold Applications

In this study, specific additives were incorporated in polyhydroxyalcanoate (PHB) and polylactic acid (PLA) blend to improve its compatibility, and so enhance the cell metabolic activity of scaffolds for tissue engineering. The formulations were manufactured through material extrusion (MEX) additive manufacturing (AM) technology. As additives, petroleum-based poly(ethylene) with glicidyl metacrylate (EGM) and methyl acrylate-co-glycidyl methacrylate (EMAG); poly(styrene-co-maleic anhydride) copolymer (Xibond); and bio-based epoxidized linseed oil (ELO) were used. On one hand, standard geometries manufactured were assessed to evaluate the compatibilizing effect. The additives improved the compatibility of PHB/PLA blend, highlighting the effect of EMAG and ELO in ductile properties. The processability was also enhanced for the decrease in melt temperature as well as the improvement of thermal stability. On the other hand, manufactured scaffolds were evaluated for the purpose of bone regeneration. The mean pore size and porosity exhibited values between 675 and 718 μm and 50 and 53%, respectively. According to the results, the compression stress was higher (11–13 MPa) than the required for trabecular bones (5–10 MPa). The best results in cell metabolic activity were obtained by incorporating ELO and Xibond due to the decrease in water contact angle, showing a stable cell attachment after 7 days of culture as observed in SEM.

The Impact of 3D Printing Technology on the Improvement of External Wall Thermal Efficiency—An Experimental Study

Three-dimensional printing technology continues to evolve, enabling new applications in manufacturing. Extensive research in the field of biomimetics underscores the significant impact of the internal geometry of building envelopes on their thermal performance. Although 3D printing holds great promise for improving thermal efficiency in construction, its full potential has yet to be realized, and the thermal performance of printed building components remains unexplored. The aim of this paper is to experimentally examine the thermal insulation characteristics of prototype cellular materials created using 3D additive manufacturing technologies (SLS and DLP). This study concentrates on exploring advanced thermal insulation solutions that could enhance the energy efficiency of buildings, cooling systems, appliances, or equipment. To this end, virtual models of sandwich composites with an open-cell foam core modeled after a Kelvin cell were created. They were characterized by a constant porosity of 0.95 and a pore diameter of the inner core of the composites of 6 mm. The independent variables included the different material from which the composites were made, the non-uniform number of layers in the composite (one, two, three, and five layers) and the total thickness of the composite (20, 40, 60, 80, and 100 mm). The impact of three independent parameters defining the prototype composite on its thermal insulation properties was assessed, including the heat flux (q) and the heat transfer coefficient (U). According to the experimental tests, a five-layer composite with a thickness of 100 mm made of soybean oil-based resin obtained the lowest coefficient with a value of U = 0.147 W/m2·K.

Vat photopolymerization of ultra-porous bioactive glass foams

The introduction of additive manufacturing technologies in the field of biomaterials science has opened new horizons for regenerative medicine. In this work, we pushed the potential of vat polymerization to the limit for fabricating ultra-porous bioactive SiO2-CaO-MgO-P2O5-CaF2-Na2O glass scaffolds with bone-like architectural characteristics. The tomographic reconstruction of an open-cell foam was used as input file to the printing system and reliably reproduced in all its exquisite details, as assessed by morphological analyses of sintered scaffolds (thickness of single struts 35 μm, exceptionally high porosity around 94 vol%, most pores with size from 500 to 900 μm). Immersion studies in simulated body fluid (SBF) revealed the apatite-forming ability (i.e., in vitro bioactivity) of the scaffolds, the surface of which started being coated by calcium phosphate after just 3 days from the beginning of the experiments. Taken together, these results show great promise for application of such scaffolds in bone defect repair.