Additive Manufacturing Technology Research Articles

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.

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.

Optimal Design of a Bilateral Stand-Alone Robotic Motion-Assisted Finger Exoskeleton for Home Rehabilitation

This paper presents a novel exoskeleton robot that can be used at home to rehabilitate the index fingers of stroke-affected patients. This exoskeleton is designed as a one-degree-of-freedom four-bar mechanism able to guide the human index finger to perform a finger curl exercise motion. The proposed device is the only lateral, stand-alone mechanism built to date that can carry the weight of the human hand, thus making the user free from wearing it. The design starts by tracing the trajectory of the index finger using ‘Angulus’ software. ‘SALAR Mechanism Synthesizer’ software is used for dimensional synthesis of the four-bar mechanism. Using additive manufacturing technology, a prototype of the proposed device is developed. Static force analysis is performed to select the most appropriate actuator for producing the required torque to manipulate the fingers effectively. The kinematics of the index finger while performing a finger curl exercise is obtained. The proposed linkage mechanism can drive the index fingers of both hands. Simulation and experimental results proved the feasibility and effectiveness of the proposed design to be used for index finger rehabilitation for a wide range of users and applications by making simple minor alterations in the design. Also, a scheme for when the device can be used for rehabilitating the middle finger together with the index finger when performing flexion and extension motions is discussed.

Effect of Deposit Scale on Mechanical Properties of In-Situ Alloyed CrCoNi Medium Entropy Alloys Formed by Directed Energy Deposition

Directed energy deposition (DED), as an additive manufacturing technology, has shown unique advantages in multi-material additive manufacturing and remanufacturing. In this study, two types in-situ alloyed CrCoNi medium entropy alloys that have thin-walled structures with different thicknesses (T1 and T2) were manufactured by the DED process, and the mechanisms of differences in relative density, microstructure, and mechanical properties at different heights were systematically analyzed. In terms of microstructure, the T1 and T2 samples along the building direction exhibit significant differences in crystallographic orientation, grain size, and dislocation density, which are related to the local temperature gradient differences caused by the scanning path and heat accumulation. In terms of mechanical properties at different heights of the two types of thin-walled structures, the yield strength is higher but the elongation is lower at the bottom position of sample, while the yield strength is lower but the elongation is higher at the middle and top positions. The differences of mechanical properties at different heights of the T1 and T2 samples are related to the microstructure and relative density. This finding provides new insights for the design and performance analysis of complex thin-walled structures formed by additive manufacturing.

Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing

The advent of 3D printing has transformed manufacturing. However, extending the library of materials to improve 3D printing quality remains a challenge. Defects can occur when printing parameters like print speed and temperature are chosen incorrectly. These can cause structural or dimensional issues in the final product. This review investigates closed‐loop artificial intelligence‐augmented additive manufacturing (AI2AM) technology that integrates AI‐based monitoring, automation, and optimization of printing parameters and processes. AI2AM uses AI to improve defect detection and prevention, improving additive manufacturing quality and efficiency. This article explores generic 3D printing processes and issues using existing research and developments. Next, it focuses on fused deposition modeling (FDM) printers and reviews their parameters and issues. The current remedies developed for defect detection and monitoring in FDM 3D printers are presented. Then, the article investigates AI‐based 3D printing monitoring, closed‐loop feedback systems, and parameter optimization development. Finally, closed‐loop 3D printing challenges and future directions are discussed. AI‐based systems detect and correct 3D printing failures, enabling current printers to operate within optimal conditions and minimizing the risk of defects or failures, which in turn leads to more sustainable manufacturing with minimum waste and extending the library of materials.

Elastocaloric effect of NiTi shape memory alloys manufactured by laser powder bed fusion

To address the challenges posed by the complex processing of NiTi alloys, the additive manufacturing technology has been employed for the preparation of NiTi alloys samples. Up to now, there is limited research on the elastocaloric effects of NiTi alloys manufactured by LPBF. This study focused on the utilization of laser powder bed fusion (LPBF) to fabricate NiTi samples, with a particular emphasis on the influence of laser power, scanning speed and hatch spacing on the microstructure and properties of NiTi alloys. The results indicated that the process parameters affect the stress hysteresis of superelasticity by influencing the Ni content in the NiTi alloys. The higher Ni content, the lower the stress hysteresis. In addition, the critical stress for plastic deformation of the texture in the <001> direction in NiTi alloys is the smallest. When only the P was changed, the proportion of the texture in the <001> direction in the sample increased with the increase of P, the proportion of plastic deformation increased, and the degree of recovery also gradually decreased. Furthermore, at a specific temperature, 10 K higher than the end temperature of austenite transformation, each sample exhibited good superelasticity. In particular, the 1st cooling capacity of the sample with the best elastocaloric effect reached 11.0 K, which was superior to other results obtained by LPBF under similar compressive strain levels.

Equal–Additive–Subtractive Remanufacturing Integrated Laser Directed Energy Deposition with Shot Peening and Machining Induced High Performance of Plunger Rod

The number of easily destroyed parts with high value is increasing in industry, and green remanufacture engineering is now mainstream in this new and expanding industrial field. Equal–additive–subtractive manufacturing, as a new technology that combines strengthening technology, additive manufacturing, and machining technology has great potential for development in the area of remanufacturing. Aiming at the damage characteristics of a plunger rod, this paper carries out a study about the repair technology by equal–additive–subtractive manufacturing of laser-directed energy deposition and shot peening. It was found that the microstructure of the materials repaired by equal–additive–subtractive technology is finer and the tensile strength can reach 100.4% of the base material. The surface residual stress of cladding materials changes from tensile stress to compressive stress, which reduces forming defects. Equal–additive–subtractive manufacturing has great significance in expanding the application of hybrid manufacturing and promoting green remanufacturing of parts with high value.

Energy absorption structure with negative stepped plateau force characteristics under quasi-static loads

The force-displacement curve is the key to determine the energy absorption performance. Customizing the force-displacement curve enables the design of more advanced and efficient energy absorption systems. In this paper, we introduce a negative stepped plateau force structure (NSPFS) design by combining crooked plate units with curved and straight combined cross-section (CSCC) units. First, the NSPFS is fabricated by using additive manufacturing technology, and a compression experiment is conducted to validate the design methodology. Then, a finite element model is developed and compared with the experimental results to verify its effectiveness. The peak force (PF) and mean crushing force (MCF) of the crooked plate unit are primarily controlled by wall thickness t1 and initial angle θ1. When θ1 = 1.146° and t1 = 1.0 mm of crooked plate unit, the lowest point of the plateau phase decreases by 84 % compared to the PF, and the ratio of PF to MCF is 4.9. When the PF of the crooked plate unit is greater than the PF of the CSCC unit, the structure has the characteristics of negative stepped plateau force. More phase plateau forces can be obtained when multiple NSPFS are compressed in series. When multiple NSPFS units are compressed in parallel, multi-phase continuous negative stepped plateau force can be achieved. Therefore, NSPFS allows for the customization of complex force-displacement curves through series and parallel design.

Use of Pulsed Potentials for Electropolishing TA6V Parts Elaborated By Additive Manufacturing in Deep Eutectic Solvent

The Ti-6Al-4V alloy's high mechanical properties, low density, excellent biocompatibility, and corrosion resistance make it an ideal material for sectors such as aeronautics, astronautics and medical technology. The demand for increasingly complex part designs is speeding up the development of Additive Manufacturing (AM) technology, enabling the creation of shapes unachievable with traditional machining. However, the parts manufactured present significant surface roughness, thus reducing resistance to corrosion and mechanical stress. To mitigate these risks, homogeneous polishing of the entire shape is required. Electrochemical polishing is an all-round part treatment technique ideal for addressing AM challenges. Deep Eutectic Solvent (DES) electrolytes are well-suited for this application. The aim of this study is to develop electropolishing of Ti-6Al-4V parts in a DES solvent of ethylene glycol and choline chloride. The challenges posed by viscous layer thickness problems, as well as two methods to overcome them, are described, including agitation control and pulsed potentials.