Additive Manufacturing Fabrication Research Articles

Adaptive Neuro-Fuzzy Inference System-Based Predictive Modeling of Mechanical Properties in Additive Manufacturing

Predicting the mechanical properties of Additive Manufacturing (AM) parts is a complex task due to the intricate nature of the manufacturing processes. This study presents a novel application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the mechanical properties of PLA specimens produced using Fused Filament Fabrication (FFF). The ANFIS model integrates the strengths of neural networks and fuzzy logic to establish a mapping between the inputs and the output mechanical properties, specifically maximum stress, strain, and Young’s modulus. Experimental data were collected from three-point bending tests conducted on FFF samples fabricated from PLA material with different manufacturing parameters, such as infill pattern, infill, layer thickness, printing speed, extruder and bed temperature, printing orientation (along each axis and twist angle), and raster angle. These data were used to train, check, and validate the ANFIS model. The results reveal that the proposed predictive model can effectively predict the mechanical properties of FFF-printed PLA samples, demonstrating its potential for broader applications across various AM technologies and materials, ultimately enhancing the efficiency and effectiveness of the AM fabrication process.

Machine Learning for Additive Manufacturing of Functionally Graded Materials.

Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.

Anisotropic polymer components with desired elastic properties manufactured through fused filament fabrication additive manufacturing

AbstractFused filament fabrication (FFF) is a popular additive manufacturing (AM) process, primarily used for fabricating polymer components. Optimizing the mechanical properties of FFF components, such as their elastic moduli, is crucial in many applications. This study focuses on adjusting the elastic properties of polymer components manufactured through FFF process by selecting appropriate process parameters. The elastic constants of the anisotropic FFF components are measured by using ultrasonic testing (UT). Response surface methodology (RSM) is employed to determine the optimal settings for these parameters to achieve the desired elastic properties. The effects of layer thickness, printing speed, and raster angle on Young's modulus are explored. Analysis of variance (ANOVA) is used to identify the contributions of each process factor on the output responses. According to ANOVA results, the optimal conditions identified are: a printing speed of 2040 mm/min, a layer thickness of 0.2 mm, and a raster angle of 29°. These conditions collectively achieved the maximum Young's modulus. The differences between the predicted and measured moduli for all responses are less than 5%. The structural factors influencing the results are examined by analyzing the fracture surfaces of the tensile testing (TT) specimens with field emission scanning electron microscopy. Additional measurements of other properties, including ultrasound velocity and wave attenuation, are conducted on the samples. The findings indicate that optimizing the parameters by setting them to their minimum values does not only improve the maximum elastic modulus in specific directions but also reduces attenuation. It is concluded that the desired elastic modulus for a component can be achieved by properly adjusting the process parameters.Highlights Optimizing AM parameters to achieve the desired elastic properties of FFF samples. Examining the effects of each AM parameter by utilizing ANOVA and RSM methods. Measuring the anisotropic elastic properties of AM samples by UT. Verifying UT results through TT and measuring attenuation.

Research on material jetting fabrication and dielectric constant calculation of LTCC based on silica-borosilicate glass

Several material systems are available for low-temperature co-fired ceramics, and to achieve more efficient designs and applications using additive manufacturing (AM) techniques such as material jetting, the predictability of the dielectric constant of the sintered structure will significantly reduce the consumption of material development. In this work, the LTCC part was fabricated by 3D material jetting using silica-borosilicate glass LTCC ink and densified at 850°C. The borosilicate glass composition was derived from nanopowders prepared by radio frequency plasma spheronisation and configured as surface-grafted glass sols. Scanning electron microscopy results showed that silica particles were embedded in the glass matrix by a liquid phase sintering mechanism accompanied by several confined pores that were difficult to remove. A dielectric constant of around 3.78 was tested from room temperature to 100°C. Molecular dynamics sintering simulation was used to create sintered structures that matched the experimental values. The dipole moment fluctuation of the silica/glass bulk was counted to obtain an accurate value of the dielectric constant, and then the Bruggeman model based on the effective medium theory predicted the dielectric constant of the sintered body, with a prediction of 3.65, which is within 4 % difference from the measured value (3.78). The dielectric constant was predicted for different ceramic/glass compositions and sintering temperatures and found that 35 wt% and 60 wt% should be the upper and lower bounds for borosilicate glass additions to satisfy a stable structure with a relatively low dielectric constant range (3.38–4.42). In conclusion, an effective molecular dynamics approach to predict the dielectric constant of sintered bodies was developed to assist material design in LTCC AM fabrication.

Utilizing the derivative of unwrapped phase in ultrasonic nondestructive evaluation of elastic properties of polymer filaments.

Polymer filaments represent the fundamental materials employed in fused filament fabrication additive manufacturing. This paper uses an innovative nondestructive evaluation technique for gauging the elastic properties of these polymer filaments. The method hinges on acoustic wave scattering theory, wherein a polymer filament is immersed in water and exposed to an incident acoustic wave. The waves scattered from the cylinder contain crucial information concerning the elastic properties of the filament. To extract these properties, an inverse method is applied which compares the resonance frequencies of the scattered signal with those anticipated based on a theoretical model. To improve the performance of this method in identifying the resonance frequencies, the derivative of the unwrapped phase within the backscattered pressure spectrum is analyzed. This is an advantageous approach as it can be equally applied to both experimental and theoretical pressure spectra, simplifying the task of identification of resonance frequencies considerably. The method's effectiveness is exemplified through its application to an aluminum rod followed by its application to two polymer filaments. Comparing the elastic constants of the polymer filaments determined through the proposed method with values reported in the literature underscores the method's capability to accurately measure the elastic constants of polymer filaments.

Electroanalysis overview: additive manufactured biosensors using fused filament fabrication.

Additive manufacturing (3D-printing), in particular fused filament fabrication, presents a potential paradigm shift in the way electrochemical based biosensing platforms are produced, giving rise to a new generation of personalized and on-demand biosensors. The use of additive manufactured biosensors is unparalleled giving rise to unique customization, facile miniaturization, ease of use, economical but yet, still providing sensitive and selective approaches towards the target analyte. In this mini review, we focus on the use of fused filament fabrication additive manufacturing technology alongside different biosensing approaches that exclusively use antibodies, enzymes and associated biosensing materials (mediators) providing an up-to-date overview with future considerations to expand the additive manufacturing biosensors field.

A Real-Time Defect Detection Strategy for Additive Manufacturing Processes Based on Deep Learning and Machine Vision Technologies.

Nowadays, additive manufacturing (AM) is advanced to deliver high-value end-use products rather than individual components. This evolution necessitates integrating multiple manufacturing processes to implement multi-material processing, much more complex structures, and the realization of end-user functionality. One significant product category that benefits from such advanced AM technologies is 3D microelectronics. However, the complexity of the entire manufacturing procedure and the various microstructures of 3D microelectronic products significantly intensified the risk of product failure due to fabrication defects. To respond to this challenge, this work presents a defect detection technology based on deep learning and machine vision for real-time monitoring of the AM fabrication process. We have proposed an enhanced YOLOv8 algorithm to train a defect detection model capable of identifying and evaluating defect images. To assess the feasibility of our approach, we took the extrusion 3D printing process as an application object and tailored a dataset comprising a total of 3550 images across four typical defect categories. Test results demonstrated that the improved YOLOv8 model achieved an impressive mean average precision (mAP50) of 91.7% at a frame rate of 71.9 frames per second.

Effects of annealing treatment on microstructure and mechanical properties of laser melting-deposited VCoNi medium-entropy alloy

Additive manufacturing (AM) is a promising approach to producing high- or medium-entropy alloys (HEAs or MEAs) with excellent properties. However, some undesirable microstructural defects and unstable precipitated phases were formed owing to the repeated heating and cooling steps and the rapid solidification nature of the AM process. Annealing treatment after AM fabrication is often beneficial for adjusting the comprehensive mechanical properties and stabilizing the microstructures. In this study, a VCoNi MEA single wall sample was fabricated successfully via laser melting deposition (LMD), and the effects of post-annealing treatments on the microstructures and mechanical properties were explored. The grain size and σ-phase particle size increase and the σ-phase particles undergo fragmentation, spheroidization, and coarsening during annealing. As the annealing time increases, the tensile strain increases, but the tensile strength decreases. An excellent combination of strength and ductility can be obtained after annealing for 1.5 h (The yield strength, ultimate tensile strength, and tensile strain are 741.2 MPa, 1287.8 MPa, and 30.5 %, respectively). HDI hardening is the most important strengthening mechanism in this work. The decrease in tensile stress after the annealing treatment is mainly attributed to the decrease of heterogeneity of microstructure, and the increase in tensile strain after the annealing treatment is mainly attributed to the decrease of the pores and the spheroidization of the rod-like σ-phase particles. This work provides practical insight into adjusting the microstructure and comprehensive mechanical properties of HEAs and MEAs for AM.

Pre-melted electron beam freeform fabrication additive manufacturing: modeling and numerical simulation

Pre-melted electron beam freeform fabrication additive manufacturing: modeling and numerical simulation

Corrosion-Fatigue Performance of 3D-Printed (L-PBF) AlSi10Mg.

Additive manufacturing (AM) allows for optimized part design, reducing weight compared to conventional manufacturing. However, the microstructure, surface state, distribution, and size of internal defects (e.g., porosities) are very closely related to the AM fabrication process and post-treatment operations. All these parameters can have a strong impact on the corrosion and fatigue performance of the final component. Thus, the fatigue-corrosion behavior of the 3D-printed (L-PBF) AlSi10Mg aluminum alloy has been investigated. The influence of load sequence (sequential vs. combined) was explored using Wöhler diagrams. Surface roughness and defects in AM materials were examined, and surface treatment was applied to improve surface quality. The machined specimens showed the highest fatigue properties regardless of load sequence by improving both the roughness and removing the contour layer containing the highest density of defect. The impact of corrosion was more pronounced for as-printed specimens as slightly deeper pits were formed, which lowered the fatigue-corrosion life. As discussed, the corrosion, fatigue and fatigue-corrosion mechanisms were strongly related to the local microstructure and existing defects in the AM sample.

Current monitoring for a fused filament fabrication additive manufacturing process using an Internet of Things system

The Fourth Industrial Revolution (Industry 4.0) has led to the transformation of manufacturing systems into digital ecosystems. The Internet of Things (IoT) and other emerging technologies play a significant role in this shift. Smart sensor systems are necessary to integrate their resources into the digital world to move small and medium size manufacturers toward digital transformation. The proposed work offers an IoT-based current monitoring system for a fused filament fabrication (FFF) additive manufacturing process to assist in the digital transformation efforts. In this work, we investigate how a widely available microcontroller paired with a non-invasive split core current transformer can create functional data streams to support critical industry-related applications. We tested the IoT solution using two different sampling algorithms and different sampling windows to assess how they influence the output of the device. Our results indicate the successful current draw monitoring of static resistive power loads and an FFF additive manufacturing process. More specifically, the IoT device was able to differentiate between different levels of current draw and produces output that is suitable for a wide range of manufacturing machinery applications such as power monitoring, downtime recognition, task recognition, and condition monitoring.

Additive manufacturing for Proton Exchange Membrane (PEM) hydrogen technologies: merits, challenges, and prospects

With the growing demand for green technologies, hydrogen energy devices, such as Proton Exchange Membrane (PEM) fuel cells and water electrolysers, have received accelerated developments. However, the materials and manufacturing cost of these technologies are still relatively expensive which impedes their widespread commercialization. Additive Manufacturing (AM), commonly termed 3D Printing (3DP), with its advanced capabilities, could be a potential pathway to solve the fabrication challenges of PEM parts. Herein, in this paper, the research studies on the novel AM fabrication methods of PEM components are thoroughly reviewed and analysed. The key performance properties, such as corrosion and hydrogen embrittlement resistance, of the additively manufactured materials in the PEM working environment are discussed to emphasise their reliability for the PEM systems. Additionally, the major challenges and required future developments of AM technologies to unlock their full potential for PEM fabrication are identified. This paper provides insights from the latest research developments on the significance of advanced manufacturing technologies in developing sustainable energy systems to address the global energy challenges and climate change effects.

Synthesis of Rectangular Waveguide Filters With Smooth Profile Oriented to Direct Metal Additive Manufacturing

In this article, a novel design method for rectangular waveguide filters intended for fabrication using direct metal additive manufacturing (AM) is proposed. The synthesized filters will feature a smooth profile that allows us to fabricate them orienting the filter propagation axis in the vertical building direction, achieving an optimum configuration for direct metal AM fabrication. The novel design method is valid for any all-pole transfer function, which is initially implemented with a commensurate-line distributed unit element prototype. The impulse response of that initial prototype is then properly interpolated to obtain the target response for a smooth-profiled filter with similar length and profile excursion. Finally, the target impulse response just generated is implemented in the rectangular waveguide technology employing a novel inverse scattering synthesis technique that relies on the coupled-mode theory to model the electromagnetic behavior of the waveguide filter. The novel inverse scattering synthesis technique is general and also valid for the case of filters with very high rejection levels, which is of great relevance in rectangular waveguide technology. A Ku-band low-pass filter with stringent satellite specifications is designed using the proposed method, fabricated by means of a direct metal AM technique, and measured with a vector network analyzer. A very good agreement is achieved between the simulated and measured results, fulfilling the required specifications and demonstrating the feasibility and performance of the novel design method.

Quantitative analysis of bone regeneration efficacy as shape conformity of scaffold: Evidence for importance of additive-manufacturing precision in tissue engineering

Additive manufacturing (AM) of customized scaffolds has been actively studied in tissue engineering. Most studies have focused on the functionalization of scaffolds for osteoinduction and osteoconduction for bone regeneration. However, in our previous study, we found that shape conformity between a defect and the scaffold was also a major factor with regard to enhancing bone regeneration. Thus, in this study, we performed a quantitative analysis of the shape conformity between the defect and scaffold using several novel experimental scaffolds. Employing several scaffolds with different conformities that increased linearly, the degree of bone regeneration was analyzed through new bone volume measurements and a histological analysis in conjunction with an in vivo experiment using rat calvaria. For the quantitative analysis, we compared the new bone volume reconstructed from a three-dimensional model based on CT data. New bone maturation was analyzed with respect to shape conformity through a histological analysis. Proliferation, maturation, and new bone formation were promoted by scaffold shape conformity. Thus, for bone regeneration, the AM precision and the fabrication technique are critical as the shape conformity of the scaffold to the bone defect is highly dependent on AM fabrication errors.

Gravity Augmented Fused Filament Fabrication Additive Manufacturing

Abstract This article introduces the design, methods, and use cases of a novel gravity augmented additive manufacturing (GAAM) approach to fused filament fabrication (FFF) using a novel seven degree-of-freedom (DoF) delta robotic system. Capable of rotating parts and approaching the workpiece with the deposition head from user-specified or algorithm-determined angles, this system allows users the design freedom to create objects with self-supporting capability, while improving the performance of three-dimensional (3D) printed components. Additionally, this system and the methods of operation described below allow users to create objects that are otherwise impossible or impractical to construct using traditional three axis FFF 3D printing, while maintaining compatibility with existing G-code preparation techniques. Finally, this more flexible 3D printing system has advanced applications in generating patient specific objects, which may benefit from more highly specialized toolpaths and design freedom afforded by this system.

On the process of designing material qualification type specimens manufactured using laser powder bed fusion

Recent investigations involving specimens for material and process qualification related to the Laser Powder Bed Fusion (L-PBF) additive manufacturing (AM) method, point that microstructural quality and surface properties vary significantly based on feedstock material and process parameters. There is currently the need, therefore, to create systematic ways to design specimens for process and material qualification. This manuscript provides an approach to evaluate the effects on the as-built quality based on specific metrics for specimens produced by L-PBF. Surface quality, as well as micro- and sub-microstructures and defects are related to specific aspects of the L-PBF process. To accomplish this goal, a digital engineering approach was first developed to design geometries based on process simulations to account for mechanical and thermal process effects. A parametric AM fabrication plan was then chosen to produce statistically significant populations of AM parts. Moreover, on axis, melt-pool scale, in-situ monitoring data, collected during the AM process, was examined in conjunction with the formation of flaws. X-ray micro-computer tomography analysis was additionally used to characterize porosity levels. Finally, scanning electron microscopy (SEM) and electron back-scatter diffraction (EBSD), as well as surface profilometry were combined to correlate specific process parameters and conditions with microstructural and surface effects.

Modeling the strength of laminated parts made by fused filament fabrication additive manufacturing

Fused filament fabrication (FFF, also known as fused deposition modeling) is the most popular 3D printing additive manufacturing technology: cheap 3D printers are largely widespread and most polymeric materials can be used. It is probably the most versatile additive manufacturing technology. The applications range from prototyping to the production of custom parts with structural capabilities: fiber reinforced plastics can also be used. However, the knowledge of the FFF materials and the design criteria for fused filament fabricated parts are scarcely known and this results in a very common skepticism in adopting this technology for technical structural components. As for the other processing technologies for plastics, included injection molding, the mechanical properties of the components strongly depend on the manufacturing parameters. As shown by some authors, since production by FFF produces a layered structure, it is possible to model the mechanical behavior by means of the classical lamination theory largely verified and known for composites. In those papers, it was shown that the equivalent elastic properties could be predicted as a function of the lamination stacking and angles. In this paper, based on experimental results obtained on symmetric balanced angle-ply laminated samples made of polyethylene terephthalate with added glycol (PETG) and polyamide (PA) subject to tensile loads, the Tsai-Hill criterion was applied to predict the strength. After identification of the strength parameters, a good correlation of the experimental with the model parameters, for all lamination angles, was obtained.

Fluid topology optimization and additive manufacturing of a liquid atomizer using an extensive number of grid points

Additive manufacturing (AM) can fabricate complicated shapes and is useful when manufacturing topology optimized shapes. Fluid parts often consists of three dimensional curves that are suitable for AM fabrication. However, the application of fluid topology optimization and AM has not been investigated yet. However, modeling and solving an optimization problem have not been investigated for a real industrial fluid topology optimization problem of AM parts with tiny channels, i.e., a liquid atomizer which is equipped with an aero-engine fuel injector. In order to reduce computation time, which is an important issue in real industrial problem, the instantaneous sensitivity approximation method is used as a topology optimization method. The optimized part exhibited a reduction in pressure loss compared to that of a conventional part.

Additive Manufacturing for Lightweighting Satellite Platform

Lightweight structures with an internal lattice infill and a closed shell have received a lot of attention in the last 20 years for satellites, due to their improved stiffness, buckling strength, multifunctional design, and energy absorption. The geometrical freedom typical of Additive Manufacturing allows lighter, stiffer, and more effective structures to be designed for aerospace applications. The Laser Powder Bed Fusion technology, in particular, enables the fabrication of metal parts with complex geometries, altering the way the mechanical components are designed and manufactured. This study proposed a method to re-design the original satellite structures consisting of walls and ribs with an enclosed lattice design. The proposed new structures must comply with restricted requirements in terms of mechanical properties, dimensional accuracy, and weight. The most challenging is the first frequency request which the original satellite design, based on traditional fabrication, does not satisfy. To overcome this problem a particular framework was developed for locally thickening the critical zones of the lattice. The use of the new design permitted complying with the dynamic behavior and to obtain a weight saving maintaining the mechanical properties. The Additive Manufacturing fabrication of this primary structure demonstrated the feasibility of this new technology to satisfy challenging requests in the aerospace field.

Dislocation structures formation induced by thermal stress in additive manufacturing: Multiscale crystal plasticity modeling of dislocation transport

The motion of dislocations governs the plastic deformation of crystalline materials, which in turn determines the mechanical properties. The complex thermal history, large temperature gradients and high cooling rates during the process of additive manufacturing (AM) can induce high dislocation density and unique dislocation structures in the material. The origin of these dislocation structures and their stability during mechanical loading are debated. A novel temperature dependent continuum dislocation dynamics (CDD) model is developed, in which four state variables are used for each slip system representing the total dislocation density, edge and screw geometrically necessary dislocation densities and dislocation curvature. The CDD model is fully coupled with a crystal plasticity solver, which captures the plastic deformation induced by the dislocation motion. A hybrid continuous and discontinuous Galerkin formulation is developed to accurately reproduce the dynamics of highly discontinuous dislocation density fields that are typical of dislocation structures. A multiscale modeling approach is used, in which the thermally induced deformation in specific grains of a polycrystal is extracted from larger scale crystal plasticity simulations of the laser powder-bed fusion process, and is then used for single crystal scale dislocation dynamics simulations. Simulation results reveal the dynamics of dislocation structure formation in grains at different positions during laser scanning and cooling stages. The effect of the cyclic thermal stress during multi-layer AM fabrication is also investigated. The simulations provide a new perspective on the specific conditions that should be satisfied during AM process for the formation of stable dislocation structures.