Structures For Additive Manufacturing Research Articles

Vat photopolymerization of dual-scale porosity alumina honeycombs using phase-separable camphene porogen dissolved in binary photopolymer blends

Dual-scale porosity ceramics comprised of macro-scale pore networks surrounded by porous frameworks manufactured by additive manufacturing (AM) can offer significantly enhanced functions compared to conventional porous ceramics. However, exploring new ways of more tightly controlling micro-scale porous structures for AM still remains challenging. Here, we propose a novel type of vat photopolymerization using camphene as a phase-separable porogen dissolved in binary photopolymer blends for manufacturing dual-scale porosity alumina honeycombs. More specifically, solid camphene with various contents (40 vol%, 50 vol%, and 60 vol%) could be completely dissolved in blends of triethylene glycol dimethacrylate (TEGDMA) and polyethylene glycol diacrylate (PEGDA) used as solvent and anti-solvent, respectively, at room temperature and then crystallize dendritically at ∼ 5 °C via phase separation. After which, thin layers of phase-separated alumina suspensions could be selectively photopolymerized by a digital light processing (DLP) engine to construct 3-dimensionally interconnected macro-scale pore channels. In addition, micro-scale pores could be created in alumina frameworks by removing camphene-rich crystals by freeze-drying. This new approach enabled manufacturing of well-defined macro-scale honeycomb structures with tailored micro-scale porosities. As the camphene content increased from 40 vol% to 60 vol%, micro-scale porosity increased from 39.1 ± 0.5 vol% to 57.5 ± 0.6 vol%, and thus the overall porosity increased from 58.5 ± 0.1 vol% to 68.8 ± 0.3 vol%, while the compressive strength decreased from 355.6 ± 67.1 MPa to 97.6 ± 10.2 MPa.

Internal damping measurements of additive manufactured metal beams

Modern additive manufacturing (AM) techniques have created an endless number of new design possibilities. Naturally, it is important to understand how the material properties, including internal damping, differ for AM structures. An experimental procedure has been developed to measure an upper bound for the internal damping of metal beams by minimizing the effects of energy dissipation at the supports and acoustic radiation into the surrounding air. In this talk, measurements for the loss factor of several common metals will be compared to equivalent samples constructed by powder bed fusion at varying angles. The results will also be compared to Zener’s thermoelasticity model, developed for isotropic Euler beams in flexure. Reasons for deviation from theory which arise from the manufacturing technique will be explored.

Plasma wire arc additive manufacturing and its influence on high-carbon steel substrate properties

Steels with a high carbon content are considered to be difficult to weld. High carbon equivalent indicates not only problems when joining such steels but also when using them as substrate in additive manufacturing (AM). In this study, the possibility of manufacturing a high-strength steel structure on a high-carbon steel substrate using plasma wire arc additive manufacturing (PWAAM) is demonstrated. This study deals with the thermal history and its effects on the substrate resulting from a multilayer build-up. In-substrate temperature measurements as well as metallographic and hardness measurement will provide an understanding of the influence of the process on the substrate. Additionally, a local pre-heating concept using penetration depth induction (PDI) is considered. PDI also enables energy rearrangement in which energy is removed from the AM process and introduced directly into the substrate by PDI. These variants of the preheating concept are also being investigated concerning their effects on the substrate and its properties. A comparison of multilayer buildup without and with PDI is intended to show the differences but also the possibilities that can be achieved with the PDI approach. The investigations carried out are intended to create the basis for a quality AM structure on a high-carbon steel substrate as well as optimized properties of the substrate. It is also shown that the transition zone between the substrate and the AM structure is not trivial for such a material combination.

A comparison of high strain rate response and adiabatic shear band formation in additively and traditionally manufactured Inconel 718

3D printing of Inconel 718 has been increasingly commercialized, as such research comparing the mechanical properties of additively manufactured (AM) and traditionally manufactured (TM) components in both quasi-static and high rate regimes should be conducted. Herein, the high strain rate material response, as well as the formation of adiabatic shear bands (ASBs) in AM and TM Inconel 718 are studied using a split Hopkinson pressure bar (SHPB) system. A top-hat cylindrical specimen geometry was used to convert the compressive load to shear and conduct high rate shear tests in the SHPB. The quasi-static and high rate stress-strain data reported herein indicates that TM components have at least a 70 MPa higher compressive yield strength. Interestingly, the AM specimens were more susceptible to ASB formation than their TM counterparts. The critical shear strain at which an ASB forms was higher for the TM specimens compared to the AM specimens. This was attributed to the TM material having a higher critical stress for dynamic recrystallization initiation, which is required to ASB formation. The microstructures of the shear zone in the top-hat specimens were examined using an optical microscope (OM), scanning electron microscope (SEM), and transmission electron microscope (TEM). Dynamic recrystallization was observed at the center of the shear affected zone (SAZ) using TEM from material lifted out from the ASB. Due to the plastic deformation in the shear region, the material strain hardens and therefore has a higher hardness than the bulk material. At the center of the shear region, a peak in nano-hardness of 585 HV was recorded on the ASB. The outcome of this research will lead to the prevention, containment, and control of shear instability in AM structures.

Shakedown-reliability based fatigue strength prediction of parts fabricated by directed energy deposition considering the microstructural inhomogeneities

For mechanical components made by additive manufacturing (AM) techniques such as directed energy deposition (DED), micropores and other defects prevalently exist in the microstructure and they significantly reduce the reliability of these parts. To allow the influence of these microstructural features over the load bearing capacity to be considered in the design stage of AM structures, in the present paper we developed a multiscale numerical approach. The goal of this approach is to predict the failure probability of AM structures subjected to time-varied loadings, and it is realized through combining statistical homogenization, shakedown analyses, and reliability methods such as first-order reliability method and Monte Carlo simulation. Based on this strategy, we employed statistics of material parameters obtained from micromechanical models as inputs, implemented numerical tools and applied them to two exemplary structures, the plate with a hole and an aircraft bracket. Through a series of case studies carried out using different methods and under different assumptions of material randomness, the paper confirmed the robustness of the derived results and explained the mechanism of how micropores influence the structural reliability. The method developed in this paper can be a viable means for the design and optimization of metallic and metal matrix composite structures produced by AM techniques.

A comprehensive analysis of guided-wave propagation in 3D-printed PLA plates with different infill densities – Experimental study

Thanks to its many advantages, additive manufacturing (AM) is witnessing a gigantic shift from small applications to the production of complex industrial components, including safety–criticalapplications like aerospace and civil construction. This arises the need to develop accurate and robust technologies for evaluating and monitoring the structural integrity of AM components. The objective of this study is to comprehend how ultrasonic guided waves (GWs) propagate in 3D-printed poly(lactic acid) (PLA) plates of different infill densities. The experimental investigation involves five intact PLA plates printed with infill densities ranging from 20 to 100% at a step of 20%. A scanning laser Doppler vibrometer was used to visualize GW propagation by performing full-wavefield area-scans. Spatial-time, frequency-wavenumber, and wavenumber-wavenumber analyses were performed to scrutinize the availableGW modes and their slowness profiles within the anisotropic structure. In addition, the tuning of the fundamental Lamb-wave (LW) modes at different excitation frequencies was examined, and the amplitude attenuation of the generally most pronounced mode (A0 mode) was investigated for different infill densities at an excitation of 50 kHz. A reference methodology is presented to experimentallyidentify more than five propagating modes in the frequency-wavenumber domain then obtain their phase-velocity dispersion curves. Significant variations are observed in the dispersion characteristics when lowering the infill density leading to the appearance of more modes at lower cutoff frequencies and drops in both the group and phase velocities in most of the cases. A complex behavior of the guided waves was observed in the dispersion curves of the 20%-infill plate, with possible mode interruptions/disturbance at an excitation frequency beyond 70 kHz. The appearance of shear-horizontal modes is noticed/strengthened at oblique propagation directions which may be attributed to mode conversion of the LW modes after obliquely interacting with the internal structure of the plates. Though high wave attenuation is a characteristic of the used polymer material, this attenuation is further increased at lower infill densities. The tuning behavior of the fundamental LW modes is also shown to be significantly affected by the infill density. The presented results can serve as reference propagation and dispersion characteristics for researchers working on similar materials and geometries. Such knowledge and understanding can be particularly useful for GW-based structural healthy monitoring of AM structures.

Preliminary tests on additive-manufactured Al-Sc specimens for the setup of a numerical model for Laser Shock Peening

Aluminum-Scandium alloys offer a great potential in aerospace applications due their high corrosion resistance and improved strength properties. Furthermore, these alloys have been qualified for laser additive manufacturing (AM), producing parts with static strengths rivalling their conventionally manufactured counterparts. However, laser processing also results in large residual stresses that can severely affect fatigue properties and result in geometric distortion. A proven method for reducing the fatigue-related problems in metallic structures is to drive compressive residual stresses into the affected area by means of Laser Shock Peening (LSP). This surface treatment is very effective in bulk structures, improving life performances of fatigue-sensitive aeronautical components, such as jet engines turbine blades or helicopter gearboxes. On the other hand, quite a limited number of studies has been presented on the effect of LSP on fatigue crack growth in thin components and laser AM structures. This work presents first the results of preliminary tensile tests on additive manufactured Al-Sc specimens. The tensile strengths of as-built and heat-treated samples are compared. Then, a reliable and computationally time-effective numerical model of laser peening is reviewed, referring to case studies investigated earlier. In view of applying LSP to additive manufactured Al-Sc components, the effects of different laser parameters and geometries are discussed. Finally, the possible drawbacks of the LSP treatment are addressed, in order to exploit its full potential in increasing the fatigue life of AM components.

Optimization of Additively Manufactured Interposers for DC and RF Applications in Printed Circuit Boards

Additive manufacturing (AM) for printed electronics (PE), though still in its infancy, has seen major growth in the past few years. AM techniques have several benefits over traditional manufacturing, namely the reduction of waste material and the increased efficiency in producing prototypes. However, there are disadvantages associated with AM, the most prevalent being a low throughput of parts. Limited research has been done to increase the throughput to the scale of traditional manufacturing methods. To combine the benefits of both AM and traditional manufacturing techniques, this paper discusses a hybrid printed circuit board (PCB) assembly that employs an additively manufactured interposer to serve as a DC interconnect between the AM structure and the PCB manufactured with traditional methods.

Influence of topology optimization parameters on the mechanical response of an additively manufactured test structure

Topology Optimization (TO) determines a material distribution within a domain under given conditions and design constraints, and generally generates complex geometries as a result. Complementary to TO, Additive Manufacturing (AM) offers the ability to fabricate complex geometries which may be difficult to manufacture using traditional techniques such as milling. AM has been used in multiple industries including the medical devices area. Hence, TO may be used to create patient-matched devices where the mechanical response is catered to a particular patient. However, during a medical device regulatory 510(k) pathway, demonstrating that worst-cases are known and tested is critical to the review process. Using TO and AM to predict worst-case designs for subsequent performance testing may be challenging and does not appear to have been thoroughly explored. Investigating the effects of TO input parameters when AM is employed may be the first step in determining the feasibility of predicting these worst-cases.In this paper, the effect of selected TO parameters on the resulting mechanical response and geometries of an AM pipe flange structure are investigated. Four different input parameters were chosen in the TO formulation: (1) penalty factor, (2) volume fraction, (3) element size, and (4) density threshold. Topology optimized designs were fabricated using PA2200 polyamide and the mechanical responses (reaction force, stress, and strain) were observed through experiments (universal testing machine and 3D Digital Image Correlation) and in silico environments (finite element analysis). In addition, 3D scanning and mass measurement were performed to inspect the geometric fidelity of the AM structures. A sensitivity analysis is performed to examine the effect of each TO parameters. The sensitivity analysis revealed mechanical responses can have non-monotonic and non-linear relationships between each tested parameter.

ACOUSTIC ABSORBERS MADE OF WOOD FIBER COMPOSITES DEVELOPED BY COMPRESSION MOLDING AND ADDITIVE MANUFACTURING

This research aims to address the noise pollution by developing an acoustic absorber made of polylactic acid (PLA)/polyhydroxyalkanoates (PHA)-wood fibers (PLA/PHA-WF) by compression molding (CM) and additive manufacturing (AM). Physical, mechanical, thermal, water absorption, and biodegradation properties of the developed acoustic absorbers by CM and AM were characterized and compared. Upon providing an air gap, thin absorbers developed by AM exhibit an increased and narrow acoustic peak than the CM absorbers because of theHelmholtz resonance effect due to the decreased density and increased porosity in the AM absorber. The results also show that the mechanical and thermal properties of theabsorbers developed by CM and AM were almost similar and absorber developed by AM shows anincreased rate of water absorption and biodegradation compared to absorber developed by CM due to the presence of porosity in the AM structure.

Additive manufacturing of recyclable, highly conductive, and structurally robust graphite structures

• Complex, electrically conductive structures with relatively high compressive strength. • Printable aqueous pastes made from graphite and cellulose nanocrystals. • Additively manufactured from sustainable and recyclable materials. • Structures made via direct ink writing (DIW) and dried at room temperature. • Minimal and controllable shrinkage of printed structures. This study investigates the design and use of a printable, sustainable, aqueous paste for room-temperature low-energy material extrusion (ME) additive manufacturing (AM) of complex structures. To this end, pastes with controlled rheology and a total solid content of ∼42% are formulated. Constituents of the pastes are commercial graphite and cellulose nanocrystal (CNC) powder, as a dispersing additive, with 91:09 and 88:12 graphite:cellulose wt.% compositions. The AM structures are dried in air at three rates (slow, medium, and fast). The structure of printed parts is characterized using electron microscopy, X-ray diffraction, Infrared/X-ray photoelectron spectroscopy, X-ray micro-computed tomography, and thermogravimetric analysis. The compressive strength of AM graphite structures reached 5.8±0.6 MPa with almost no effect from drying rates. However, samples containing more cellulose were ∼30% stronger in compression. Carbonization of the AM parts increased their electrical conductivity by more than an order of magnitude to ∼2400 S.m −1 . In addition, it enabled the fabrication of nearly pure graphite structures. The mechanical and electrical properties of samples fabricated in this study exceed the performance of previously reported AM graphite structures. Moreover, the recyclability of the printed parts was demonstrated by regenerating pastes via mixing printed parts in water and re-printing new parts with the paste. The AM graphite structures can be used in numerous applications, including but not limited to electrical discharge machining (EDM), electrochemical machining (ECM), high-temperature customized sealing, high-temperature composite tooling, and energy conversion and storage.

Ultrasonic Nondestructive Evaluation of Additively Manufactured Photopolymers

ABSTRACT Advanced applications of polymer additive manufacturing (AM) require knowledge of the material acoustic and complex elastic properties. Recently, ultrasound nondestructive evaluation (NDE) methods have been applied to the understanding of AM polymers manufactured with a variety of methods. Nonetheless, the available information is still limited to a few materials and frequency ranges, and knowledge of shear acoustic properties and complex elastic properties of AM polymers is lacking. In this study, ultrasound measurements of compressional and shear phase velocity and attenuation are used to experimentally determine the complex elastic properties of 14 AM photopolymers manufactured using PolyJet systems. The results provided here are expected to aid in design of advanced polymer AM structures, and highlight the value of ultrasound NDE for the characterization of AM polymers.

Sub-Second Prediction of the Heatmap of Powder-Beds in Additive Manufacturing Using Deep Encoder–Decoder Convolutional Neural Networks

Abstract Physical modeling of the transient temperature during the Selective Laser Sintering (SLS) Additive Manufacturing (AM) process is essential for the characterization of the quality and structural integrity of the final products. The conventional numerical models used to simulate the thermal field of Additively Manufactured structures (AM structures) are time-consuming and could not be directly used to develop a real-time simulation or a process control system. This paper presents a deep learning encoder–decoder Convolutional Neural Network (CNN) model to predict the thermal field of AM structures. For deep learning training purposes, a time-consuming physics-based simulation was used to create a dataset including thousands of two-dimensional (2D) position-time representations of the laser head with different process parameters and their corresponding heatmap of AM structures. The deep learning model developed based on this dataset is capable of sub-second prediction of the heatmap being more than 41,000 times faster than the physics-based model. The resulting sub-second computational time of the developed deep learning model allows real-time process simulation as well as provides a basis for developing a process control system for the AM process in the future.

Additive manufacturing: A machine learning model of process-structure-property linkages for machining behavior of Ti-6Al-4V

Prior studies in metal additive manufacturing (AM) of parts have shown that various AM methods and post-AM heat treatment result in distinctly different microstructure and machining behavior when compared with conventionally manufactured parts. There is a crucial knowledge gap in understanding this process-structure-property (PSP) linkage and its relationship to material behavior. In this study, the machinability of metallic Ti-6Al-4V AM parts was investigated to better understand this unique PSP linkage through a novel data science-based approach, specifically by developing and validating a new machine learning (ML) model for material characterization and material property, that is, machining behavior. Heterogeneous material structures of Ti-6Al-4V AM samples fabricated through laser powder bed fusion and electron beam powder bed fusion in two different build orientations and post-AM heat treatments were quantitatively characterized using scanning electron microscopy, electron backscattered diffraction, and residual stress measured through X-ray diffraction. The reduced dimensional representation of material characterization data through chord length distribution (CLD) functions, 2-point correlation functions, and principal component analysis was found to be accurate in quantifying the complexities of Ti-6Al-4V AM structures. Specific cutting energy was the response variable for the Taguchi-based experimentation using force dynamometer. A low-dimensional S-P linkage model was established to correlate material structures of metallic AM and machining properties through this novel ML model. It was found that the prediction accuracy of this new PSP linkage is extremely high (>99%, statistically significant at 95% confidence interval). Findings from this study can be seamlessly integrated with P-S models to identify AM processing conditions that will lead to desired material behaviors, such as machining behavior (this study), fatigue behavior, and corrosion resistance.

Tunable and Robust Nanostructuring for Multifunctional Metal Additively Manufactured Interfaces.

Novel processing phenomena coupled with various alloying materials used in metal additive manufacturing (AM) have opened opportunities for the development of previously unexplored micro-/nanostructures. A rationally devised structure nanofabrication strategy of AM surfaces that can tailor the interface morphology and chemistry has the potential for many applications. Here, through an understanding of grain formation mechanisms during AM, we develop a facile method for tuning micro-/nanostructures of one of the most used AM alloys and rationally optimize the morphology for applications requiring low surface adhesion. We demonstrate that optimized AM structures reduce the adhesion of impaling water droplets and significantly delay icing time. The structure can also be altered and optimized for antiflooding jumping-droplet condensation that exhibits significant enhancement in heat transfer performance in comparison to nanostructures formed on conventional Al alloys. In addition to demonstrating the potential of functionalized AM surfaces, this work also provides guidelines for surface-structuring optimization applicable to other AM metals.

Tailoring the alloy composition for wire arc additive manufacturing utilizing metal-cored wires in the cold metal transfer process

In the present study, the application and tailoring of the alloy composition of chromium martensitic hot-work steels using metal cored wires (MCW) for wire arc additive manufacturing (WAAM) in a modified short-circuit metal transfer process is demonstrated. The nickel content was varied and the alloys were fabricated as tubular-cored wires with various powder fillings. By recording the material transfer at high speed during processing, evidence was gathered indicating the suitability of the fabricated cored wires for WAAM. Optimized process parameters were identified by taking a Design of Experiment (DoE) approach and additive manufacturing (AM) structures were fabricated from the chromium martensitic hot-work tool steel alloys. The microstructure and mechanical properties of the parts were subsequently characterized. The phase fraction of the polygonally shaped delta ferrite could be reduced and microstructural refinement could be achieved by adding nickel to the investigated hot-work tool steel. In addition to molybdenum-enriched precipitates that covered the grain boundaries, randomly scattered non-metallic inclusions and oxides were observed. Modifying the microstructure by adding nickel also affects the mechanical properties of the product: an increase in hardness, impact toughness and yield strength as the nickel content increased in the AM structures fabricated by WAAM was observed.

Failure classification of porous additively manufactured parts using Deep Learning

Microstructural features are one of the most important factors that determine performance and failure of engineering structures. In metal parts produced through Additive Manufacturing (AM), pores formed during deposition are one such feature that can dominate material response. These pores can be a result of keyhole-mode melting, gas porosity contained within the powder particles prior to deposition, or poor process parameters. Predicting the mechanical reliability of as-built AM structures containing such pores is often challenging, even with modern computational methods. Here we show that Deep Learning can be used to predict performance based on pore structure in AM components. We used a three-dimensional convolutional neural network to predict peak load in simulated tensile specimen gauge sections with different realizations of porosity sampled from an experimentally-measured statistical distribution. The algorithm was also tested in two additional simulation scenarios: a modified specimen geometry and a different nominal stress state. The algorithm remained predictive in these scenarios, which were outside the bounds of its training data. Our results show that Deep Learning can effectively learn microstructural features and patterns that have important implications in part performance.

Understanding influence of micro pores on strengths of LMDed AlSi10Mg material using a direct method based statistical multiscale framework

For mechanical components composed of AlSi10Mg material and produced by laser melting deposition (LMD), micro pores introduced during the additive manufacturing (AM) process significantly deteriorates the material performance. In order to evaluate how characteristics such as the porosity level, the shape and the spatial distribution of pores influence the ultimate strength and endurance limit of the material, a direct method based statistical framework is presented in this paper. Within this framework, a random sampling strategy is developed to bridge heterogeneities associated with two sub-scales, namely the scale of micro pores and the scale of material constituents. Through statistical analyses of results calculated from many 2.5D and 3D representative volume element (RVE) samples, the study shows that micro pores have more severe impact on endurance limit than on ultimate strength: With an averaged porosity degree of 0.71%, the former case drops for more than 20.0% while the latter case is almost unchanged. Meanwhile, compared to shape, spatial distribution of pores has a more significant influence on the endurance limit. The analysis in this paper shows that the established approach can be a viable means for studying similar AM materials and guiding reliability design of AM structures.

Dynamic fracture of additively manufactured continuous-fibre composites under ballistic impact: experimental and numerical study

The ability to manufacture complex geometries with additive manufacturing (AM) has led to a significant increase in research in this field. Thermoplastic polymer-matrix composites are mostly fabricated using fused deposition modelling, and the addition of fibres demonstrated a considerable increase in strength and stiffness of resultant composites. Dynamic deformation and fracture of AM structures are investigated to optimise architectures for biomedical, industrial, and civil applications. This study focuses on the dynamic-fracture behaviour of AM continuous-carbon-fibre-reinforced composites with nylon polymer-matrix under ballistic impact. The mechanical behaviour of these composites exhibits brittle fracture under tension and ductile deformation under compression. Compared to short-carbon-fibre-reinforced AM composites with similar carbon content, the continuous-fibre composite had higher tensile and compressive moduli. Although the tensile strength was also found to be significantly higher than that of composites with short fibres, the elongation at break in tension and the compressive strength was lower. A ballistic test on a solid unidirectional plate was carried out using a 9 mm steel bullet at 100 – 120 m s−1. Complete perforation was achieved, with a circular hole at the front of the target and a delaminated damaged area at the back. The impact boundary conditions were replicated in finite-element simulations at 100 m s−1. The material's linear elastic behaviour was modelled based on in-house mechanical characterisation tests, and a VUMAT of Hashin damage model was used for the description of impact damage with cohesive-surface modelling of the delamination failure. The simulation showed a good agreement with the experimentally observed damaged areas at the front and the back of the plate, indicating that traditional composite-material and damage models can be used to predict the dynamic impact fracture of AM composites.

Real-time multiscale prediction of structural performance in material extrusion additive manufacturing

The material extrusion additive manufacturing (AM) has been extensively used in fabricating structures with complex geometries. However, geometric defects often exist in an AM structure, which could compromise its final performance. In this paper, a real-time multiscale performance evaluation method is developed for material extrusion-based honeycomb structures. The representative cell boundary is extracted from three-dimensional (3D) point clouds obtained via an in-situ monitoring approach. The cell boundary is then used to generate the digital twin of the unit cell of the printed layer based on the finite element (FE) method. A physics-based multiscale modeling approach called mechanics of structure genome (MSG) is then employed to predict the effective material properties of the printed layer and plate stiffness matrix of the final structure. The proposed approach provides a highly efficient way to predict the real-time performance of the as-manufactured products. Moreover, the numerical example shows that the geometric defects could result in complex mechanical behaviors in the defected parts, which cannot be captured by the conventional approaches based on the shape deviations. The numerical results are validated by the three-point bending tests. The proposed method can be used in the closed-loop control of material extrusion-based manufacturing systems.