Search

Metal additive manufacturing (AM) processes have transitioned from rapid prototyping applications to industrial adoption owing to their flexibility in product design, tooling, and process planning. Directed energy deposition (DED) is one of the most commonly used metal AM processes capable of producing large, high density parts, with a controlled microstructure. However, there are still ongoing challenges in maintaining a high level of reliability and repeatability when compared to conventional manufacturing processes. There is a need to define, identify and maintain regions of process stability in DED. In this study, a high-dynamic range camera and a physics-based model are used to monitor the melt pool, obtain process signatures, and predict deposition stability characteristics. The research efforts are focused on generating process maps to identify unstable process zones, with a reference to process physics, process signatures, and process outcomes using analytical modeling, in-situ melt pool monitoring, and ex-situ characterization, respectively. The goal is to classify the process signatures in pre-defined process zones (under-melt, conduction, keyhole, balling) to avoid instabilities, defects and anomalies using a low-cost high-dynamic range camera and kNN classifier, which has achieved 13% error rate. With this approach, decisions can be made to perform corrective actions (e.g. machining, re-manufacturing) or to scrap the manufactured part without ex-situ characterization.

Poor surface quality has long been a significant challenge to the broad adoption of additive manufacturing (AM) in industry. This work deploys a customized electropolishing (EP) procedure to improve the surface finish of complex laser powder bed fusion Ti-6Al-4V parts with internal channels and various geometries using a substantially less hazardous, anhydrous, alcoholic solution, while maintaining the geometrical fidelity of the parts. A preliminary study on simple geometries was run to evaluate the effect of EP time on surface roughness and thickness loss of a part. By using a customized test setup and an artificial EP waveform to accurately control EP parameters, a high EP efficiency was achieved (maximum reduction of surface roughness up to 70% and a minimum thickness loss of 11%) with low treatment times. Subsequently, EP was conducted on the internal surfaces of various geometries in increasing order of complexity; the results showed the effectiveness of the proposed technique across different geometrical features, leading to maximum uniform surface roughness improvement of 77% with minimum thickness loss of only 7%. The proposed methodology from this work for a fast and cost-effective surface enhancement methodology in an environmentally safe solution may be used on real, complex AM parts to enhance both internal and external surface quality.

Laser power bed fusion (LPBF) enables the possibility to improve the performance of critical automotive components by leveraging new design and manufacturing potentials. While the LPBF approach taps into numerous design freedom advantages, the finely focused energy input source, layer-wise thermal cycling, and rapid cooling rates also impact the properties of a given material, thereby affecting performance characteristics of the end-product. The microstructure and mechanical properties of LPBF components must hence be thoroughly compared with the traditional processing technique used for a given application to evaluate its feasibility. In the context of this work, AlSi10Mg processed via LPBF is compared to a high-pressure die-cast aluminum alloy to compare the performance toward technology adoption in manufacturing automotive transmissions. It was found that, with proper process control, LPBF parts can achieve better or comparable density of 99.84–99.95% (cast: 99.15–99.97% cast), similar surface topography, comparable hardness of 54.3–69.3 HRB (cast: 72.8–81.5 HRB), comparable specific wear rates of 3.92*10−4 to 6.04*10−4 mm3N−1m−1 (cast: 2.50*10−4 to 2.55*10−4 mm3N−1m−1), and an overall better corrosion resistance compared to the cast pump housing. The findings show that, with an appropriate selection of process parameters, it is feasible to pursue and possibly enhance the performance of AlSi10Mg for fluid power applications using LPBF.

Electron beam powder bed fusion can offer advantages in terms of powder reuse by virtue of larger layer thickness and flexible recoater, thus lowering the economic barrier by reducing waste. Systematic studies focused on quantifying the effect of reuse on powder performance metrics such as size, rheometry and flowability remain scarce. This work presents results on the influence of the process on Ti-6Al-4V powder, by comparing virgin powder to reused powders over two processing cycles, benchmarking the most sensitive powder characteristics. Particle size, morphology, flow properties and rheometry are compared. It was observed that morphology, size distribution, flowability and spreadability were degraded due to partial sintering and powder recovery.

Additively manufactured parts produced via laser powder bed fusion (LPBF) have limitations in their applications due to post-processing requirements caused by high surface roughness. The characteristics of side-skin surfaces are generally assumed to be dominated by adhered powder particles. This work aims to analyze and interpret the effects of LPBF processing parameters on side-skin surfaces. As such, this work has two sections to investigate the effect of (i) core and (ii) border LPBF parameters on side-skin surface roughness for Ti–6Al–4V. The findings show that there is a robust correlation between both core and border LPBF parameters on side-skin surface morphologies. In terms of core LPBF parameters, an interaction between laser power and beam velocity is shown to influence side-skin surface roughness, resulting in Sa values in the range of 11–26 µm. Additionally, a preliminary investigation into the effect of melting mode phenomena at the border leads to a possibility of obtaining Sa values of

The data included in this article provided additional supporting information on our recent publication (McGregor et al. [1] ) on the review of the natural lattice architecture in human bone and its implication towards titanium (Ti) lattice design for laser powder bed fusion and electron beam powder bed fusion. For this work, X-ray computed tomography was deployed to understand and visualize a Ti-6Al-4V lattice structure manufactured by laser powder bed fusion. This manuscript includes details about the manufacturing of the lattice structure using laser powder bed fusion and computed tomography methods used for analyzing the lattice structure. Additionally, a comprehensive literature review was conducted to understand how lattice parameters are controlled in additively manufactured Ti and Ti-alloy parts aimed at replacing or augmenting bone. From this literature review, lattice design information was collected and is summarized in tabular form in this manuscript.

LPBF of Al alloys is associated with numerous challenges when compared to other commonly used alloys due to their higher reflectivity and thermal conductivity. In this work, processing diagrams, temperature prediction models, XCT, and metallography are used for establishing criteria in process parameter optimization of high reflectivity Al alloys based on AlSi$_{10}$Mg response in using 57 different process parameter combinations - 21 using a focused Gaussian laser beam and 36 using divergent beams. For LPBF systems with focused beam diameters 99.98%. Additionally, an analytical model-guided selection of laser power and velocity settings for a focused beam help in stabilizing melt pool and spatter dynamics in the transition melting mode thereby enabling a potential to obtain density values close to conduction mode densities (~99.98%). A dimensionless keyhole number (Ke) was derived in this work to identify distinct regions of conduction (Ke of 0-12), transition (Ke of 12-20), and keyhole (Ke > 20) mode melting during LPBF of AlSi$_{10}$. A melt pool aspect ratio (ratio of melt pool depth to width) of ~0.4 is observed to be the threshold between conduction and transition/keyhole mode melt pools for AlSi$_{10}$, different from the conventionally assumed 0.5. Lastly, inferred laser absorptivity values (from experimental melt pools) of transition/keyhole mode melt pools are observed to be >40% higher when compared to conduction mode melt pools. This work demonstrates a dimensionless-process map method to obtain near fully dense parts that can be generalized for LPBF of high reflectivity alloys.

This study focuses on developing and demonstrating a straightforward workflow for identifying pathways to increase green part density in binder jetting additive manufacturing (BJAM) using statistically driven process maps. The workflow was applied to investigate the effects of process parameters toward improving green part density, with a direct application in manufacturing of Fe-Si components. Specifically, a half-factorial experimental design was used to study the effects of four key parameters—layer thickness, powder spreading speed, roller rotational speed, and binder saturation—on Fe-Si spherical powder with D50 of 32.40 µm. Relative bulk density was estimated via three methods: geometrical and mass measurements, the Archimedes test, and CT imaging. The study discusses relative bulk density as well as localized density variation in the printed parts, which is attributed to both parameter selection and inherent process variability. A regression analysis was used to reveal the significance of main effects and second-order interactions. The regression model (R2 = 0.915) was used to derive an expression for green density as a function of the parameters and had a prediction error of 0.96%. Based on the regression model, an optimized set of parameters was obtained that would maximize green density up to 57.96% for the machine and material system.

Additive manufacturing (AM) of titanium (Ti) and Ti-6Al-4V lattices has been proposed for bone implants and augmentation devices. Ti and Ti-6Al-4V have favourable biocompatibility, corrosion resistance and fatigue strength for bone applications; yet, the optimal parameters for Ti-6Al-4V lattice designs corresponding to the natural micro- and meso-scale architecture of human trabecular and cortical bone are not well understood. A comprehensive review was completed to compare the natural lattice architecture properties in human bone to Ti and Ti-6Al-4V lattice structures for bone replacement and repair. Ti and Ti-6Al-4V lattice porosity has varied from 15% to 97% with most studies reporting a porosity between 50% and 70%. Cortical bone is roughly 5–15% porous and lattices with 50–70% porosity are able to achieve comparable stiffness, compressive strength, and yield strength. Trabecular bone has a reported porosity range from 70% to 90%, with trabecular thickness varying from 120 to 200 μm. Existing powder bed fusion technologies have produced strut and wall thicknesses ranging from 200 to 1669 μm. This suggests limited overlap between current AM of Ti and Ti-6Al-4V lattice structures and trabecular bone architecture, indicating that replicating natural trabecular bone parameters with latticing is prohibitively challenging. This review contributes to the body of knowledge by identifying the correspondence of Ti and Ti-6Al-4V lattices to the natural parameters of bone microarchitectures, and provides further guidance on the design and AM recommendations towards addressing recognized performance gaps with powder bed fusion technologies.

Achieving defect-free parts is traditionally challenging in laser powder bed fusion (LPBF). The mechanical properties of additively manufactured parts are highly affected by their density; as such, research in defect detection and pore prediction has gained significant interest. The process parameters, the powder characteristics, and the process environment conditions play an important role in defect occurrence. Moreover, the laser scan path affects density, especially at scan path discontinuities. In this work, the complex interaction between the process parameters and the scan path on the occurrence of subsurface pores is investigated. In the data preparation step, a synthetic data set is generated to model the melt pool morphology along the scan path. A secondary data set containing the pore space of the resulting parts is obtained via X-ray computed tomography (CT) and is registered with the synthetic data set. Machine learning models, namely, a Conditional Variational AutoEncoder (CVAE) and a Convolutional Neural Network (CNN), are then trained based on the input features to predict pore occurrence. The performance evaluation of both CNN and CVAE models on synthetic data indicates that the scan path and process parameters can be utilized in predicting pore locations. Quantitative results show that employing offline CT images a priori in training the CVAE, without the need to have CT information in the test phase, leads the CVAE model to superior performance over the CNN.

Additive manufacturing (AM) enables freedom of design, part complexity, and customization with minimal added cost, by fusing materials layer upon layer. AM, in general, is considered to have great potential in complementing conventional manufacturing methods. Functional parts with high strength to weight ratio generated using structural topology optimization can be eventually realized by AM. Limitations of AM parts related to surface finish and dimensional accuracy can be overcome by post-machining of critical features and surfaces in order to achieve a specific tolerance and surface quality. To minimize the trial and error efforts, accurate AM and post-machining simulations are essential for effective planning of the synergized processes. The goal of this study is to propose a process workflow which can be used as a guideline for successful production of complex parts manufactured via laser powder bed fusion (LPBF) and post-processed via CNC (computer numerical control) machining. The workflow is deployed and iterated through a case study of the manufacturing of a surgical navigation tracker, where the holistic manufacturing process involves a digital design utilizing structural topology optimization, AM part geometric distortion simulation, machining process planning, fabrication, and validation.

A path to lowering the economic barrier associated with the high cost of metal additively manufactured components is to reduce the waste via powder reuse (powder cycled back into the process) and recycling (powder chemically, physically, or thermally processed to recover the original properties) strategies. In electron beam powder bed fusion, there is a possibility of reusing 95 - 98% of the powder that is not melted. However, there is a lack of systematic studies focusing on quantifying the variation of powder properties induced by number of reuse cycles. This work compares the influence of multiple reuse cycles, as well as powder blends created from reused powder, on various powder characteristics such as the morphology, size distribution, flow properties, packing properties and chemical composition (oxygen and nitrogen content). It was found that there is an increase in measured response in powder size distribution, tapped density, Hausner ratio, Carr index, basic flow energy and specific energy, dynamic angle of repose, oxygen, and nitrogen content, while the bulk density remained largely unchanged., Comment: 24 pages, 8 figures, 1 graphical abstract

The master sinter curve (MSC) is an empirical model used to predict the density of a part after being sintered. The model is typically applied to components that undergo isotropic shrinkage. Parts manufactured using binder jetting additive manufacturing (BJAM) are known to have nonuniform powder systems and high levels of anisotropy. This work explores the application of the master sinter curve to components made by BJAM. Cylindrical samples were manufactured with the long axis parallel (vertical), perpendicular (horizontal), and 45 deg to the printing direction. A bimodal blend of titanium powder (0–45 µm and 106–150 µm) was used to make samples with consistent green densities (ranging from 47.2% to 52.3%) between the different orientations. Samples were then sintered at heating rates of 1, 3, and 5 °C/min to a maximum of 1400 °C. After sintering, the samples showed significant variation between the different orientations, with vertical samples on average 7.6 ± 2.98% and 4.7 ± 1.20% denser than the horizontal and the 45 deg samples, respectively. The calculated apparent activation energies for sintering were within the same range for all orientations, 200–260 kJ/mol for vertical and 45 deg, and 140–260 kJ/mol for horizontal samples. Validation sinter runs showed that the density prediction errors of the master sinter curves were between 0.9% and 4.3%. This work shows that the master sinter curve can be applied to predict the sintered density of components manufactured by binder jetting additive manufacturing.

Binder jetting additive manufacturing (BJAM) is a comparatively low-cost process that enables manufacturing of complex and customizable metal parts. This process is applied to low-cost water-atomized iron powder with the goal of understanding the effects of printing parameters and sintering schedule on maximizing the green and sintered densities of manufactured samples, respectively. The powder is characterized by using scanning electron microscopy (SEM) and particle size analysis (Camsizer X2). In the AM process, the effects of powder compaction, layer thickness, and liquid binder level on green part density are investigated. Post-process heat treatment is applied to selected samples, and suitable debinding parameters are studied by using thermo-gravimetric analysis (TGA). Sintering at various temperatures and durations results in densities of up to 91.3%. Image processing of x-ray computed tomography (μCT) scans of the samples reveals that porosity distribution is affected by powder spreading, and gradients in pore distribution in the sample are largely reduced after sintering. The resulting shrinkage ranges between 6.7 ± 3.0% and 25.3 ± 2.8%, while surface roughness ranges between 11.6 ± 5.0 μm and 32.1 ± 3.4 μm. The results indicate that the sintering temperature and time might be tailored to achieve target densities anywhere in the range of 64% and 91%, with possibly higher densities by increasing sintering time.

To facilitate functional part production in metal binder jetting additive manufacturing, the relationship between materials, process and sintering needs to be understood. This work relates sintering theory with process outcomes. For this, commercially pure titanium was deployed to study the effect of powder size distributions on green and sintered part qualities (bulk density, relative density, particle size, pore size, sinter neck size). The powders were uni- and bi-modal blends of 0–45 μm, 45–106 μm, and 106–150 μm. Computed tomography analysis was used to evaluate non-densifying (1000 °C) and densifying (1400 °C) sintering regimes. For green parts, the relative density and powder size distribution along the build direction followed a periodic fluctuation equivalent to the 150 μm layer thickness. The relative density fluctuation range was higher (±20%) for bi-modal blends with 0–45 μm, compared to all other blends (±8%) due to powder segregation. For non-densifying sintering, parts with 0–45 μm blends displayed both densifying and non-densifying behavior. For densifying sintering, powders containing 0–45 μm blends surpassed the 70% density threshold expected for this sintering regime. Overall, the finer particles improved bulk density of sintered parts, at the expense of higher levels of shrinkage and density anisotropy along the build direction. Keywords: Additive manufacturing, Binder jetting, Computed tomography, Commercially pure titanium powder blends, Sinter structure analysis, Particle distribution

The adoption of metal binder jetting additive manufacturing (AM) for functional parts relies on a deep understanding between the materials, the design aspects, the additive manufacturing process and sintering. This work focuses on the relationship between sintering theory and process outcomes. The data included in this article provides additional supporting information on the authors’ recent publication (Wheat et al., 2018 [1]) on the sinter structure analysis of commercially pure titanium parts manufactured using powder bed binder jetting additive manufacturing. For this work, commercially pure titanium was deployed to study the effect of powder size distributions on green and sintered part qualities (bulk density, relative density, particle size, pore size, sinter neck size). This manuscript includes the overall computed tomography visualization methods and results for the green and sintered samples using uni- and bi-modal powders. Moreover, the effective particle and pore size for the different batches of powder are presented.

Experimental studies in the literature have identified the powder-bed compaction density as an important parameter, governing the quality of additively manufactured parts. For example, in laser powder-bed fusion (LPBF), the powder-bed compaction density directly affects the effective powder thermal conductivity and consequently the temperature distribution in melt pool. In this study, this physical parameter in a LPBF build compartment is measured using a new methodology. A UV curable polymer is used to bind powder-bed particles at various locations on the powder-bed compartment when Hastelloy X was used. The samples are then scanned using a nano-computing tomography (CT) system at high resolution to obtain an estimation of the relative powder-bed compaction density. It is concluded that due to the interaction between the recoater and the variation in the powder volume accumulated ahead of the recoater across the build compartment, the relative powder-bed compaction density decreases along the recoater moving direction (from 66.4% to 52.4%.). This variation in the powder-bed compaction density affects the density and surface roughness of the final printed parts that is also investigated. Results show that the part density and surface quality decrease ~0.25% and ~20%, respectively, along the build bed in direction of the recoater motion. Keywords: Additive manufacturing, 3D printing, Compaction density, Selective laser melting, Laser powder-bed fusion, Hastelloy X

Spherical powders are almost exclusively deployed for metal laser powder bed fusion (LPBF) additive manufacturing (AM). In this work, low-cost irregular powder feedstock is studied for its potential in three key areas to meet minimum AM process requirements, namely: (1) hopper flow, (2) layer spreading, and (3) de-powdering. Irregular water-atomized Iron powder was used as the study population, while spherical plasma-atomized Inconel 625 powder was used as the control. Powder flow characteristics were obtained using a FT4 Powder Rheometer (Freeman Technology). Layer spreading was evaluated indirectly via powder bed density measurements. Measurements were quantified by using printed artifacts for captive powder and evaluated using isopropanol infiltration and three-dimensional computed tomography (CT) imaging (Zeiss Xradia 520 Versa). Powder clearing from fine channels was quantified through vision-based measurements of printed artifacts (Dino-Lite DinoCapture 2.0). The results from this work demonstrated that: (1) A larger opening and steeper hopper angle are necessary to maintain a mass flow regime with the irregular powder. (2) Powder bed density is similarly consistent across the bed indicating adequate spreadability. (3) Water-atomized Iron powder has better de-powdering characteristics in the smallest cleared 0.6 mm diameter features, likely due to its 15% lower bed density.

The data included in this article provides additional supporting information on our recent publication (Liravi et al., 2018 [1]) on a novel hybrid additive manufacturing (AM) method for fabrication of three-dimensional (3D) structures from silicone powder. A design of experiments (DoE) study has been carried out to optimize the geometrical fidelity of AM-made parts. This manuscript includes the details of a multi-level factorial DOE and the response optimization results. The variation in the temperature of powder-bed when exposed to heat is plotted as well. Furthermore, the effect of blending ratio of two parts of silicone binder on its curing speed was investigated by conducting DSC tests on a silicone binder with 100:2 precursor to curing agent ratio. The hardness of parts fabricated with non-optimum printing conditions are included and compared.

The feasibility of a hybrid additive manufacturing (AM) method combining material extrusion and powder bed binder jetting (PBBJ) techniques for fabrication of structures made of silicone (polysiloxane) is investigated in this paper. A full factorial experimental design was conducted to maximize the geometrical accuracy of the parts. The rheological and morphological properties of the silicone powders, the thermal characteristics of the liquid silicone binder, and mechanical characterization the additively manufactured parts are reported. Using this hybrid AM method, porous cylindrical structures (5 mm diameter (D) × 3 mm height (H)) with potential applications in biomedical industry were additively manufactured. The final structures are composed of ∼60% silicone powder, ∼ 30% silicone binder, and

The acquisition of large amounts of high-dimensional data is becoming prevalent in additive manufacturing, arising from sensor integration in such processes. Although supervised machine learning has gained popularity to predict process quality from such data, annotation can be time-consuming and labour intensive. Active learning is a sub-field within machine learning concerned with maximizing model performance using the least amount of annotated data. The focus of this study is twofold. Firstly, a novel active learning method is introduced, called adaptive weighted uncertainty sampling (AWUS) which balances uncertainty with random sampling using the model change between active learning iterations. This way, AWUS is able to adapt from exploration of the instance space to exploitation of model knowledge throughout the active learning process. Secondly, a novel feature extraction and classification method is proposed for directed-energy-deposition additive manufacturing processes which predicts image and process quality based upon the visibility of the melt-pool using the visual signature of occluding features in the image. AWUS is compared against 6 state-of-the-art query strategies on 28 datasets from the OpenML database and 8 in-situ machine vision recordings of directed-energy-deposition processes using 4 different classifiers. The results show that AWUS outperforms the state-of-the-art across the board of the datasets, classifiers and active learning batch sizes. Furthermore, the application of AWUS reduces the amount of necessary annotations by 20 to 70% in 90% of our experiments compared to random sampling.

Tensile testing is often proposed as the preferred methodology to qualify builds and materials produced through additive manufacturing. While there is already work demonstrating the difference in measured properties between tensile specimens produced in different build orientations, this does not extend to different specimen geometries. In addition, the body of knowledge in this domain is typically made up of studies that utilize custom combinations of specimen geometries, part finishing, and post-processing, making it challenging to compare results. To study the impact of geometry of tensile specimens on tensile testing results, a selection of standard specimen types provided in ASTM E8/E8M was prepared in Ti-6Al-4V using an electron beam powder bed fusion additive manufacturing machine. These specimens were characterized to observe any porosity defects, dimensional deviations, and surface topography that could impact performance. It was found that changes in specimen geometry, specimen size, build orientation, and the internal porous defects; have significant effects on the tensile properties of the specimens. The horizontally built specimens had higher yield and tensile strengths, but lower elongation compared to vertically built specimens. With an increase in cross-sectional area, an increase in the yield, tensile strength, and elastic modulus was observed. With an increase in surface area to volume ratio, there was a decrease in the yield and tensile strength. The average solid fraction of the specimens had no influence on any measured tensile properties. Furthermore, with an increase in maximum pore size, the elongation of the specimen decreased.

Master sinter curves (MSC) are empirically derived models used to predict bulk densification behavior of a powder-compact exposed to heat in a controlled nitrogen-hydrogen atmosphere. In this study, solid-state sintering MSC models were developed for two types of AISI 4340 powders: water-atomized and gas-atomized, which were deployed in binder jetting additive manufacturing. The models were constructed based on shrinkage measurements obtained through push-rod dilatometry. The results showed that the bulk densification behavior of the water-atomized parts was generally similar to the gas-atomized, indicating its relevance in BJAM when it comes to sintering. Some of the differences in bulk densification were attributed to the powder organization in the green state. The water-atomized powder was found to have a larger activation energy value (341 kJ/mol) than that of the gas-atomized (312 kJ/mol) and exhibited a slightly slower densification rate. The models were validated through independent sintering trials, resulting in model prediction accuracies of 91.2% and 99.4% for the water- and gas-atomized powders respectively. The starting green densities in the validation trials were 49% and 50% respectively, with final density targets of 70–73%. Examination of validation samples from both powders revealed similar particle necking behavior. Further optimization of the powder spreading for WA powders may maximize the powder packing and further improve the densification rate.

Over the past two decades, additive manufacturing has opened a new window of opportunities in fabricating complex porous matrix structures such as cellular solids. Several factors including design, material and process parameters can selectively be varied to tailor the porous properties of products based on the intended application. This article addresses the effect of variable throughout layer thickness configuration in the binder-jet additive manufacturing of titanium structures for orthopedic applications. Two layer thicknesses of 80 and 150 μm are selectively controlled inside of each titanium sample with four different configurations. Several studies were performed, including shrinkage analysis, porosity measurements, and mechanical compression tests to quantify the effect of layer thickness on part quality and mechanical properties. The results of the porosity measurement revealed that there is about 5% variation among the samples with different layer thickness configuration. Bulk porosity values obtained from micro computed tomography (μCT) scan data placed the bulk porosity of the samples combining more than one layer thickness, in between of the results for control specimens, which were manufactured by applying a single layer thickness throughout the samples. Mechanical properties did not show any significant variation, which is attributed to the low range of the porosity deviation (less than 5%). The highest Young’s modulus of 3.50 ± 0.4 GPa and yield stress of 175 ± 25 MPa were obtained from analysis of the data achieved from the compression test.

The paradigm shift towards digital manufacturing [1] is seen in additive manufacturing (AM) corporate annual industry growth at 17.4% increase in 2016 to $6.063 billion, with 11% attributed to the biomedical sector [2]. The adoption of AM in healthcare has boomed since 2010 by a 3200% increase in hospitals with AM facilities [2]. Forecasts predict that AM will become common, with a disruptive effect on supply chains [2]. For high-value custom surgical instruments and implants, the metal AM design-to-fabrication lifecycle is performed via costly empirical approaches, hindering widespread technology acceptance [3]; thus, metal AM technologies have not yet reached their full potential in the healthcare industry. With the rapid growth of AM in the field of medical [4], several technologies are becoming popular in producing medical implants. Such technologies need to be distinguished precisely with respect to their capabilities to produce a specific implant. Customization of implants’ complex manufacturing approaches, and is nowadays possible by AM. Therefore, the rising demand for the use of AM technologies for the medical implant production shows its importance for different stakeholders in the healthcare industry and is expected to be continued in the future.

Cellular structures (CSs) have been used extensively in recent years, as they offer a unique range of design freedoms. They can be deployed to create parts that can be lightweight by introducing controlled porous features, while still retaining or improving their mechanical, thermal, or even vibrational properties. Recent advancements in additive manufacturing (AM) technologies have helped to increase the feasibility and adoption of cellular structures. The layer-by-layer manufacturing approach offered by AM is ideal for fabricating CSs, with the cost of such parts being largely independent of complexity. There is a growing body of literature concerning CSs made via AM; this presents an opportunity to review the state-of-the-art in this domain and to showcase opportunities in design and manufacturing. This review will propose a novel way of classifying cellular structures by isolating their Geometrical Degrees of Freedom (GDoFs) and will explore the recent innovations in additively manufactured CSs. Based on the present work, the design inputs that are common in CSs generation will be highlighted. Furthermore, the work explores examples of how design inputs have been used to drive the design domain through various case studies. Finally, the review will highlight the manufacturability limitations of CSs in AM.

This article addresses the effects of glycerol (GLY) concentrations on the mechanical properties of calcium polyphosphate (CPP) bone substitute structures manufactured using binder jetting additive manufacturing. To achieve this goal, nine types of water-based binder solutions were prepared with 10, 12.5, and 15 wt % GLY liquid-binding agent, mixed, respectively, with 0, 0.75, and 1.5 wt % ethylene glycol diacetate (EGD) flow enhancer. The print quality of each of the solutions was established quantitatively using an image processing algorithm. The print quality analysis narrowed down the solutions to three batches containing 1.5 wt % EGD and variable amount of GLY. These solutions were used to manufacture porous CPP bone substitute samples, which were characterized physically to determine shrinkage, porosity, microstructure, and compression strength. The 12.5 wt % GLY, 1.5 wt % EGD solution resulted in the highest mechanical strength after sintering (34.6 ± 5.8 MPa), illustrating similar mechanical properties when compared to previous studies (33.9 ± 6.3 MPa) of additively manufactured CPP bone substitutes using a commercially available binder. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 828-835, 2017.

In-situ vision data acquisition, feature extraction, and analysis are ongoing challenges for quality assessment in directed energy deposition (DED). This work proposes a method for detecting target regions in the laser-material interaction zone based on a low-cost high-dynamic-range (HDR) vision sensor. Adaptive image thresholding, connected component analysis, and iterative energy minimization are used to identify target regions. The method is designed to be adaptive, in terms of obtaining parameters based on simple training data, and robust, in terms of feature detection performance subject to under-melt, conduction and keyhole melting mode phenomena. The performance of the proposed region detection scheme is quantitatively and qualitatively evaluated against annotated data. It was found that the True Positive Rate in detection was above 90%, while the False Detection Rate was less than 10%. Extensive experimental results show that the proposed scheme is able to detect and follow target regions under a variety of power levels and process conditions.

Directed energy deposition (DED) is a metal additive manufacturing process, where dimensional accuracy and repeatability are traditionally challenging to achieve. Strategies for computationally inexpensive process modelling and fast-response process controls of the laser deposition process are necessary to keep the geometric features close to the required dimensional tolerances. The deposition geometry depends highly on the complex local laser-material interaction and global thermal history of the substrate. In order to control the deposition geometry, an accurate and computationally inexpensive discretized state space thermal history model coupled with an analytical deposition geometry model is developed in this work. The model accounts for the local laser-material interaction using the mass and energy equilibrium equations coupled in a lumped parameter solution, as well as the global thermal history of the product using a state space thermomechanical discretization. In literature, studies have only focused on 1D toolpaths with constant process parameters such as speed, powder feedrate, and laser power. As it is possible to achieve highly complex geometric shapes with additive manufacturing, it is important to have models compatible with 2D/3D complex toolpaths. In this paper, an analytical thermomechanical model and a coupled deposition geometry model for DED process are presented and experimentally validated. As such, the thermal history of the deposited part is predicted throughout the process and the geometric features are predicted for 2D toolpaths.

Depending on processing conditions, laser powder bed fusion (LPBF) is known to have two operational regimes – conduction mode and keyhole mode. Heat conduction is the dominant heat transfer mechanism for conduction mode melting, whereas heat convection is the dominant heat transfer mechanism for keyhole mode melting. In addition, there exists a transition mode, which lies between the conduction and keyhole mode, wherein the dominance of conduction or convection depends upon the processing conditions. In this work, normalized processing diagrams are obtained to visualize the three melting modes - conduction mode, transition mode, and keyhole mode. The normalized processing diagrams obtained from this work are shown to be independent of material for specific classes of materials, of LPBF system, of laser modulation, and of powder layer thickness. Additionally, an analytical model is proposed to robustly predict the threshold between the three melting modes for two different classes of materials, (i) materials with low reflectivity and low thermal conductivity such as titanium, ferrous, and nickel alloys, and (ii) materials with high reflectivity and high thermal conductivity such as aluminium alloys. The normalized processing diagrams, alongside the identified melting mode thresholds, can provide a useful tool in diagnosing the origins of porous defects and enable accelerated process optimization efforts towards tailoring material properties in LPBF.

In this work, the tensile behavior of Inconel 625 parts fabricated via laser powder bed fusion (LPBF) at different laser power levels is examined, and correlated to bulk porosity as well as pore characteristics such as pore size, aspect ratio morphology, and polar orientation extracted from X-ray computed tomography (CT). Scanning electron microscopy (SEM) is employed to identify the fracture mode and origin of failure in the pulled samples. Microstructural examination on the as-built samples showed that increasing the laser power resulted in the transition of melting mode, from lack of fusion to keyhole, with an increase in part bulk density from 98.86% to 99.29%, respectively. It was found that the general bulk porosity level does not correlate directly with the Ultimate Tensile Strength (ranging between 780–820 MPa) and strain to fracture (ranging between 0.2–0.39) behavior of the parts. Detailed pore space characteristics obtained from CT datasets before and after the tensile test contributed to establishing a relationship between defects size, morphology, orientation and tensile properties of the samples. In general, it was found that strain to failure is directly influenced by pore space characteristics, while tensile strength is influenced by a combination of pore space and microstructural characteristics. This study also identified that there are systematic bias effects in the LPBF process, likely introduced by the combination of nuisance variables such as powder layer spreading and gas flow.

Additive manufacturing (AM) is a promising approach for fabricating structures to serve as bone substitutes, or as biomaterial components in biphasic implants for repair of osteochondral defects. In this study, the three dimensional printing (3DP) AM process was investigated to determine the effect of powder layer orientation on mechanical and structural properties of fabricated parts. Five types of standard cylindrical parts were manufactured via AM with 0°, 30°, 45°, 60° and 90° stacking layer orientations relative to the vertical z -axis of the print bed, using amorphous calcium polyphosphate (CPP) powder of irregular particle shape, average aspect ratio ≈1.70 and particle size between 75 and 150 μm. It was concluded that layer orientation had an effect on porosity and compressive strength, based on induced powder particle orientation in the green part during powder layering. The resulting bulk porosity values ranged between 30.0 ± 2.4% and 38.2 ± 2.7%, while the compressive strength ranged between 13.50 ± 1.95 MPa and 45.13 ± 6.82 MPa. The orientation with the highest compressive strength was 90°, while orientations with the weakest compressive strength were 0° and 45°. Based on these results, it was established that AM-made parts are structurally and mechanically anisotropic. The stacking layer orientation which results in the highest strength performance along a preferred loading orientation can be implemented to further optimize mechanical strength of constructs along the maximum loading direction.

An additive manufacturing platform was developed for fabricating constructs with heterogeneous properties. The liquid binder amount was investigated in calcium polyphosphate bone substitutes. Two part categories were produced with 150 and 190 μm layer thickness and 70, 80, 90, 100, and 90/70% gray scale. It was shown that the gray scale level had an effect on samples with 150 μm layer thickness where porosity ranged between 43 ± 1% and 49 ± 2% and the compressive strength varied between 4.8 ± 1.3 MPa and 15.5 ± 1.9 MPa. The results suggest that the binder gray scale level can be used to locally predict porous properties.

This work addresses the characterization of a UV-based micro-syringe deposition (μSD) system utilized in the micro-dispensing of photopolymers on particulate ceramic substrates. This methodology is used in embedding functionally graded and interconnected micro-features within constructs produced by a novel combined powder-based additive manufacturing (AM) and UV-based micro-syringe deposition (μSD) technique. The process is experimentally characterized using SEM and optical microscopy to study the effect of a wide range of process parameters on the geometrical quality of deposited tracks. Experimental data show that the system can produce features ranging from 200 to 575 μm in width and from 20 to 200 μm in height on particulate ceramic surfaces. To gain insight into the proposed micro-deposition process, a two-tier model is also developed. The first framework describes an analytical model for predicting the flow rate of the dispensed photopolymer fluid based on the piston displacement. The second model is a stochastic framework for predicting the line width of the features deposited on the substrate using a Monte Carlo probabilistic simulation to compensate for uncertainty in the system input parameters. A comparison between experimental and modeling line width predictions shows that the modeling results are 14–38% higher than the experimental results, depending on the system input variables. The proposed model is enhanced by introducing adjustment factors to compensate for UV exposure delay, fluid migration, and imbibition.

This article presents a novel rapid layered manufacturing approach based on a combined additive manufacturing (AM) process and a UV-based micro-syringe deposition (μSD) technique to be used in the fabrication of bio-ceramic structures with controlled micro-sized channels for bone and osteochondral tissue regeneration. In the proposed rapid manufacturing method, micro-scale sacrificial photopolymer networks are integrated within the manufactured part by depositing the photopolymer on selected bio-ceramic powder layers using an injection system. This AM–μSD method along with a post-processing protocol can potentially overcome current limitations of traditional powder-based AM approaches that are restricted in terms of complexity of internal architecture and feature size. For bone or osteochondral repair applications, the material system composed of the bio-ceramic and sacrificial photopolymer, along with the post-processing protocol, must ensure that the final implants are free from manufacturing residuals that could trigger an immune response post-implantation. In this study, calcium polyphosphate bio-ceramic was used as the substrate material based on prior art, polyvinyl alcohol solution was used as the powder binding agent, and ethoxylated (10 bisphenol A diacrylate) photopolymer solution was used as the sacrificial photopolymer element. Material characterization suggests that the proposed material system along with heat treatment protocol is suitable for the targeted applications where micro-scale channels within the implant are produced by AM–μSD.