Context Of Additive Manufacturing Research Articles

A thermal model for topology optimization in additive manufacturing: Design of support structures and geometry orientation

In this study, we present an efficient Topology Optimization (TO) approach designed to optimize support structures in metal Additive Manufacturing (AM), with a particular focus on Powder Bed Fusion (PBF) technology. The developed framework uses a purely thermal formulation to identify regions within the design that are susceptible to high heat concentrations. In the proposed modeling of the AM process, we postulate that new material layers are included in a partially built design that has already cooled to a controlled temperature. This aspect provides a layer-by-layer model of AM entirely local, enabling the building process parallelization and resulting in an algorithm with superior computational efficiency. Numerical results show the robustness of the proposed strategy, with the successful incorporation of support structures beneath overhanging surfaces and their effectiveness across a wide range of geometries and orientations. Furthermore, this framework is also applied to optimize the geometry orientation within the build chamber, further enhancing its applicability in the AM context.

Optimizing the Thickness of Functionally Graded Lattice Structures for High-Performance Energy Absorption: A Case Study Based on a Bicycle Helmet

This study explores the complete production chain of designing, optimizing, and Additive Manufacturing (AM) of a helmet incorporating a functionally graded lattice structure (FGLS). The potential of FGLSs in impact energy absorption tasks is investigated, along with the demonstration of a novel lattice optimization approach. Fifteen conformal, strut-based lattices are implemented in a realistic mountain bike helmet geometry and simulated in a standardized impact scenario in accordance with EN 1078. One model is subjected to the optimization procedure, produced, and physically tested. The study addresses limitations in prior research, emphasizing manufacturability in an AM context, lattice type exploration, the comparability of different unit cell types, and numerical modeling choices. The findings provide insights into the performance of lattice structures during impact, emphasizing practical engineering aspects such as design choices, optimization approaches, and manufacturing constraints.

AltPrint: new filling and slicing process planning based on deposited material with geometry variation

PurposeThis paper aims to address the development and implementation of “AltPrint,” a slicing algorithm based on a new filling process planning from a variation in the deposited material geometry. AltPrint enables changes in the extruded material flow toward local variations in stiffness. The technical feasibility evaluation was conducted experimentally by fused filament fabrication (FFF) process of snap-fit subjected to a mechanical cyclical test.Design/methodology/approachThe methodology is based on the estimation of the parameter E from the mathematical relationships among the variation of the material in the material flow, nozzle geometry and extrusion parameters. Calibration, validation and analysis of the printed specimens were divided into two moments, of which the first refers to the material responses (flexural and dynamic mechanical analysis) and the second involves the analysis of the printed components with localized flow properties (for estimating the response to cyclic loading). Finite element analysis assisted in the comparison of two snap-fit geometries, one traditional and one generated by AltPrint. Finally, three examples of compliant mechanisms were developed to demonstrate the potential of the algorithm in the generation of functional prototypes.FindingsThe contribution of AltPrint is the variable fill width integrated with the slicing software that varies the print parameters in different regions of the object. The alternative extrusion method based on material rate variation was conceived as an “open software” available in GitHub platform, hence, open manufacturing with initial focus on desktop 3D printer based on FFF. The slicing method provides deposited variable-width segments in an organized and replicable filling strategy, resulting in mechanical properties variations in specific regions of a part. It was implemented and evaluated experimentally and indicated potential applications in parts manufactured by the additive process based on extrusion, which requires local flexibilities.Originality/valueThis paper presents a new alternative method for application in an open additive manufacturing context, specifically for additive extrusion techniques that enable local variations in the material flow. Its potential for manufacturing functional parts, which require flexibility due to cyclic loading, was demonstrated by fabrication and experimental evaluations of parts made in acrylonitrile butadiene styrene filament. The changes proposed by AltPrint enable geometric modifications in the response of the printed parts. The proposed slicing and filling control of parameters is inserted in a context of design for additive manufacturing and shows great potential in the area of product design.

Hierarchical analysis of barriers in additive manufacturing implementation with environmental considerations under uncertainty

Notwithstanding additive manufacturing has been gaining momentum in the industry, particularly during the fourth industrial revolution, their widespread implementation as a disruptive production technology has brought impacts on the environment. Considering this issue, the research for this paper was built upon the discussion concerning life cycle assessment (LCA) adoption in implementing additive manufacturing (AM). The capability of LCA to achieve environmental goals is a vivid illustration of why it has given researchers and practitioners an extensive impetus. However, to actualize such an effective approach, it is necessary to identify and face the barriers impeding its implementation in AM. Thus, this research aims to identify, classify, and analyze the most critical barriers hindering LCA adoption in AM implementation. To do so, the fuzzy analytical hierarchy process, along with a comprehensive literature review and thorough interviews with the relevant experts, was used to achieve the research purposes. The results revealed twenty-two barriers within five classifications, where the lack of financial resources to conduct LCA study on AM is the most dominant barrier, followed by the lack of LCA expertise in the AM context and the lack of laws and directives for LCA application in AM, respectively. The findings would be useful to decision-makers to develop suitable mitigation strategies and make more informed decisions with individual and/or cluster concentrations. This study can be fruitfully exploited as a guiding reference since no article has hitherto discussed, identified, or analyzed barriers in the understudied area.

Enhancing three-dimensional convolutional neural network-based geometric feature recognition for adaptive additive manufacturing: a signed distance field data approach

Abstract In the context of additive manufacturing, the adjustment of process data to individual geometric features offers the potential to further increase manufacturing speed and quality, while being widely underestimated in recent research. Unfortunately, the current non-uniform data handling in the CAD-CAM-Link results in a downstream data loss, that prevents the availability of geometric knowledge from being present at any time to apply the more advanced approaches of adaptive slicing and tool path generation. Automatic detection of various geometric entities would be beneficial for classifying partial surfaces and volumetric ranges to gain customized informational insights of geometric parameterization. In this work, an enhanced approach of geometric deep learning for the analysis of voxelized engineering parts will be presented to align the inference representations to modeling paradigms for complex design models like architected materials. Although the baseline voxel representation offers distinct advantages in detection accuracy, it comes with an adversely large memory footprint. The geometry discretization leads to high resolutions needed to capture various detail levels that prevent the analysis of fine-grained objects. To achieve efficient usage of three-dimensional (3D) deep learning techniques, we propose a 3D-convolutional neural network-based feature recognition approach using signed distance field data to limit the needed resolution. These implicit geometric data leverage the advantages of volumetric convolution while alleviating their disadvantages through the use of the continuous signed distance function. When analyzing computer-aided design data for geometric primitive features, a common application task in surface reconstruction of reverse engineering the proposed methodology, achieves a detection accuracy that is in line with the accuracy values achieved by comparable algorithms. This enables the recognition of fine-grained surface instances. The unambiguous shape information extracted could be used in subsequent adaptive slicing algorithms to achieve individual geometry-based hatch generation.

Selective laser sintering in reactive atmospheres: Towards in-situ synthesis of net-shaped carbide and nitride ceramics

Selective laser reaction sintering techniques (SLRS) were investigated to produce near net-shape non-oxide ceramic materials that were previously incompatible with powder-bed fusion additive manufacturing (AM) methodologies. Specifically, reaction-bonded layers of carbide and nitride materials (Cr3C2 and CrN) were fabricated using SLRS from a model system of Cr and Cr2O3 precursor materials. Varied particle sizes (<5–45 µm) were simultaneously chemically-converted and reaction bonded into layers during selective laser processing in 100 vol% CH4 or NH3 gas. For each precursor material, factors such as particle size, chemistry, optical properties, and microstructure were investigated using quantitative phase characterization and microstructural imaging to understand important aspects of carbide and nitride phase formation within an AM context. SLRS processing of metal or metal oxide precursor systems produced yields up to 100 wt% total for carbide formation and 100 wt% total for nitrides during reactive processing. However, when either metal or metal oxide precursor was processed by itself, the simultaneous synthesis and reaction bonding approach resulted in volumetric changes during in-situ gas-solid conversion that adversely affected layer microstructure. To circumvent the internal stresses that accompanied volumetric changes induced from conversion of the metal (which expands during conversion) and the metal oxide (which contracts), we demonstrate an alternative method for the production of near-net shape carbide and nitride materials for AM. By combining optimized ratios of metal/metal oxide precursors for processing in CH4 or NH3 reactant gas, the novel precursor formulations produced undistorted, crack-free refractory ceramic layers with sub-millimeter spatial resolution and viable layer thickness for powder bed fusion-AM. The results not only demonstrate the formation of near net-shape carbides and nitrides using AM-compatible techniques but suggest that transition metal carbide and nitrides may be compatible with AM processing even though they cannot be processed using more traditional laser-assisted AM approaches.

A Simple Approach to Stiffness Enhancement of a Printable Shape by Hamilton-Jacobi Skeletonization

The 3D-Printing technology is ready to produce parts with specific properties like individual stiffness. Based on a predefined outer shape, the inner structure of a printed part defines mainly the mechanical features. By Hamilton-Jacobi skeletonization, a stiffness enhancement of a printable shape can be achieved in a way, that a novel AM-corner includes linear axis function. Originating in the field of shape analysis in computer vision and graphics, the so-called medial axis transform (MAT) is designed for the computation of a structure that resembles the bone structure of biological shapes. The input for MAT computation is typically a shape’s boundary. The arising topological skeletons have proven to provide a useful concept for many applications; however, their computation is generally intricate and also known to rely on many parameters, diminishing the accessibility of skeletonization methods. In this work, the classical Hamilton-Jacobi skeletonization approach is adopted to compute a stability enhancing shape structure. As the basic method has not been designed for the context of additive manufacturing, a set of suitable modifications are introduced to design an algorithm that suits our intended purpose. Unlike the traditional skeletonization schemes, the resulting method appears to be robust and in practice almost completely automated as we can identify useful generic parameter settings. By a finite element method (FEM) study, the elastic stress properties of the AM-corner with linear axis function is validated and printed with in metal (1.4404) with the 3D Selected Laser Melting (SLM) system AconityMIDI. The AM-knot with skeletonization guides approximately 20 times better than a standard knot. While the first obtained results are shown as 2.5 dimensional shapes, it is emphasized that the proposed algorithm offers many possibilities for extensions to three dimensions and variations in context of additive manufacturing.

Corrections to “3D Printed Elastomeric Lattices With Embedded Deformation Sensing”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , Figure 3 should read “Isometric view of two wires forming a capacitor in the measured configuration. The yellow wire is the top plate and the red is the bottom plate of a capacitor. The gray is a dielectric elastomer lattice, the deformation of which can be indirectly determined by measuring the capacitance.” Figure 4 should read “Isometric (A) and bottom view (B) of a lattice with an alternative configuration in four quadrants for selective sensing. The selectivity can be extended to any combination of cells in both vertical and horizontal configurations.” In <xref ref-type="fig" rid="fig9" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 9</xref> , the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula> -axis should read “Min Deformation Slow; Min Deformation Fast; Max Deformation Slow; Max Deformation Fast.” A sentence in the introduction should read “Within the context of additive manufacturing, lattices are the focus of significant research since they...” The authors requested this correction for clarity purposes.

An experimental analysis of deepest bottom-left-fill packing methods for additive manufacturing

The adoption of Additive Manufacturing (AM) technology requires the efficient utilisation of the avail- able build volumes to minimise production times and costs. Three-dimensional algorithms, particularly the Deepest Bottom-Left-Fill (DBLF) heuristic, have been extensively used to tackle the problem of packing arbitrary 3D geometries within the AM sector. A particularly common method applied to more realistic packing problems is the combination of DBLF and metaheuristics such as Genetic Algorithms (GAs). Through a series of experiments, this paper experimentally investigates the practical aspects, and comparative performance of different DBLF based methods including a brute force algorithm and GA combined with DBLF for AM build volume packing. The insights into the relationship between algorithm efficiency (in terms of volume utilisation), simulation runtime, and practical requirements, in particular geometry rotation constraints are investigated. In addition to providing an increased comprehension of the practical aspects of applying DBLF algorithms in the AM context, this study confirms the limita- tions of traditional DBLF and the requirements for more flexible and intelligent placement strategies while experimentally demonstrating that higher degrees of freedom for part rotation contribute to small improvements in volume density. The resulting additional computational effort discourages this strategy, however.

Forming characteristics of additive manufacturing process by twin electrode gas tungsten arc

Traditional gas tungsten arc-based additive manufacturing (GTA-AM) system normally applies high-level deposition current to achieve a high deposition rate. Due to the induced high arc pressure, defects (e.g., humps) may always happen, which made the deposited beads unsuitable for additive manufacturing (AM) purposes. To solve this issue, a twin electrode approach has been applied in an implemented GTA-AM system, which achieved high deposition rate while kept the arc pressure with a relatively small value. The objective of this paper is to investigate the principles of using the twin electrode GTAW approach in an AM context. This paper first explored the maximum allowable wire feed speed (MAWFS) with regard to the deposition currents ranging from 200 to 450 A. A piecewise linear model was established to represent the relationship between the MAWFS and the current, which formed a basis for defining the feasible range of travel speed at a given deposition current. To validate the proposed principles, a case study was conducted, in which a set of deposition parameters were determined for fabricating a cube part. The deposition rate of twin electrodes GTA-AM is up to 2.7 kg/h, which is almost twice as much as that of traditional GTA-AM.

Additive manufacturing: empirical evidence for supply chain integration and performance from the automotive industry

PurposeAdditive Manufacturing offers much potential for industry, but at the same time is likely to have a significant impact on supply chain theory and practice. To-date there has been limited empirical work exploring the effect of Additive Manufacturing, and this study aims to provide a detailed appraisal of how supply chain integration, supply chain performance and firm performance may be affected by the adoption of Additive Manufacturing. These are critical factors for supply chain management, but have received little quantified attention to date.Design/methodology/approachA theoretical model is developed from a detailed review of the literature, from which a quantitative analysis is performed. Using data collected from 124 automotive manufacturers in European Union and the partial least square- structural equation modeling method, the research examines the relationships among different dimensions of supply chain integration, supply chain performance and firm performance from the perspective of Additive Manufacturing adoption.FindingsThe findings indicate that Additive Manufacturing adoption positively influences supply chain performance and as a consequence, firm performance. In addition, supporting the resource-based view perspective, the results show a positive indirect effect of supply chain integration on the supply chain and firm performance improvements, enabled by the Additive Manufacturing adoption. This helps to explain some inconsistent findings in previous research regarding the impacts of supply chain integration on performance.Research limitations/implicationsThe results of this study support the view that Additive Manufacturing can make a positive contribution to the supply chain, but this is not achieved solely by the technologies alone. Many of the traditional activities of supply chain management (i.e. integration) are still needed when using Additive Manufacturing, and research needs to understand whether Additive Manufacturing adoption will necessitate changes to the way these traditional activities are undertaken. Building on the findings of the current study, much more work is therefore needed to understand how operations within the supply chain may be changed, and how this may affect the integration and performance of the supply chain.Practical implicationsThis study provides quantitative evidence to show that the adoption of Additive Manufacturing has the potential to affect both firm and supply chain performance. This is significant for those companies considering the adoption of Additive Manufacturing, and may serve as a valuable insight in the strategic decision-making process. For those already using Additive Manufacturing, this study serves to underline the potential for firm performance to be influenced, by focusing on improvements to their production strategies and policies.Originality/valueThis study provides an initial insight into some fundamental supply chain concepts within an Additive Manufacturing context, which have received very little research attention. It develops a novel conceptual model, and through a large-scale industry survey provides quantified evidence of the impact of Additive Manufacturing on the supply chain. To date, much of the supply chain research is exploratory and qualitative; the quantitative evidence presented in this work, therefore, makes an important and original contribution to both research and practice.

Powders for powder bed fusion: a review

The quality of powder used in powder bed-based additive manufacturing plays a key role concerning process performance and end part properties. Even though this is a generally accepted fact, there is still a lack of a comprehensive understanding of the powder property–part property relationship. However, numerous investigations focusing on selected powder properties and their corresponding influence on process aspects or final part properties have been published in recent years. Still, generalized statements on powder requirements for a defined process performance are not available. This can be attributed to the fact that the community has not yet come to an agreement which characterization techniques are most suitable for powder characterization in the additive manufacturing context and in most cases only selected aspects have been investigated for special powder materials. The aim of this review is to assess these building blocks of knowledge and to provide an overview on the current state of the art.

Local and recyclable materials for additive manufacturing: 3D printing with mussel shells

The potential of additive manufacturing (AM) for distributed production is often mentioned as an enabler for sustainable manufacturing within a circular economy. Currently, even if manufacturing with AM is distributed, the used materials can rarely be acquired locally and are usually obtained from a centralized location. Addressing this issue, we are developing an approach that supports the search for local materials that are suitable as material input for AM and are recyclable to serve multiple product lifecycles. The approach is an iterative process consisting of four phases; “material in AM context”, “recycling opportunities”, “material property testing”, and “application possibilities”. As an initial example, we present a process to adapt mussel shell waste into AM material. Mussel shells are a voluminous waste stream in the Netherlands. The shells, which mainly exist of calcium carbonate, are ground into a powder and combined with sugar water. Using a modified material extrusion process, 3D objects are created. In this paper, we discuss the iterations through our approach and illustrate the initial 3D printed results. With this project, we intend to demonstrate the potential of using local waste streams for AM processes for a circular economy. This is a first step towards the development of a methodology for linking local material streams to novel AM processes and meaningful applications.

Polymer recycling in an open-source additive manufacturing context: Mechanical issues

Nowadays, the low recycling rate of polymers is still a challenge to humankind due to energy, economic and logistical issues. In the context of additive manufacturing, there is an exponential use of thermoplastic materials in the industrial and public open-source additive manufacturing sector, leading to an increase in global polymer consumption and waste generation. However, the coupling of the open-source 3D printers with polymer processing could potentially offer the basis for a new paradigm of distributed recycling process. It could be a complementary alternative to the traditional paradigm of centralized recycling of polymers, which is often uneconomical and energy intensive due to transportation embodied energy. In order to achieve this goal, a first step is to prove the technical feasibility to recycle thermoplastic material intended for open-source 3D printing feedstock.The contribution of the present study is twofold: first, a general methodology to evaluate the recyclability of thermoplastics used as feedstock in open-source 3D printing machines is proposed. Then, the proposed methodology is applied to the recycling study of polylactic acid (PLA) material addressed to the fused filament fabrication (FFF) technique, which is currently the most widely used. The main results of this application contribute to the understanding of the influence of the material's physico-chemical degradation on its mechanical properties as well as its potential distributed recyclability.

Rapid curing and additive manufacturing of thermoset systems using scanning microwave heating of carbon nanotube/epoxy composites

Here we show that the heating response of multi-walled carbon nanotube (MWCNT) fillers to applied microwave fields can provide a means to rapidly cure epoxy composites; this is useful for eliminating thermal gradients and decreasing cure times, even in the unconstrained environments common to additive manufacturing. We demonstrate this by comparing 5.0 wt% (2.2 vol.%) MWCNT-loaded epoxy samples cured in different environments: (i) in a conventional oven, (ii) under a forced thermal gradient in order to simulate industrial-scale non-uniformities, and (iii) scanned beneath a microwave waveguide. We assess both cure speed and cure uniformity in conductivity and composition. Microwave-cured samples show ∼1 order of magnitude higher conductivity than oven-cured samples, whereas the forced thermal gradient results in extreme variations in conductivity. This rapid cure allows for thermosets to be used in processing techniques that had previously been very difficult to achieve due to the long cure times necessary for a thermally curable epoxy. The concept of additive manufacturing of thermosets is demonstrated by completing a novel deposition-and-scan technique that results in a patterned structure of cured epoxy. This indicates that the microwave scanning technique is a feasible way to quickly cure multiple layers of unconstrained epoxy in an additive manufacturing context.

Enhancing parametric design through non-manifold topology

This paper aims to build a theoretical foundation for parametric design thinking by exploring its cognitive roots, unfolding its basic tenets, expanding its definition through new concepts, and exemplifying its potential through a use- case scenario. The paper focuses on a specific type of topological parameter, called non-manifold topology as a novel approach to thinking about designing cellular spaces and voids. The approach is illustrated within the context of additive manufacturing of non-conformal cellular structures. The paper concludes that parametric design thinking that omits a definition of topological relationships risks brittleness and failure in later design stages while a consideration of topology can create enhanced and smarter solutions as it can modify parameters based on an accommodation of the design context.

Process chain modeling and selection in an additive manufacturing context

This paper introduces a new two-dimensional approach to modeling manufacturing process chains. This approach is used to consider the role of additive manufacturing technologies in process chains for a part with micro scale features and no internal geometry. It is shown that additive manufacturing can compete with traditional process chains for small production runs. Combining both types of technology added cost but no benefit in this case. The new process chain model can be used to explain the results and support process selection, but process chain prototyping is still important for rapidly evolving fields like additive manufacturing.