Additive Manufacturing Applications Research Articles

Robot material processing and hardware-in-the-loop-based real-time simulations

Abstract This paper presents a cyber-physical production system that consists of a simulation, an industrial robot cell, and sensors. The industrial robot hardware, used for welding and additive manufacturing applications, is connected or “in-the-loop” with a real-time target machine on which simulations are running. These simulations are updated in real-time by the data provided by process sensors. Particular focus is given to wire-arc additive manufacturing (WAAM). Still, the cyber-physical system allows use in other robot-based material processes, such as sheet forming, (dis)assembly and material handling applications. It is also argued that the proposed cyber-physical system can be used so that it competes against the concept of using machine learning to optimize manufacturing processes. The proposed cyber-physical system enables the transition from traditional robot automation to autonomous robot systems.

Enhanced cooling performance of blade tip slot cooling: Effect of slot open length

Addressing the critical challenge of thermal load-induced damage in turbine blade tips, this study aims to elucidate the effects of slot open length on film cooling effectiveness (FCE) across turbine blade tips with Pressure Side (PS) and Suction Side (SS) slots considering the application of additive manufacturing to turbine blade tips. Employing a linear cascade setup and the pressure sensitive paint (PSP) technique, we examined and quantified cooling performance across turbine blade tips with PS or SS slots at various slot open lengths. We revealed that while short slot open lengths enhance FCE at the leading edge (LE) due to increased averaged blowing ratios, they fail to adequately cover the trailing edge (TE) region. This decrease in cooling performance is largely attributable to the mixing of coolant with hot gas. In contrast, longer slot open lengths achieve a consistent FCE distribution across the blade tip despite a slight decrease in FCE at the LE region. The study suggests that integrating additional thermal design strategies, such as partition walls with slots, could effectively manage hot gas behaviors, thus enhancing the overall cooling design of turbine blade tips. This research makes a significant contribution to the field of turbine blade tip cooling design, providing insights into optimizing cooling performance through advanced slot configurations enabled by additive manufacturing technologies.

Meta-structures for energy absorption: materials, designs, and applications in additive manufacturing and its future scope

Meta-structures for energy absorption: materials, designs, and applications in additive manufacturing and its future scope

Static and dynamic characterization of 3D-printed polymer structural elements

Considering wide applications of Additive Manufacturing (AM), profound knowledge on the mechanical performance of AMed components is a necessity. In the present study, the mechanical behavior of AMed polymer parts under static and dynamic tests has been investigated. To this end, cantilever beams with three different mesostructure cells were designed and fabricated via ABS Carbon material based on the fused deposition modeling process. The specimens were subjected to a series of static bending tests and free vibration experiments. In addition, numerical models have been presented for both static bending and the dynamic tests. In the current study, digital image correlation technique has been employed to determine strain field and validate the numerical results. The experimental findings and numerical outcomes have been compared and the convergence has been investigated. Based on the applications of AM in fabrication of structural elements with complex geometries, the results of the current study are useful for new designs of AMed parts with customized mechanical strength and enhanced structural performance.

Laser-directed energy deposition of low-carbon, low-temperature ultra-fine bainitic multi-physical modeling, microstructure and performance studies

An innovative iron-based alloy powder characterized by reduced carbon content and elevated Mn content was developed, leading to the fabrication of iron-based ultrafine bainitic steel with exceptional mechanical properties utilizing laser-directed energy deposition (L-DED). The layers' morphology, microstructure, phase composition, and elemental distribution were analyzed, indicating that the melting depth of the layer was deep and ultra-fine dendrite formed in the top region. A multi-physical powder and melt pool simulation was conducted to analyze the melt flow and pool solidification characteristics, showing that the strong downward flow inside the molten pool caused by the powder impingement increased the melting depth. The cooling rate G × R, where G was the temperature gradient and R was the solidification rate, was higher at the top pool boundary, facilitating the ultra-fine dendrite formation at this location. Following subsequent low-temperature isothermal heat treatment, a refined bainitic microstructure emerged within the deposited layer. The bainitic plates in the 573 K isothermally transformed bainite were interspersed with film-retained austenite (RA). Conversely, the bainitic plates formed at a lower temperature (523 K) exhibited a coalesced morphology stemming from the diminished stability of super-cooled austenite. The bainite plates underwent growth and coalescence, ultimately giving rise to the bainitic microstructure. The 523 K transformed coatings demonstrated superior hardness (509.2 HV), strength (1435 MPa), and elongation (11.5 %) compared to the 573 K transformed coatings (456.5 HV, 1241 MPa, 10.1 %). This research holds significant implications for advancing low-carbon bainitic materials with remarkable comprehensive mechanical properties in additive manufacturing applications.

Machine Learning for Additive Manufacturing of Functionally Graded Materials.

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

Fluid Ejections in Nature.

From microscopic fungi to colossal whales, fluid ejections are universal and intricate phenomena in biology, serving vital functions such as animal excretion, venom spraying, prey hunting, spore dispersal, and plant guttation. This review delves into the complex fluid physics of ejections across various scales, exploring both muscle-powered active systems and passive mechanisms driven by gravity or osmosis. It introduces a framework using dimensionless numbers to delineate transitions from dripping to jetting and elucidate the governing forces. Highlighting the understudied area of complex fluid ejections, this review not only rationalizes the biophysics involved but also uncovers potential engineering applications in soft robotics, additive manufacturing, and drug delivery. By bridging biomechanics, the physics of living systems, and fluid dynamics, this review offers valuable insights into the diverse world of fluid ejections and paves the way for future bioinspired research across the spectrum of life.

A‐prori layer height determination for wire arc additive manufacturing based on weld geometry

AbstractThe application of wire arc additive manufacturing (WAAM) for the production of large size components is currently limited, due to strong distortion and unprecise filling behavior. The resulting geometric and metallurgical irregularities pose a challenge to the process. The current approach of a layered structure cannot be adopted without adjustments when using wire arc additive manufacturing. Reasons include incompleteness, material accumulation and deformation. The combination of experimental weld geometry‐determination and its numerical estimation is presented here as solution to this challenge. The procedure is based on the measurement of a weld bead cross‐section‐area. By convolution of a path matrix with a weld‐geometry‐function, the planned path is filled with the seam geometry. Subsequent summation of multiple matrices results in a height profile showing discontinuities and accumulations. Further validation tests show a good agreement between the method and experimentally determined problem areas. The presented optimisation procedure can be extended with material parameters. A local compensation for deformation can be achieved.

A Detailed Properties Comparison of an Automotive Sealant Nozzle Produced Using Three Metal Additive Manufacturing Technologies.

Choosing the right metal AM equipment and material is a highly intricate process that forms a crucial part of every manufacturing company's strategic plan. This study undertakes a comprehensive comparison of the performance and material properties of three Metal Additive Manufacturing (AM) technologies: Powder Bed Fusion (PBF), Metal Filament Deposition Modeling (MFDM), and Bound Metal Deposition (BMD). An automotive nozzle was selected and manufactured using all three technologies and three metallic materials to understand their respective advantages and disadvantages. The samples were then subjected to a series of tests and evaluations, including dimensional accuracy, mechanical properties, microstructure, defects, manufacturability, and cost efficiency. The nozzle combinations were PBF in aluminum, MFDM in stainless steel, and BMD in hard tool steel. The results underscore significant differences in functionality, material characteristics, product quality, lead time, and cost efficiency, all of which are crucial factors in making equipment investment decisions. The conclusions drawn in this paper aim to assist automotive industry equipment experts in making informed decisions about the technology and materials to use for parts with characteristics like these. Future studies will delve into other technologies, automotive components, and materials to further enhance our understanding of the application of metal AM in manufacturing.

Wire-arc directed energy deposition of metal using a tendon-driven soft robotic gun: prototyping and conceptual validation

ABSTRACT Most directed energy deposition (DED) implementations employ a rigid end effector to deposit material layer-by-layer, which is insufficient in cases with space constraints. To enhance the flexibility, this paper proposes a novel soft-robotic gun (SRG) based DED technology. The ideology is to replace the rigid weld gun with a tendon-driven omnidirectional-steering continuum manipulator, which a computer can numerically control. To this end, both requirements and concepts of the SRG-DED are introduced. A prototype system is designed and implemented before dedicated kinematic modelling and trajectory planning mechanisms are presented. As demonstrated by the experimental results, the developed trajectory planning algorithm improves the computational efficiency of tendon variables and enhances the consistency of trajectory tracking. The prototype system can deposit a complex path on an inclined substrate and fabricate a thin-wall component within a pipeline. The validation work confirms the feasibility and applicability of the prototype system for metal additive manufacturing applications.

Lean-and-Green Fractional Factorial Screening of 3D-Printed ABS Mechanical Properties Using a Gibbs Sampler and a Neutrosophic Profiler

The use of acrylonitrile butadiene styrene (ABS) in additive manufacturing applications constitutes an elucidating example of a promising match of a sustainable material to a sustainable production process. Lean-and-green datacentric-based techniques may enhance the sustainability of product-making and process-improvement efforts. The mechanical properties—the yield strength and the ultimate compression strength—of 3D-printed ABS product specimens are profiled by considering as many as eleven controlling factors at the process/product design stage. A fractional-factorial trial planner is used to sustainably suppress by three orders of magnitude the experimental needs for materials, machine time, and work hours. A Gibbs sampler and a neutrosophic profiler are employed to treat the complex production process by taking into account potential data uncertainty complications due to multiple distributions and indeterminacy issues due to inconsistencies owing to mechanical testing conditions. The small-data multifactorial screening outcomes appeared to steadily converge to three factors (the layer height, the infill pattern angle, and the outline overlap) with a couple of extra factors (the number of top/bottom layers and the infill density) to supplement the linear modeling effort and provide adequate predictions for maximizing the responses of the two examined mechanical properties. The performance of the optimal 3D-printed ABS specimens exhibited sustainably acceptable discrepancies, which were estimated at 3.5% for the confirmed mean yield strength of 51.70 MPa and at 5.5% for the confirmed mean ultimate compression strength of 53.58 MPa. The verified predictors that were optimally determined from this study were (1) the layer thickness—set at 0.1 mm; (2) the infill angle—set at 0°; (3) the outline overlap—set at 80%; (4) the number of top/bottom layers—set at 5; and (5) the infill density—set at 100%. The multifactorial datacentric approach composed of a fractional-factorial trial planner, a Gibbs sampler, and a neutrosophic profiler may be further tested on more intricate materials and composites while introducing additional product/process characteristics.

Investigation of oil palm fiber reinforced polylactic acid composite extruded filament quality

This study examines the quality of Polylactic Acid (PLA) filament reinforced with Oil Palm Fiber (OPF) for additive manufacturing applications. The research aims to create a composite filament that leverages the advantages of PLA, a biodegradable polymer, and OPF, a natural fiber from the oil palm tree, to enhance mechanical strength, dimensional stability, and printability. The methodology involves crushing the PLA filament and OPF to the desired size using a crusher machine, blending them in different ratios (e.g., 90:10 and 80:20 PLA to OPF), and using a hot-pressing process to bond the components. The resulting pelletized composites are then extruded into filaments using an extruder machine. The quality of the produced filament is assessed based on diameter consistency, surface smoothness, and printability, considering compatibility with 3D printers. The study reveals that composition ratios and processing parameters impact filament quality, leading to challenges such as diameter variations, rapid hardening, breakage, and extruder die clogs. Future recommendations were suggested to optimize compositions, refine processing, explore advanced extrusion, and investigate fiber distribution and bonding for improved filament properties. This research offers valuable insights for creating high-quality OPF-reinforced PLA filaments for additive manufacturing, advancing understanding of filament quality factors, and proposing ways to enhance composite filament performance across applications.

Advancements in Fused Deposition Modeling for Aerospace: Optimizing Lightweight and High- Strength Components

Fused Deposition Modeling (FDM) has emerged as a pivotal technology in aerospace manufacturing, enabling the creation of lightweight and high-strength components. Recent advancements in FDM materials, process optimization, and design methodologies have significantly enhanced its applicability in producing aerospace parts that meet stringent performance criteria. This paper reviews the latest developments in FDM technology, focusing on material innovations, structural optimization techniques, design for additive manufacturing and practical applications in the aerospace sector. Key advancements include the use of high-performance thermoplastics, carbon fiber composites, and hybrid materials, as well as improved printing techniques that reduce defects and enhance mechanical properties. The potential of FDM to revolutionize aerospace manufacturing through cost- effective and efficient production of complex geometries is explored, highlighting ongoing research and future directions in this dynamic field.

Evaluation of alternative construction methods for architectural additive manufacturing applications

Evaluation of alternative construction methods for architectural additive manufacturing applications

Bridging molecular dynamics and additive manufacturing: A study of Al-12 at% Si alloy solidification dynamics

This manuscript provides a detailed analysis of the molecular dynamics of the directional solidification process in Aluminum-Silicon (Al-12 % at Si) alloys, with a particular emphasis on applications in additive manufacturing. Utilizing molecular dynamics (MD) simulations, the study examines large systems containing 5–15 million atoms to investigate various facets of the solidification process. These include the dynamics of interface motion and the formation of Silicon (Si) precipitates. The research reveals significant variations in interface velocities and orientations across different thermal gradients and growth rates, underscoring the intricate interaction between thermal and solute flows in the microstructural development of Al-Si alloys. These findings are vital for comprehending and refining the solidification process in additive manufacturing, ultimately enhancing the mechanical strength and reliability of the final products. The paper highlights the critical role of microstructure control in printed metals and proposes future research directions, aiming to apply these insights to more complex alloy compositions and manufacturing contexts. In the concluding phase, the scikit-image library was employed to analyze dendritic formations in solidifying Al-Si alloys using MD simulations. This analysis delineates trends in dendritic count, spacing, perimeter, and area over time, offering essential perspectives on the solidification process and its implications for the alloy's microstructure and mechanical characteristics. The Si content in the Al-Si alloy used can be considered eutectic in the solidified structure. Si atoms are predominantly located at grain boundaries and precipitate regions. Coordination number analysis further confirms the presence of Si in these locations. This microstructure, characterized by a primary Al/Al-Si matrix and Si-rich eutectic regions, aligns with experimental observations in rapid solidification processes in additive manufacturing.

Generative Artificial Intelligence: Analyzing Its Future Applications in Additive Manufacturing

New developments in the field of artificial intelligence (AI) are increasingly finding their way into industrial areas such as additive manufacturing (AM). Generative AI (GAI) applications in particular offer interesting possibilities here, for example, to generate texts, images or computer codes with the help of algorithms and to integrate these as useful supports in various AM processes. This paper examines the opportunities that GAI offers specifically for additive manufacturing. There are currently relatively few publications that deal with the topic of GAI in AM. Much of the information has only been published in preprints. There, the focus has been on algorithms for Natural Language Processing (NLP), Large Language Models (LLMs) and generative adversarial networks (GANs). This summarised presentation of the state of the art of GAI in AM is new and the link to specific use cases is this first comprehensive case study on GAI in AM processes. Building on this, three specific use cases are then developed in which generative AI tools are used to optimise AM processes. Finally, a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is carried out on the general possibilities of GAI, which forms the basis for an in-depth discussion on the sensible use of GAI tools in AM. The key findings of this work are that GAI can be integrated into AM processes as a useful support, making these processes faster and more creative, as well as to make the process information digitally recordable and usable. This current and future potential, as well as the technical implementation of GAI into AM, is also presented and explained visually. It is also shown where the use of generative AI tools can be useful and where current or future potential risks may arise.

Application of Additive Manufacturing in the Development of Polymeric Bioresorbable Cardiovascular Stents: A Review

AbstractPolymeric vascular bioresorbable stents (BRSs) have been widely used for the treatment of coronary artery diseases. While additive manufacturing (AM) is changing the landscape of the healthcare sector by enabling the realization of patient‐specific stents with highly complex structures. There are, however, challenges associated with the use of polymeric BRS, particularly in‐stent restenosis (ISR), related to its poor mechanical properties. Therefore, the aim of this review is to provide an overview of recent advancements in the development of polymeric BRSs designed to meet both mechanical and biological requirements. First, biopolymers as well as shape memory polymers (SMPs) that are suitable for BRSs are highlighted and briefly described. Second, different types of designing structures of vascular stents in addition to introducing effective mechanical metamaterials, e.g., negative Poisson ratio (NPR) structures are addressed. Subsequently, AM methods currently being used to fabricate polymeric BRSs, are discussed and compared with conventional fabrication methods. Lastly, future directions for research are proposed in relation to existing challenges to the realization of a new generation of AM BRSs. Overall, this paper serves as a benchmark for future cardiovascular applications, especially in order to obtain clinically viable polymeric vascular stents by selecting suitable polymers, designs, and AM technologies.

Microstructure and mechanical properties of Co31.5Cr7Fe30Ni31.5 high-entropy alloy powder produced by plasma rotating electrode process and its applications in additive manufacturing

Microstructure and mechanical properties of feedstock powders critically determine the quality of additive manufacturing (AM). The present study employs a plasma rotating electrode process (PREP) to produce non-equiatomic Co31.5Cr7Fe30Ni31.5 high-entropy alloy (HEA) powders and examines variations of their microstructure and mechanical properties with powder particle size in the range of 20–106 μm. It finds that the powders possess smooth surfaces, good sphericity, and equiaxed polycrystal grains under PREP electrode rod arc speed of 32000 r/min and main arc current of 760 A. A stable FCC structure is maintained regardless of the powder particle size. Moreover, the nano-hardness of the powders generally decreases with the increase of powder particle size except for that of 50–75 μm, which is positively correlated with the change of the grain size. The finest grain size is present for powders with particle sizes of 50–75 μm, which possess the highest average nano-hardness and reduced elastic modulus. Bulk alloys fabricated by cold spray (CS) and laser cladding (LC) AM maintain the FCC structure. The CSAM bulk alloy shows early brittle fracture due to the presence of pores and prior particle boundaries resulting from insufficient plastic deformation of the powder particles. The LCAM bulk alloy presents good tensile properties with the ultimate tensile strength (UTS) and elongation to failure being 481.2 MPa and 35.9%, respectively, which can be attributed to the good cohesion and grain characteristics. The present study shall provide the field of AM with useful information in the applications of non-equiatomic PREP HEA powders.

Fabrication of Customized Bioactive Scaffolds for the Repair of Bone Defects using 3d Printing

3D-printing is a hot topic of today's technological discussion. A large number of research papers have been published every year on additive manufacturing and its application in medical cases. A significant number of papers from that are studied and various applications of 3d-printing in the field of medical analyzed for delivering the best and reliable method for the development of scaffolds and biomedical implants. This study will illustrate the advantages of 3D-printing as compared to conventional manufacturing methods. Numerous applications of additive manufacturing are represented in the different field of today's world. The paper aims to show the 3D-Printing technology as being utilized in medical and its advantages alongside present days and future applications. Conclusion: Paper presents the literature review of medical application of 3-D Printing and its future scope. 3-D medical models are prepared with reverse engineering to design and manufacturing of customized implants and bones. It modified and customized as pre patient requirements. It may be different for every patient and can be modified. It provides the extensive benefits to humanity in short time as compared to other manufacturing processes.

Micro- and Nanoengineered Metal Additively Manufactured Surfaces for Enhanced Anti-Frosting Applications.

The greater geometrical design freedom offered by additive manufacturing (AM) as compared to the conventional manufacturing method has attracted increasing interest in AM to develop innovative and complex designs for enhanced performance. However, the difference in material composition and surface properties from conventional alloys has made surface micro-/nanostructuring of AM metals challenging. Frost accretion is a safety hazard in numerous engineering applications. To expand the application of AM, this study experimentally investigates the antifrosting performance of superhydrophobic and slippery lubricant-infused porous surfaces (SLIPSs) generated on AM alloy, AlSi10Mg. By strategically utilizing the subgrain structure in the metallography of the AM alloy, the functionalized superhydrophobic AM surface featuring hierarchical structures was shown to greatly reduce frost formation as compared to functionalized single-tier structured surfaces, hierarchical structures formed on conventional aluminum alloy surfaces, and SLIPSs. Optical observation of frost propagation demonstrated that the mechanism of frost delay is governed by the inhibition of spontaneous droplet freezing through exceptional Cassie state stability during condensation frosting. The Cassie stability results from the unique AM structure morphology, which creates a higher structural energy barrier to prevent condensate from infiltrating the cavities. This phenomenon also enables the formation of a high surface-to-droplet thermal resistance, which eliminates spontaneous droplet freezing down to a -15 °C surface temperature. Our work demonstrates a scalable structuring method for AM metals, which can result in delayed frost formation, and it also provides guidelines for the development of engineered surfaces requiring the antifrosting function for several industries.