Metal Additive Manufacturing Research Articles

Anisotropy of Additively Manufactured Metallic Materials.

Additive manufacturing (AM) is a technology that builds parts layer by layer. Over the past decade, metal additive manufacturing (AM) technology has developed rapidly to form a complete industry chain. AM metal parts are employed in a multitude of industries, including biomedical, aerospace, automotive, marine, and offshore. The design of components can be improved to a greater extent than is possible with existing manufacturing processes, which can result in a significant enhancement of performance. Studies on the anisotropy of additively manufactured metallic materials have been reported, and they describe the advantages and disadvantages of preparing different metallic materials using additive manufacturing processes; however, there are few in-depth and comprehensive studies that summarize the microstructural and mechanical properties of different types of additively manufactured metallic materials in the same article. This paper begins by outlining the intricate relationship between the additive manufacturing process, microstructure, and metal properties. It then explains the fundamental principles of powder bed fusion (PBF) and directed energy deposition (DED). It goes on to describe the molten pool and heat-affected zone in the additive manufacturing process and analyzes their effects on the microstructure of the formed parts. Subsequently, the mechanical properties and typical microstructures of additively manufactured titanium alloys, stainless steel, magnesium-aluminum alloys, and high-temperature alloys, along with their anisotropy, are summarized and presented. The summary indicates that the factors leading to the anisotropy of the mechanical properties of metallic AM parts are either their unique microstructural features or manufacturing defects. This anisotropy can be improved by post-heat treatment. Finally, the most recent research on the subject of metal AM anisotropy is presented.

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.

Fabrication and control architecture of novel hybrid metal additive manufacturing incremental forming technology

Hybrid manufacturing processes redefine production dynamics by harnessing the synergistic interplay between process mechanisms, energy sources, and tools to impact manufacturing quality, productivity, and sustainability significantly. Accordingly, this article focuses on hybrid additive manufacturing incremental forming, where additive manufacturing and incremental forming are integrated in a unified setup, driven by a single power source. This integration opens avenues for innovative component and production design, capitalizing on the strengths of both methods while mitigating drawbacks. The fabrication of the hybrid additive manufacturing incremental forming setup involves crucial components like a hybrid extrusion forming unit, supporting plates, hopper-barrel assembly, band heaters, solenoid setup, and a comprehensive control architecture. Addressing challenges, particularly overheating in the hopper, and feeding zone, ensures effective material transformation with the hybrid extrusion forming unit. The subsequent section provides analytical analysis and validation of the hybrid extrusion unit. This technology enhances the entire process and addresses issues related to metal additive manufacturing, such as porosity and material shrinkage. The maximum tensile force sustained in hybrid additive manufacturing incremental forming before fracture demonstrates a notable enhancement of about 20% from 670 N in additive manufacturing to 805 N in hybrid additive manufacturing incremental forming. It also removes micro-cracks, and voids, and improves the inter-layer bonding, as observed through scanning electron microscopy. The results highlight hybrid additive manufacturing incremental forming's superior enhancement of mechanical properties and surface quality compared to traditional additive manufacturing approaches.

Experimental study of balling defect generation and audible sound analysis in directed energy deposition metal additive manufacturing

Experimental study of balling defect generation and audible sound analysis in directed energy deposition metal additive manufacturing

A Comparison Study on Detailed Strengthening mechanism analysis on micro structure and mechanical properties of 3 phases FGM Material with vibrational behavior analysis is fabricated using WAAM process and traditional method

Additive Manufacturing (AM) techniques implement the surface thickness accumulating through the support of CAD/CAM model for improving the 3D (three dimensional) products. Metal Additive Manufacturing provides design versatility as well as the freedom in fabricating the materials. The preparation technology of the metal composites integrates pressure leaching, agitation casting, powder metallurgy and friction stirring. The strengthening mechanism of the metal composites comprises of ceramic particle, short fiber and whisker etc. The two common strengthening mechanism to attain the high performing material composite are ceramic particulate reinforced composites (CPRCs) and precipitation hardening (PH). Thus, the current study evaluates the strengthening mechanism of fabricated material along with vibrational behavior using Wire Arc Additive Manufacturing (WAAM). The study fabricated three functionally graded material. The FGM materials are SS316L, SS316L+Inconel 625 + Ti6Al4V and SS316L+Inconel 625 + Inconel 718. The three phase FGM material is annealed at 700⁰C, 900⁰C and 1200⁰C for 60 min. Later, it have been air-cooled to the room temperature. The heating process strengthens the three phase FGM materials. The heating process of FGM material results in the precipitation which might not improve the mechanical properties. Nonetheless, the varied annealing temperature such as 900⁰C and 1200⁰C has enhanced the mechanical properties of the three phase FGM materials. As per the analysis, 60 % SS316L+20 % INCONEL 625+20 % Ti6Al4V material has been completely broken whereas 60 % SS316L+20 % INCONEL 625+20 % Inconel 718 has slight crack which show the increased strength of the material. The resonance frequency in conventional part is lower than the AM part. The resonance is oscillated with greater amplitude at certain frequencies. In damping, a higher resonance frequency is less vibrated in common frequencies. Thus, the study concludes that 60 % SS316L+20 % INCONEL 625+20 % Ti6Al4V and 60 % SS316L+20 % INCONEL 625+20 % Inconel 718 has significant strength compared to parent material SS316L. Moreover, AM has better vibration behavior compared to the conventional manufacturing.

Geometric Benchmarking of Metal Material Extrusion Technology: A Preliminary Study

Metal additive manufacturing technologies such as powder bed fusion (PBF) and direct energy deposition (DED) are experiencing fast development, due to the growing awareness of industries. However, high energy consumption, slow production processes, and high costs of both machines and feedstocks hamper their competitiveness, compared to conventional manufacturing techniques. Metal material extrusion (metal-MEX) can represent a cost- and energy-effective alternative for metal additive manufacturing. This article aims to assess the potential of such technology by addressing uncertainties related to product design and process stability through a preliminary geometric benchmarking study. The geometric tolerances and minimum achievable sizes of some simple geometries produced in 316L stainless steel were evaluated using geometric benchmark test artifacts (GBTAs). Process maps were also proposed to forecast the feasibility of achieving acceptable values of the investigated tolerances, based on the nominal dimensions of the features.

A method to assess the quality of additive manufacturing metal powders using the triboelectric charging concept

A new method to assess the quality of additive manufacturing (AM) metal powders using the triboelectric charging concept is demonstrated using CpTi, Ti6Al4V, AlSi10Mg, IN 738, and SS 316L powders. For each powder tested, the surface chemical composition was first analyzed using X-ray photoelectron spectroscopy (XPS) to determine the composition of the passivation layer. Some modifications to the current GranuCharge™ setup, developed by GranuTools™, were then performed by incorporating a flow rate measuring tool to assess how tribocharging is affected as a function of flow rate. Variations in the tribocharging response have been found with the flow rate of CpTi, AlSi10Mg and SS 316L powders. Moreover, results suggest that the tribocharging behavior might not be the same even with powders fabricated with the same passivation process. Finally, the compressed exponential model of Trachenko and Zaccone was used to reproduce the tribocharging behavior of the powders. The models were found to work best when the stretch constant β = 1.5, which is identical to the value found in other systems such as structural glasses, colloidal gels, entangled polymers, and supercooled liquids, which experience jamming when motion of individual particles become restricted, causing their motion to slow down.

The Application, Challenge, and Developing Trends of Non-destructive Testing Technique for Large-scale and Complex Engineering Components Fabricated by Metal Additive Manufacturing Technology in Aerospace

The Application, Challenge, and Developing Trends of Non-destructive Testing Technique for Large-scale and Complex Engineering Components Fabricated by Metal Additive Manufacturing Technology in Aerospace

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.

Surface crack detection on selective laser melting printed Inconel 718 using a laser generated ultrasound technique and phase space reconstruction

Abstract In the field of metallic additive manufacturing, Selective Laser Melting (SLM) has become a predominant technology due to its advantages of short production cycles, high precision, and low cost. It is frequently employed in the production of complex parts. This paper proposes the use of a scanning laser line source, in conjunction with the singular value decomposition method, to reconstruct phase space and identify surface cracks in SLM specimens. The scanning laser line source addresses the limitations of a single line source, which is often unable to accurately detect tiny cracks. By comparing experimental and simulation data, the results demonstrate that the scanning laser line source can effectively compensate for some of the detection deficiencies of a single line source.

Exploring the Effect of Heat Treatment on the Mechanical Performance of 17-4PH Stainless Steel Specimens Fabricated by Metal Additive Manufacturing

Exploring the Effect of Heat Treatment on the Mechanical Performance of 17-4PH Stainless Steel Specimens Fabricated by Metal Additive Manufacturing

Multi-material structures of Ti6Al4V and Ti6Al4V-B4C through directed energy deposition-based additive manufacturing

The demand for advanced materials has driven innovation in titanium alloy design, particularly in the aerospace, automotive, and biomedical sectors. Additive manufacturing (AM) enables the construction of multi-material structures, offering potential improvements in mechanical properties such as wear resistance and high-temperature capabilities, thus extending the service life of components such as Ti6Al4V. Directed energy deposition (DED)-based metal AM was used to manufacture radial multi-material structures with a Ti6Al4V (Ti64) core and a Ti6Al4V-5 wt.% B4C composite outer layer. X-ray diffraction analysis and microstructural observation suggest that distinct B4C particles are strongly attached to the Ti6Al4V matrix. The addition of B4C increased the average hardness from 313 HV for Ti6Al4V to 538 HV for the composites. The addition of 5 wt.% B4C in Ti6Al4V increased the average compressive yield strength (YS) to 1440 MPa from 972 MPa for the control Ti6Al4V, i.e., >48% increase without any significant change in the elastic modulus. The radial multi-material structures did not exhibit any changes in the compressive modulus compared to Ti6Al4V but displayed an increase in the average compressive YS to 1422 MPa, i.e., >45% higher compared to Ti6Al4V. Microstructural characterization revealed a smooth transition from the pure Ti6Al4V at the core to the Ti64-B4C composite outer layer. No interfacial failure was observed during compressive deformation, indicating a strong metallurgical bonding during multi-material radial composite processing. Our results demonstrated that a significant improvement in mechanical properties can be achieved in one AM build operation through designing innovative multi-material structures using DED-based AM.

Effects of process parameters on the surface characteristics of laser powder bed fusion printed parts: machine learning predictions with random forest and support vector regression

Laser powder bed fusion (L-PBF) fuses metallic powder using a high-energy laser beam, forming parts layer by layer. This technique offers flexibility and design freedom in metal additive manufacturing (MAM). However, achieving the desired surface quality remains challenging and impacts functionality and reliability. L-PBF process parameters significantly influence surface roughness. Identifying the most critical factors among numerous parameters is essential for improving quality. This study examines the effects of key process parameters on the surface roughness of AlSi10Mg, a widely used aluminum alloy in high-tech industries, fabricated by L-PBF. Part orientation, laser power, scanning speed, and layer thickness were identified as crucial parameters via cause-and-effect analysis. To systematically examine their effects, the Taguchi method was employed within the framework of the design of experiment (DoE). Experimental results and statistical analysis revealed that laser power, scanning speed, and layer thickness significantly influence surface roughness parameters: arithmetic mean (Ra) and root mean square (Rq). Main effect plots and energy density analyses confirmed their impact on surface quality. Microscopic investigations identified surface flaws such as spattering, balling, and porosity contributing to poor quality. Given the complex interplay between parameters and surface quality, accurately predicting their effects is challenging. To address this, machine learning models, specifically random forest regression (RFR) and support vector regression (SVR), were used to predict the effects on surface roughness. The RFR model’s R2 values for predicting Ra and Rq are 97% and 85%, while the SVR model’s predictions are 85% and 66%, respectively. Evaluation metrics demonstrated that the RFR model outperformed SVR in predicting surface roughness.

Computationally guided alloy design and microstructure-property relationships for non-equiatomic Ti–Zr–Nb–Ta–V–Cr alloys with tensile ductility made by laser powder bed fusion

Single-phase body-centered cubic refractory complex concentrated alloys (RCCAs), exhibit strength retention at temperatures above the melting points of conventional Ni-based superalloys. However, their lack of room temperature tensile ductility leads to cracks during processing and/or premature failure under mechanical loading when fabricated by laser-based metals additive manufacturing (AM). We present an alloy design framework for tensile ductility in non-equimolar RCCAs amenable to AM processing within the Ti–V–Cr–Nb–Zr–Ta system. First, density functional theory (DFT) and machine learning informed screening of alloy compositions were used to down-select ∼106 alloys with potential for tensile ductility. Next, Scheil solidification modeling was used to eliminate compositions most at risk for micro-segregation and solidification cracking based on the estimated freezing range of each alloy. This led to the design of two new, representative alloys: Ti0.4Zr0.4Nb0.1Ta0.1 and Ti0.486V0.375Ta0.028Cr0.111. These alloys were evaluated for rapid solidification defect formation using in-situ synchrotron melt pool imaging, fabricated via laser powder bed fusion (L-PBF) AM, and then characterized for processing defects, microstructure, and mechanical properties. Overall, both alloys achieved tensile yield strengths >800 MPa and failure strains >5 %. However, the occurrence of un-melted powders of high melting point elements likely resulted in premature failure during tensile straining. Considerations about mitigating this defect source are discussed and the role of local micro-segregation on ductility are elucidated. Overall, these results highlight the success of our framework, and show potential for laser-based AM-RCCAs with tensile ductility.

Physics-Informed Online Learning for Temperature Prediction in Metal AM.

In metal additive manufacturing (AM), precise temperature field prediction is crucial for process monitoring, automation, control, and optimization. Traditional methods, primarily offline and data-driven, struggle with adapting to real-time changes and new process scenarios, which limits their applicability for effective AM process control. To address these challenges, this paper introduces the first physics-informed (PI) online learning framework specifically designed for temperature prediction in metal AM. Utilizing a physics-informed neural network (PINN), this framework integrates a neural network architecture with physics-informed inputs and loss functions. Pretrained on a known process to establish a baseline, the PINN transitions to an online learning phase, dynamically updating its weights in response to new, unseen data. This adaptation allows the model to continuously refine its predictions in real-time. By integrating physics-informed components, the PINN leverages prior knowledge about the manufacturing processes, enabling rapid adjustments to process parameters, geometries, deposition patterns, and materials. Empirical results confirm the robust performance of this PI online learning framework in accurately predicting temperature fields for unseen processes across various conditions. It notably surpasses traditional data-driven models, especially in critical areas like the Heat Affected Zone (HAZ) and melt pool. The PINN's use of physical laws and prior knowledge not only provides a significant advantage over conventional models but also ensures more accurate predictions under diverse conditions. Furthermore, our analysis of key hyperparameters-the learning rate and batch size of the online learning phase-highlights their roles in optimizing the learning process and enhancing the framework's overall effectiveness. This approach demonstrates significant potential to improve the online control and optimization of metal AM processes.

Phase-field ductile fracture simulations of thermal cracking in additive manufacturing

We present a multiphysics phase-field fracture model for thermo-elasto-plastic solids in the context of finite deformation and apply it to simulate the hot cracking phenomenon during metal additive manufacturing. The model is derived in a thermodynamically consistent manner, with the intercoupling mechanisms among elastoplasticity, phase-field crack and heat transfer comprehensively considered. It involves particularly coupled parameters among these materials physics, e.g. plasticity-dependent degradation function and fracture toughness, damage-dependent yield surface and thermal properties, and temperature-dependent elastoplastic properties and fracture strength. The finite element implementation of the coupled phase-field model is benchmarked with simulation results of a tensile test of an I-shape specimen, encompassing elastoplasticity, hardening, necking, crack initiation and propagation, in contrast to the related experimental results. The validated model is further employed to simulate the multiphysics hot cracking phenomenon in additive manufacturing in the context of both the effective powder-bed model and the powder-resolved model thanks to prior non-isothermal phase-field powder-bed-fusion simulations. Simulation results reveal certain key features of the hot crack and its dependency on process parameters like beam power and scan speed, which are helpful for the fundamental understanding of crack formation mechanisms and process optimization.

A highly efficient computational approach for fast scan-resolved microstructure predictions in metal additive manufacturing on the scale of real parts

In metal additive manufacturing (AM), fast and efficient simulation approaches are essential to explore the full potential of these promising processes, particularly in generating components with tailored microstructures via laser powder bed fusion (LPBF). Due to the inherent multiscale nature of LPBF, existing approaches often need to resort to strong simplifications, such as layer-wise heating models, to make part-scale simulations feasible. In contrast, the present article proposes a scan-resolved approach, which consistently resolves the laser scan path in a coupled thermo-microstructural model of LPBF. Building on a high-performance computing model for the thermal problem, we propose a highly efficient implementation of a recently developed microstructure model for Ti-6Al-4V with three main constituents: stable αs-phase, martensitic αm-phase and β-phase. The implementation is tailored to modern hardware features using vectorization and fast approximations of transcendental functions. A performance model and selected numerical examples of LPBF manufacturing of parts on the centimeter scale are studied to verify the high degree of optimization. Depending on the specific example, results were obtained with moderate computational resources in a few hours to days. We demonstrate how the proposed scan-resolved model allows us to predict the correlation between scan strategy and resulting microstructure composition, an aspect that layer-wise heating models cannot capture. The numerical examples include scan-resolved thermo-microstructure simulations of the full NIST AM Benchmark cantilever specimen. It is shown that varying the build plate temperature by only 100K can significantly change the microstructure composition from αm- to αs-dominated.

Fault Diagnosis and Prediction System for Metal Wire Feeding Additive Manufacturing.

In the process of metal wire and additive manufacturing, due to changes in temperature, humidity, current, voltage, and other parameters, as well as the failure of machinery and equipment, a failure may occur in the manufacturing process that seriously affects the current situation of production efficiency and product quality. Based on the demand for monitoring of the key impact parameters of additive manufacturing, this paper develops a parameter monitoring and prediction system for the additive manufacturing feeding process to provide a basis for future fault diagnosis. The fault diagnosis and prediction system for metal wire supply and additive manufacturing utilizes STM 32 as its core, enabling the capture and transmission of temperature, humidity, current, and voltage data. The upper computer system, designed on the LabVIEW 2019 virtual instrument platform, incorporates an LSTM neural network model and facilitates a connection between LabVIEW and MATLAB 2019 to achieve the prediction function. The monitoring and prediction system established in this study is intended to provide basic research assistance in the field of fault diagnosis.

Temperature-based damage detection for the commissioning dataset of the MX3D Bridge

Environmental and operational variations (EOV) remain a major obstacle for the successful transfer of structural health monitoring (SHM) techniques from laboratory experiments, such as cracked beam experiments, to full-scale structures, such as long-span bridges. With evidence that timely interventions significantly increase the service-life and reduce the overall life-cycle cost of ageing infrastructure, carrying out SHM in the presence of EOV, such as temperature, is therefore of high priority within the civil engineering community. The temperature-based measurement-interpretation (TB-MI) approach was developed to monitor the thermal response of an instrumented structure and detect changes linked to damage by minimising the impact that temperature variations have on anomaly detection techniques. The iterative regression-based thermal response prediction (IRBTRP) methodology is utilised in the TB-MI approach, and is trained on the healthy condition of the bridge to predict its undamaged response to temperature fluctuations. Thermal response predictions are compared to the measured response and deviations between them are indicative of a variation in the thermal response, due to structural change such as damage. Small deviations can then be identified using anomaly detection techniques, which results in earlier damage detection than if thermal response predictions had not been used. The TB-MI approach and the IRBTRP methodology have been applied to detect damage on the MX3D Bridge, the world’s first structure produced through metal additive manufacturing (i.e. 3D printing). This study demonstrates that the use of the TB-MI approach enables earlier and more widespread damage detection amongst multiple sensor groups, compared to when no temperature effects are considered. The adoption of the TB-MI approach can therefore greatly increase the reliability of and our reliance on SHM techniques for critical infrastructure.

Broadband ridge waveguide to rectangular waveguide transition design in D‐band

AbstractThis article presents a D‐band (110–170 GHz) double ridge waveguide (WG) to standard rectangular WG transition with an E‐bend near the WG end. The transition incorporates metal ridge steps as impedance transformers, resulting in a compact total size of 3.53 mm × 1.95 mm × 1 mm. Fabricated using micro metal additive manufacturing with copper in a monolithic back‐to‐back configuration, the transition demonstrates a bandwidth of 50% (105–175 GHz) with an average insertion loss of 0.72 dB and return loss better than 15 dB, respectively. Measurement results align closely with simulation expectations, with deviations attributed to fabrication and assembly errors.