Metal Additive Manufacturing Research Articles

3D Printing of Metals with sub-10µm Resolution.

The ability to manufacture 3D metallic architectures with microscale resolution is greatly pursued because of their diverse applications in microelectromechanical systems (MEMS) including microelectronics, mechanical metamaterials, and biomedical devices. However, the well-developed photolithography and emerging metal additive manufacturing technologies have limited abilities in manufacturing micro-scaled metallic structures with freeform 3D geometries. Here, for the first time, the high-fidelity fabrication of arbitrary metallic motifs with sub-10µm resolution is achieved by employing an embedded-writing embedded-sintering (EWES) process. A paraffin wax-based supporting matrix with high thermal stability is developed, which permits the printed silver nanoparticle ink to be pre-sintered at 175°C to form metallic green bodies. Via carefully regulating the matrix components, the printing resolution is tuned down to ≈7µm. The green bodies are then embedded in a supporting salt bath and further sintered to realize freeform 3D silver motifs with great structure fidelity. 3D printing of various micro-scaled silver architectures is demonstrated such as micro-spring arrays, BCC lattices, horn antenna, and rotatable windmills. This method can be extended to the high-fidelity 3D printing of other metals and metal oxides which require high-temperature sintering, providing the pathways toward the design and fabrication of 3D MEMS with complex geometries and functions.

Knowledge graph network-driven process reasoning for laser metal additive manufacturing based on relation mining

Knowledge graph network-driven process reasoning for laser metal additive manufacturing based on relation mining

Improved accuracy of continuum surface flux models for metal additive manufacturing melt pool simulations

Computational modeling of the melt pool dynamics in laser-based powder bed fusion metal additive manufacturing (PBF-LB/M) promises to shed light on fundamental mechanisms of defect generation. These processes are accompanied by rapid evaporation so that the evaporation-induced recoil pressure and cooling arise as major driving forces for fluid dynamics and temperature evolution. The magnitude of these interface fluxes depends exponentially on the melt pool surface temperature, which, therefore, has to be predicted with high accuracy. The present work utilizes a diffuse interface finite element model based on a continuum surface flux (CSF) description of interface fluxes to study dimensionally reduced thermal two-phase problems representative for PBF-LB/M in a finite element framework. It is demonstrated that the extreme temperature gradients combined with the high ratios of material properties between metal and ambient gas lead to significant errors in the interface temperatures and fluxes when classical CSF approaches, along with typical interface thicknesses and discretizations, are applied. It is expected that this finding is also relevant for other types of diffuse interface PBF-LB/M melt pool models. A novel parameter-scaled CSF approach is proposed, which is constructed to yield a smoother temperature field in the diffuse interface region, significantly increasing the solution accuracy. The interface thickness required to predict the temperature field with a given level of accuracy is less restrictive by at least one order of magnitude for the proposed parameter-scaled approach compared to classical CSF, drastically reducing computational costs. Finally, we showcase the general applicability of the parameter-scaled CSF to a 3D simulation of stationary laser melting of PBF-LB/M considering the fully coupled thermo-hydrodynamic multi-phase problem, including phase change.

Multi-laser scan assignment and scheduling optimization for large-scale metal additive manufacturing

Metal Additive Manufacturing (AM) has attracted significant attention in various industry sectors for large-scale fabrication. However, the limited fabrication efficiency has hindered its practical implementation. In comparison to traditional methods of tuning process parameters, concurrent AM equipped with multiple independently driven lasers is a more promising technique recently developed for the efficient fabrication of large metal parts. To maximize fabrication efficiency while ensuring quality for a multi-laser AM processes, an optimization problem is proposed in this work for multi-laser scanning plan, including scan vector assignment and scheduling. The goal is to minimize the makespan while considering factors that may affect the quality of metal AM parts as constraints. Specifically, the constraints associated with heat-affected zones and the user-specified single-laser scanning area are considered. The optimization model is solved by Deep Reinforcement Learning (DRL), offering the flexibility to include or exclude considerations for different quality/process requirements. Two case studies demonstrate the application of DRL models considering different sets of constraints and compare their performance with two baseline scheduling methods in terms of fabrication efficiency and violation of quality constraints. In addition, the impact of the laser number on operational improvement and the computational cost of the DRL model is also studied.

Improving the Interpretability of Data-Driven Models for Additive Manufacturing Processes Using Clusterwise Regression

Wire Arc Additive Manufacturing (WAAM) represents a disruptive technology in the field of metal additive manufacturing. Understanding the relationship between input factors and layer geometry is crucial for studying the process comprehensively and developing various industrial applications such as slicing software and feedforward controllers. Statistical tools such as clustering and multivariate polynomial regression provide methods for exploring the influence of input factors on the final product. These tools facilitate application development by helping to establish interpretable models that engineers can use to grasp the underlying physical phenomena without resorting to complex physical models. In this study, an experimental campaign was conducted to print steel components using WAAM technology. Advanced statistical methods were employed for mathematical modeling of the process. The results obtained using linear regression, polynomial regression, and a neural network optimized using the Tree-structured Parzen Estimator (TPE) were compared. To enhance performance while maintaining the interpretability of regression models, clusterwise regression was introduced as an alternative modeling technique along with multivariate polynomial regression. The results showed that the proposed approach achieved results comparable to neural network modeling, with a Mean Absolute Error (MAE) of 0.25 mm for layer height and 0.68 mm for layer width compared to 0.23 mm and 0.69 mm with the neural network. Notably, this approach preserves the interpretability of the models; a further discussion on this topic is presented as well.

Characterization of an additively manufactured coating using an upsetting test with miniaturized cylindrical specimen

In order to reduce CO2 emissions in production industry, the combination of several manufacturing processes is coming to the fore. One example is the combination of metal additive manufacturing with the forming technology, whereby the advantages of both process technologies can be used. The process combination can be applied to produce hybrid barrel sleeves, for example. Using a laser-based directed energy deposition (DED-LB/M), a circular coating is first applied to a blank, which is then deep-drawn in a second step. The additive layer serves as a wear-resistant coating. One way to increase process understanding for this process combination is numerical simulation. An important part of setting up the simulation model is characterizing the material in terms of its mechanical behavior. In order to avoid long building times, small sample geometries are suitable for characterizing the additive material. In the context of the paper, the upsetting test is therefore carried out with miniaturized specimens, whereby not only the base material Bainidur AM but also the addition of tungsten carbide microparticles and carbon nanoparticles is investigated. The in-situ modification of the material significantly increases the yield strength, but at the same time reduces the ductility. The microhardness of the material is also increased by the addition of carbon or tungsten carbide.

Powder Bed Fusion Techniques in Metal 3D Printing: A Review

The use of 3D printing (additive manufacturing) with metal has grown significantly in demand recently, greatly reducing the time and expense required to produce complex interconnected metal components. This method minimizes material wastage, facilitates material recycling, and eliminates the need for support materials. Among the various Metal Additive Manufacturing techniques, Powder Bed Fusion (PBF) processes stands out as the most prevalent for manufacturing parts. Within the realm of PBF, electron beam melting technique, selective laser sintering technique, and selective laser melting technique are the primary methods employed. Selective laser melting and selective laser sintering operate without the need for any special conditions, unlike EBM, which necessitates a vacuum environment. Regarding the choice of materials, laser melting/sintering processes are suitable for almost all types of metals except those which surpasses beam melting capabilities. While electron beam melting is constrained to a few materials such as titanium alloys, cobalt and chromium alloys, and nickel alloys, whereas selective laser melting and sintering allows for a broad range of materials, including iron and steel alloys. However, electron beam melting exhibit the ability to process brittle materials that would typically be challenging for melting and sintering through laser. Nevertheless, the ductility, yield testing, and ultimate testing of materials created through EBM are inferior to those processed by laser methods. Although all PBF techniques excel at creating complex structures, finishing products to have a smooth surface directly over a rough surface remains a subject of ongoing research. To attain suitable mechanical properties such as hardness, tensile strength, and endurance, critical process factors include power of laser or beam, speed for scanning, density for powder bed, thickness of laser or beam, and material characteristics. Inadequate material selection coupled with incorrect process settings can lead to issues such as porosity, slag formation, and other flaws.

Addressing safety and sustainability issues in the development of nano-enabled MULTI-FUNctional materials for metal additive manufacturing

This paper presents a comprehensive investigation into the sustainable development of nano-enabled multifunctional materials (NMMs) for metal additive manufacturing (MAM), based on the principles of the Safe and Sustainable-by-Design (SSbD) framework. The work focuses on both environmental impacts and health risks potentially associated with these materials, offering a groundwork for a more complete SSbD assessment that includes socio-economic considerations.The Life Cycle Assessment (LCA) identified significant environmental advantages of using Titanium Nitride (TiN) and Chromium Nitride (CrN) in wire coating processes over Titanium Carbide (TiC), with a 77% reduction in environmental impacts. However, despite the emphasis on environmental aspects during materials selection, the primary environmental hotspot is the heat treatment stage used during the process to shape NMMs into pellets. Eco-toxicity assessments performed on both nano- and microparticles from the studied NMMs for MAM, identified specific thresholds to ensure over 80% cell viability, labelling MoCu and CCuCr as particularly cytotoxic. Additionally, a task-based risk assessment in laboratory environments, evaluating the potential release of engineered nanoparticles (ENPs), identified minimal inhalation exposure risk for workers due to effective control measures implemented.Overall, by integrating findings on eco-toxicity, worker safety, and environmental hotspots in production processes, this study offers an approach for advancing sustainable practices during the production of NMMs for MAM in laboratory settings. These findings can be valuable for future research and development in the field, ensuring that sustainability and safety are integral to the advancement of nanotechnology.

Modelling of powder catchment efficiency in micro-plasma transferred arc metal additive manufacturing process

Modelling of powder catchment efficiency in micro-plasma transferred arc metal additive manufacturing process

Defect recrystallization in subtransus hot isostatic pressing of electron beam powder bed fusion Ti-6Al-4V

Post-build hot isostatic pressing (HIP) is a widely used method to close as-built defects (gas or lack-of-fusion porosity) in additively manufactured (AM) metal parts. Pore closure during this process often creates clusters of equiaxed grains, in contrast to coarse and directional as-built microstructures widely seen in AM metals. Understanding the mechanisms behind the formation of such microstructures is desirable for controlling anisotropy and improving mechanical properties of AM parts. Prior work has attributed defect-induced equiaxed grains to recrystallization during the HIP process; however the specifics of this recrystallization process have not been explored. This work systematically analyzes the grain structure, orientation, and misorientations of electron beam powder bed fusion (PBF-EB) Ti-6Al-4V to identify the mechanisms by which clusters of equiaxed grains can form during HIP of as-built microstructures. A combination of two mechanisms, dynamic recovery (DRV) and continuous dynamic recrystallization (CDRX), explains the phenomena. Evidence for both these processes is found in small rotations in crystallographic orientation, localized strains around prior pore defects, and specific misorientation distributions. Only subtransus HIP of Ti-6Al-4V was explored in this work. However, the mechanisms presented here are thought to contribute to recrystallization in other HIP processes whether for super-transus Ti-6Al-4V, β-Ti alloys, other alloy systems, or non PBF-EB build processes. These results build upon prior efforts to identify specific mechanisms by which refined microstructures can be achieved in as-HIPed AM metals, improving microstructural control and providing another pathway for microstructural tailoring via AM of metals.

Coordinate system setting for post-machining of impeller shape by wire arc DED and evaluation of processing efficiency

Wire arc additive manufacturing (WAAM) is a direct energy deposition (DED) process that uses arc welding. It is a method of stacking beads made by melting metal wires with an arc heat source generated by a short-circuit current. Compared to other metal additive manufacturing methods, this process can be used to quickly produce large and complex-shaped metal parts. However, due to the multi-bead stacking method, the surface is highly curved and the dimensional errors are large; therefore, post-processing of the surface by cutting is required. Impellers, which are widely used in various industries, have complex shapes and high material consumption during cutting; therefore, the WAAM process can improve the manufacturing efficiency. In this study, a manufacturing process for an impeller with a diameter of 160 mm was developed by using the WAAM process. A 6-bladed fan-type impeller used for high-pressure fluid delivery was similarly modeled, and the product was additively manufactured using an Inconel 625 alloy wire. Manufacturing conditions that ensure productivity and quality or the product were determined through experimentation. Considering the post-processing of the WAAM-fabricated structure, the robot and tool paths of the impeller model were designed, and the error in the process coordinate system caused by attaching and detaching the workpiece between the two processes was reduced. Through the post-processing of the WAAM-fabricated structure, the production efficiency and process reliability were verified when the conventional manufacturing method and WAAM process were applied.

Criticality of volumetric defect structure on fatigue properties of stainless steel 316L fabricated by laser powder direct energy deposition

As one of the significant metal additive manufacturing (AM) processes, laser powder direct energy deposition (LP-DED) has established its role in specific applications, including component repair, coatings, and multi-material structure. However, one of the limiting factors for the wide adoption of LP-DED is the formation of volumetric defects during the printing process. These defects are one of the crucial causes of poor and scattered mechanical properties, especially fatigue performance, by accelerating fatigue crack initiation, growth, and final failure. Unraveling defect structure-fatigue correlations can help understand how defects are affecting fatigue properties, how critical they are, and, finally, what solutions can be utilized to diminish their harmful effects. In this research study, novel defect features, including size features, shape descriptors, and location features, were proposed and investigated regarding fatigue properties in LP-DED. Accordingly, stainless steel 316L (SS 316L) blocks were fabricated by LP-DED. After cutting out the dog-bone samples by wire electric discharge machining (EDM), X-ray Computed Tomography (XCT) and subsequent fatigue tests were conducted. The defect features and their statistical descriptors were extracted from XCT data. By statistical analysis of the features, valuable insights regarding their correlations with fatigue life were realized.

Novel Lightweight Hydraulic Integration Methodology for Robotic Applications

Hydraulic integration is one of the novel technologies in the field of robotics and assistive devices. Researchers have applied recent technologies starting from non-conventional machining methodologies and ending with additive manufacturing of metals. However, those methodologies have several drawbacks related to the cost, time, and robot weight. This motivates the research of new methodologies toward developing compact, cost-effective, and lightweight hydraulic integrated robotics mechanisms. This paper presents a novel methodology for fabricating hydraulically integrated parts by using lightweight and high-strength materials. The proposed materials, fabrication steps and the obtained results are thoroughly discussed. Silicon pipes are used for building the network of internal passages. This network is built inside a 3D-printed mould which is designed accordingly. A theoretical study is conducted for the newly manufactured hydraulic parts to define the design parameters and the working pressure at which the manufactured parts can withstand in addition to stresses and deformations analysis. The theoretical results are validated using finite element modelling (FEM) and experimental testing. The simulation results were consistent with the FEM results and the experimental results by 95% and 90% respectively. The proposed methodology is cheap and simple to implement in fabrication. Hence, this methodology can be used to fabricate hydraulic integrated components in hydraulically actuated humanoid robots successfully.

Performance of laser metal deposition on hot-rolled stainless steel for hybrid steel structures

The construction sector is increasingly recognising the potential of metal additive manufacturing (AM) for fabricating complex steel details with enhanced performance and reduced resource usage. In the coming years, the application of AM technologies is expected to advance, leading to the development of new structural elements and improved performance of existing ones. This progression involves the integration of conventional construction steel with AM techniques, such as laser metal deposition (LMD), to create hybrid systems that incorporate efficient details. LMD offers the advantage of overcoming size limitations associated with laser powder bed fusion (LPBF) while leveraging its geometric capabilities. In this study, the mechanical behavior of hybrid steel components, fabricated using LMD on AISI316L stainless steel plates, was investigated through an extensive experimental campaign involving 40 samples. Axial tests, fatigue tests, and material characterisation were conducted to evaluate strength criteria and long-term behavior. Results demonstrate that LMD-produced AISI316L exhibits mechanical properties compliant with EN1993's "Design of steel structures" encompassing elastic and plastic behavior. Furthermore, both LMD samples and hybrid specimens exhibit promising fatigue performance, aligning with the highest fatigue detail category (detail category 160) for conventional steel details.

Data-driven prediction of future melt pool from built parts during metal additive manufacturing

Smart manufacturing in metal additive manufacturing (MAM) relies on real-time process optimization through the prediction of unbuilt parts using data from built parts. Melt pool dynamics are intimately associated with the development of various microstructures during the MAM. In this paper, we demonstrate the idea by establishing a machine learning (ML) model to predict the melt pool of future parts, based on the experimental data from the National Institute of Standards and Technology (NIST) where 80 % is used for the training (built parts) and 20 % for testing (future parts). The ML model integrates both data denoising and predictive modeling. First, a convolutional neural network (CNN) is used to process a large dataset of raw melt pool images, enabling the automatic removal of noises (e.g., splash and plume) and thereof extraction of high-quality melt pool data. Following that, a novel data-driven melt pool model based on multi-layer perceptron (MLP) is trained by incorporating raw, long scanning history as input features, which best accounts for the effects of printing history (e.g., intertrack heating) on melt pool development. It takes complete advantage of MLP in handling high-dimensional regression problems in conjunction with a large dataset. Upon testing under various manufacturing conditions, the average relative error magnitude (AREM) of predicting melt pool size drops to 2.8 %, compared to 14.8 % of the prior art – the Neighboring Effect Modeling Method model (NBEM). This research thus represents a significant step towards reliable melt-pool-guided AM process optimization for smart manufacturing, enabled by advanced, flexible, and maximum use of ML techniques.

Challenges in characterizing additively manufactured AlSi10Mg using X-ray Laue micro-beam diffraction

Abstract Additive manufacturing of metals using for example laser powder bed fusion systems generally results in grains of complex shapes with cellular structure of submicron sizes, accompanied by a high dislocation density. This paper presents preliminary results from characterizing an AlSi10Mg alloy manufactured by L-PBF using non-destructive three-dimensional X-ray Laue micro-beam diffraction. Both synchrotron and laboratory X-ray methods are used. The aim is to identify challenges in characterizing these microstructural features and to propose future research directions to address them.

An Image-Driven Machine Learning Method for Microstructure Characterization in Metal Additive Manufacturing: Generative Adversarial Network

An Image-Driven Machine Learning Method for Microstructure Characterization in Metal Additive Manufacturing: Generative Adversarial Network

Dislocation structure evolution during metal additive manufacturing

Abstract Dislocation structures are abundantly present in any additively manufactured alloy and they play a primary role in determining the mechanical response of an alloy. Until recently, it was understood that these structures form due to rapid solidification during AM. However, there was no consensus on whether they evolve due to the subsequent solid-state thermal cycling that occurs with further addition of layers. In order to design alloy microstructures with desired mechanical responses, it is crucial to first answer this outstanding question. This question was answered in a recent work [1] involving a novel experiment employing high resolution reciprocal space mapping, a synchrotron based X-ray diffraction technique, in situ during AM of an austenitic stainless steel. The study revealed that dislocation structures formed during rapid solidification undergo significant evolution during subsequent solid-state thermal cycling, in particular during addition of the first few (up to 5) layers above the layer of interest. A summary of the findings of this study are presented in this work. A possible pathway (involving experiment and modelling synergy) to better understanding dislocation structure formation during AM is presented.

A novel solid-state metal additive manufacturing process – Laser-induced Supersonic Impact Printing (LISIP): Exploration of process capability

Solid-state metal additive manufacturing (AM) techniques offer unique capabilities for direct printing of metallic materials towards a variety of applications. In this work, we developed a novel solid-state metal AM process, named laser-induced supersonic impact printing (LISIP), in which the laser shock-induced impact loading was utilized to trigger the adiabatic shearing phenomenon at metal-metal interfaces towards solid-state three-dimensional (3D) printing of metallic materials. The design of LISIP was inspired by cold spray, explosive welding, and laser impact welding. An experimental investigation was conducted to explore the process capability of LISIP, with a focus on 3D micro-lamination of metallic structure, AM of dissimilar materials, and printing-on-demand direct writing at various length scales from micrometer to centimeter. Steel, copper, aluminum, titanium, and magnesium alloys were used as foil and/or substate for experimentation. Moreover, the microstructure at bonding interfaces were characterized to understand the microstructure evolution as induced by adiabatic shearing. The bonding quality was evaluated using the lap shear test. The mechanisms involved in LISIP including the laser-matter interaction and adiabatic shearing bonding were investigated using first-principles modeling and finite element method simulation. In addition, the technical challenges, scientific knowledge gaps, future research directions, and potential applications of LISIP were deliberated. We envision that the findings and knowledge gained in this work will serve as the first milestone towards the establishment of LISIP for broader impacts.

Phased array-based nonlinear wave mixing technique: Application to lack-of-fusion porosity characterization in additively manufactured metals.

The objective of this research is to demonstrate the effectiveness of a phased array-based nonlinear wave mixing technique to characterize internal, localized microscale damage in an additively manufactured (AM) component. By using phased arrays for the generation of the incident waves, it is possible to produce a nonlinear wave mixing scanning technique without the need for immersion or changing coupling conditions. The phased arrays can be configured to generate incident waves in multiple directions that meet the resonance conditions required for nonlinear wave mixing at a variety of internal locations. This allows for the scanning of a specimen without the removal and re-coupling of the source transducers, leading to greater scanning speed and repeatability. To demonstrate the accuracy of this phased array wave mixing approach, measurements of acoustic nonlinearity in an AM component are first made with a bulk wave second harmonic generation through thickness measurement. Next, nonlinear wave mixing measurements are made with single element transducers to confirm the sensitivity of the proposed nonlinear wave mixing approach to lack-of-fusion porosity in AM metals. Finally, phased arrays are used to highlight the effectiveness of the proposed nonlinear wave mixing technique in these same AM components.