Metal-based Additive Manufacturing Research Articles

A Data-Driven Approach for Process Optimization of Metallic Additive Manufacturing Under Uncertainty

The presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6Al-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multiphysics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power, and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have the largest impact on the microstructure variation. Through this exemplar process optimization, the current study also demonstrates the promising potential of the presented approach in facilitating other complicate AM process optimizations, such as robust designs in terms of porosity control or direct mechanical property control.

Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling

The complicated metal-based additive manufacturing (AM) process involves various sources of uncertainty, leading to variability in AM products. For comprehensive uncertainty quantification (UQ) of AM processes, we present a physics-informed data-driven modeling framework, in which multilevel data-driven surrogate models are constructed based on extensive computational data obtained by multiscale multiphysics AM models. It starts with computationally inexpensive surrogate models for which the uncertainty can be readily quantified, followed by global sensitivity analysis for comprehensive UQ study. Using AM-fabricated Ti-6Al-4V components as examples, this study demonstrates the capability of the proposed data-driven UQ framework for efficient investigation of uncertainty propagation from process parameters to material microstructures, then to macrolevel mechanical properties through a combination of advanced AM multiphysics simulations and data-driven surrogate modeling. Model correction and parameter calibration for the constructed surrogate models using limited amounts of experimental data are discussed.

Formation Mechanism of Pores Inside Structure Fabricated by Metal-Based Additive Manufacturing

The powder bed fusion (PBF) technique is a metal-based additive manufacturing (AM) method in which metal powder is deposited on a substrate and melted by selective laser-beam irradiation. Given that the process and parameters of metal-based AM are complicated, there are various problems in high-precision fabrication. One of these is that although metal-based AM can be used for fabrication of high-density parts, pores can easily form inside the fabricated structure owing to process instabilities. Pore formation degrades the mechanical strength of the fabricated structure. Therefore, this study investigated the pore formation mechanism inside a structure fabricated by PBF. Pore suppression by controlling the substrate temperature was also evaluated. Small- and large-sized pores with diameters of 10 μm and more than 50 μm, respectively, were found. Furthermore, differences in pore formation in the cross-section of the fabricated structure were observed owing to a variation in the volume-specific energy density and substrate temperature. At a substrate temperature of 25°C, the number of pores decreased more at the upper position than at the lower position owing to repeated melting and solidification under the laser-beam irradiation. At a substrate temperature of 200°C, the number of pores decreased significantly more than at 25°C. Furthermore, as the substrate temperature increased, the wettability of the molten metal improved, resulting in smaller contact angles of the fabricated structure in the single-line track. In PBF, multiple lines are fabricated in each layer. At low substrate temperatures, interstices were formed between the lines owing to the low wettability of the molten metal. These interstices acted as the origins of pores when the next layer was fabricated. Heating the substrate made the surface of the structure smooth owing to the high wettability of the molten metal and a reduction in the number of pores. Therefore, the formation of large pores could be reduced by controlling the substrate temperature.

Metal additive manufacturing technologies: literature review of current status and prosthodontic applications.

To review the current metal-based additive manufacturing (AM) technologies, namely powder bed fusion (PBF) technologies, and their current prosthodontic applications. The PBF technologies reviewed are selective laser sintering (SLS), selective laser melting (SLM), and electron beam melting (EBM). The literature on metal AM technologies was considered, and the AM procedures and their current applications in prosthodontics were collated and described. Published articles about AM metal in dental care were searched (MEDLINE, EMBASE, EBSCO, and Web of Science). All studies related to the description, analysis, and evaluation of prosthodontic applications using metal AM technologies. AM technologies are reliable for many applications in dentistry, including metal frameworks for removable partial dentures (RPDs), overdentures, tooth- and implant-supported fixed dental prostheses (FDPs), and metal frameworks for splinting implant impression abutments. However, further studies are needed in future to evaluate the accuracy, reproducibility, and clinical outcome throughout function of AM technologies.

Study on control of porous structure built by metal-based additive manufacturing

Study on control of porous structure built by metal-based additive manufacturing

Microstructure and mechanical behaviour of additive manufactured Ti–6Al–4V parts under tension

Metal-based additive manufacturing technologies using electron or laser beams as a heat source for melting a metal powder or wire have been the subject of keen interest in recent years. At present paper a comparative analysis of the microstructure, strain response during tensile test and mechanical properties of Ti–6Al–4V samples produced by selective laser melting, electron beam melting or electron beam free-form fabrication were performed. A microstructural study using transmission electron microscopy revealed columnar prior β grains transformed into a lamellar α-morphology in the samples. According to X-ray diffraction study, the volume fractions of the β-Ti phase in the samples were equal to 2, 4 and 6 % respectively. It has been shown that the Vickers microhardness of SLM and EBM Ti–6Al–4V samples was similar (~5.4 GPa) while the hardness of EBF3 parts was significantly lower (4.5 GPa). The uniaxial stress-strain response of the Ti–6Al–4V samples fabricated by different additive manufacturing technologies were compared. Crystallographic (dislocation motion) and non-crystallographic (shear banding) deformation mechanisms of the loaded samples were studied by scanning electron microscopy and optical profilometry.

Dual process monitoring of metal-based additive manufacturing using tensor decomposition of thermal image streams

Additive manufacturing (AM) processes are subject to lower stability compared to their traditional counterparts. The process inconsistency leads to anomalies in the build, which hinders AM’s broader adoption to critical structural component manufacturing. Therefore, it is crucial to detect any process change/anomaly in a timely and accurate manner for potential corrective operations. Real-time thermal image streams captured from AM processes are regarded as most informative signatures of the process stability. Existing state-of-the-art studies on thermal image streams focus merely on in situ sensing, feature extraction, and their relationship with process setup parameters and material properties. The objective of this paper is to develop a statistical process control (SPC) approach to detect process changes as soon as it occurs based on predefined distribution of the monitoring statistics. There are two major challenges: 1) complex spatial interdependence exists in the thermal images and current engineering knowledge is not sufficient to describe all the variability, and 2) the thermal images suffer from a large data volume, a low signal-to-noise ratio, and an ill structure with missing data. To tackle these challenges, multilinear principal component analysis (MPCA) approach is used to extract low dimensional features and residuals. Subsequently, an online dual control charting system is proposed by leveraging multivariate T2 and Q control charts to detect changes in extracted low dimensional features and residuals, respectively. A real-world case study of thin wall fabrication using a Laser Engineered Net Shaping (LENS) process is used to illustrate the effectiveness of the proposed approach, and the accuracy of process anomaly detection is validated based on X-ray computed tomography information collected from the final build offline.

Peritectic titanium alloys for 3D printing

Metal-based additive manufacturing (AM) permits layer-by-layer fabrication of near net-shaped metallic components with complex geometries not achievable using the design constraints of traditional manufacturing. Production savings of titanium-based components by AM are estimated up to 50% owing to the current exorbitant loss of material during machining. Nowadays, most of the titanium alloys for AM are based on conventional compositions still tailored to conventional manufacturing not considering the directional thermal gradient that provokes epitaxial growth during AM. This results in severely textured microstructures associated with anisotropic structural properties usually remaining upon post-AM processing. The present investigations reveal a promising solidification and cooling path for α formation not yet exploited, in which α does not inherit the usual crystallographic orientation relationship with the parent β phase. The associated decrease in anisotropy, accompanied by the formation of equiaxed microstructures represents a step forward toward a next generation of titanium alloys for AM.

Manufacturing of Al and Mg nanocomposite microparticles

Lightweight high-performance metallic powders have great potentials for applications in metal-based additive manufacturing. To enhance their mechanical performance and extend their potentials in addictive manufacturing, ceramic nanoparticles are now incorporated into metal matrix microparticles. However, successful incorporation of high volume percentage nanoparticles and related scalable processing methods have not been developed yet. Here we show an effective manufacturing approach to produce aluminum (Al) and magnesium (Mg) nanocomposite microparticles with densely-packed titanium diboride and titanium carbide nanoparticles. Through a flux-assisted solidification processing method, up to 30 vol% of the nanoparticles were efficiently self-incorporated and self-dispersed into Al and Mg microparticles.

A life cycle assessment-based approach for evaluating the influence of total build height and batch size on the environmental performance of electron beam melting

Electron beam melting (EBM) is emerged as a promising metal-based additive manufacturing technology. The technique allows the build of complex and fully dense metallic parts by using an electron beam to melt metallic powder layer by layer. This technique has been applied efficiently in aeronautic and automobile sectors, as well as biomedical engineering. However, the environmental performance of EBM is still an open question. This paper aims to offer a more comprehensive understanding on environmental impacts related to EBM process. A methodology based on life cycle assessment (LCA) method is proposed to evaluate environmental impacts related to EBM. In particular, the influence of different parameters, such as total build height, batch size (i.e., number of parts per build), and material waste due to support structures on the environmental performance of EBM is discussed. By comparing environmental impacts between (EBM + finishing machining) and conventional approach (i.e., machining) approaches, which are used to manufacture a same mechanical part, the environmentally friendly area of each manufacturing approach is also determined. The results show that the (EBM + finishing machining) approach becomes the best option on the environmental point of view when the total build height decreases and the batch size is close to a full build configuration. On the other hand, the amount of material waste due to support structures in EBM does not significantly influence on the environmental performance of the EBM process.

Isolating the effects of surface roughness versus wall shape in numerically optimized, additively manufactured micro cooling channels

Metal-based additive manufacturing is a disruptive technology with the capability to transform industries. The increased design space the technology offers is enticing to designers, who are given an opportunity to develop components that exceed prior performance levels; optimization tools can be used to provide guidance. However, the nature of surface roughness in additively manufactured parts is highly irregular, and computational models generally cannot properly capture the surface morphology. How, then, can current numerical tools predict the performance of additively manufactured components, or aid designers in developing products that meet their needs?The current study aims to provide insight into the current capabilities of both design tools and the additive manufacturing process for microchannels intended for cooling components. A commercially-available optimization scheme was used to improve the design of four microchannel cooling configurations. These optimized geometries were printed using a metal-based additive manufacturing process; only some objective functions were achieved experimentally. In the current study, the same optimized geometries were printed using a stereolithography process, which built smooth channels as simulated in the optimization scheme. Experiments were performed to gather friction factor data, and results showed that the design intents were largely validated. Analyses of the metal test coupons alongside the plastic coupons showcase the direct effects of surface roughness: the influence of surface roughness on the performance of the channels was predominantly tied to the goal of the shape change.

Additive manufactured metallic implants for orthopaedic applications

Metallic implants are commonly used in various orthopaedic surgeries, like fracture fixation, spinal instrumentation, joint replacement and bone tumour surgery. Patients may need to adapt to the fixed dimensions of the standard implants. It may result in suboptimal fit to the host bones and possible adverse clinical results. The standard traditional implants may not address the reconstructive challenges such as severe bone deformity or bone loss after implant loosening and bone tumour resection. With the advent of digital technologies in medical imaging, computer programming in three-dimensional (3D) modelling and computer-assisted tools in precise placement of implants, patient-specific implants (PSI) have gained more attention in complex orthopaedic reconstruction. Additive manufacturing technology, in contrast to the conventional subtractive manufacturing, is a flexible process that can fabricate anatomically conforming implants that match the patients’ anatomy and surgical requirements. Complex internal structures with porous scaffold can also be built to enhance osseointegration for better implant longevity. Although basic studies have suggested that additive manufactured (AM) metal structures are good engineered biomaterials for bone replacement, not much peer-reviewed literature is available on the clinical results of the new types of implants. The article gives an overview of the metallic materials commonly used for fabricating orthopaedic implants, describes the metal-based additive manufacturing technology and the processing chain in metallic implants; discusses the features of AM implants; reports the current status in orthopaedic surgical applications and comments on the challenges of AM implants in orthopaedic practice.

A decision support system for the validation of metal powder bed-based additive manufacturing applications

The purpose of this research is to develop a computer-driven decision support system (DSS) to select optimal additive manufacturing (AM) machines for metal powder bed fusion (PBF) applications. The tool permits to evaluate productivity factors (i.e., cost and production time) for any given geometry. At the same time, the trade-off between feature resolution and productivity analysis is visualized and a sensitivity analysis is performed to evaluate future cost developments. This research encompasses a decision support system that includes a data structure and an algorithm which is coded in “MathWorks Matlab,” considering cost structures for metal-based AM (i.e., machine cost, material cost, and labor cost). Results of this research demonstrate that feature resolution has a crucial effect on the total cost per part, but displays decreasing impacts for higher build volume rates. Based on assumptions of business consultancies, productivity can be increased, resulting in a potential decline of cost per part of up to 55% until 2025. Using this DSS tool, it is possible to evaluate the most optimal AM production systems by selecting between several input parameters. The algorithm allows industry practitioners to retrieve information and assist in decision-making processes, including cost per part, total cost comparison, and build time evaluations for typical commercial metal PBF systems.

Numerical simulation of residual stress in laser based additive manufacturing process

Minimizing the residual stress build-up in metal-based additive manufacturing plays a pivotal role in selecting a particular material and technique for making an industrial part. In beam-based additive manufacturing, although a great deal of effort has been made to minimize the residual stresses, it is still elusive how to do so by simply optimizing the processing parameters, such as beam size, beam power, and scan speed. Amid different types of additive manufacturing processes, Direct Metal Laser Sintering (DMLS) process uses a high-power laser to melt and sinter layers of metal powder. The rapid solidification and heat transfer on powder bed endows a high cooling rate which leads to the build-up of residual stresses, that will affect the mechanical properties of the build parts. In the present work, the authors develop a numerical thermo-mechanical model for the measurement of residual stress in the AlSi10Mg build samples by using finite element method. Transient temperature distribution in the powder bed was assessed using the coupled thermal to structural model. Subsequently, the residual stresses were estimated with varying laser power. From the simulation result, it found that the melt pool dimensions increase with increasing the laser power and the magnitude of residual stresses in the built part increases.

Improving Experience

The use of metal-based additive manufacturing machines in hospitals is allowing medical professionals to pioneer the use of custom made implants for use within the NHS. Eureka! reports.

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagram delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.

3D printing of high-strength aluminium alloys.

Metal-based additive manufacturing, or three-dimensional (3D) printing, is a potentially disruptive technology across multiple industries, including the aerospace, biomedical and automotive industries. Building up metal components layer by layer increases design freedom and manufacturing flexibility, thereby enabling complex geometries, increased product customization and shorter time to market, while eliminating traditional economy-of-scale constraints. However, currently only a few alloys, the most relevant being AlSi10Mg, TiAl6V4, CoCr and Inconel 718, can be reliably printed; the vast majority of the more than 5,500 alloys in use today cannot be additively manufactured because the melting and solidification dynamics during the printing process lead to intolerable microstructures with large columnar grains and periodic cracks. Here we demonstrate that these issues can be resolved by introducing nanoparticles of nucleants that control solidification during additive manufacturing. We selected the nucleants on the basis of crystallographic information and assembled them onto 7075 and 6061 series aluminium alloy powders. After functionalization with the nucleants, we found that these high-strength aluminium alloys, which were previously incompatible with additive manufacturing, could be processed successfully using selective laser melting. Crack-free, equiaxed (that is, with grains roughly equal in length, width and height), fine-grained microstructures were achieved, resulting in material strengths comparable to that of wrought material. Our approach to metal-based additive manufacturing is applicable to a wide range of alloys and can be implemented using a range of additive machines. It thus provides a foundation for broad industrial applicability, including where electron-beam melting or directed-energy-deposition techniques are used instead of selective laser melting, and will enable additive manufacturing of other alloy systems, such as non-weldable nickel superalloys and intermetallics. Furthermore, this technology could be used in conventional processing such as in joining, casting and injection moulding, in which solidification cracking and hot tearing are also common issues.

Power–Velocity Process Design Charts for Powder Bed Additive Manufacturing

A current issue in metal-based additive manufacturing (AM) is achieving consistent, desired process outcomes in manufactured parts. When process outcomes such as strength, density, or precision need to meet certain specifications, changes in process variable selection can be made to meet these requirements. However, the changes required to achieve a better part performance may not be intuitive, particularly because process variable changes can simultaneously improve some outcomes while worsening others. There is great potential to design the additive manufacturing process, tailoring process variables based on user requirements for a given part. In this work, the tradeoffs between multiple process outcomes are formalized and the design problem is explored throughout the design space of process variables. Based on user input for each process outcome considered, P–V (power–velocity) process design charts are introduced, which map the process space and identify the best combination of process variables to achieve a user's desired outcome.

Redesigning a Reaction Control Thruster for Metal-Based Additive Manufacturing: A Case Study in Design for Additive Manufacturing

Prior research has shown that powder-bed fusion (PBF) additive manufacturing (AM) can be used to make functional, end-use components from powdered metallic alloys, such as Inconel® 718 superalloy. However, these end-use components and products are often based on designs developed for more traditional subtractive manufacturing processes and do not take advantage of the unique design freedoms afforded by AM. In this paper, we present a case study involving the redesign of NASA’s existing “pencil” thruster used for spacecraft attitude control. The initial pencil thruster was designed for and manufactured using traditional subtractive methods. The main focus in this paper is to (a) identify the need for and use of both opportunistic and restrictive design for additive manufacturing (DfAM) concepts and considerations in redesigning the thruster for fabrication with PBF AM and (b) compare the resulting DfAM thruster with a parallel development effort redesigning the original thruster to be manufactured more effectively using subtractive manufacturing processes. The results from this case study show how developing end-use AM components using specific DfAM guidelines can significantly reduce manufacturing time and costs while enabling new and novel design geometries.

Building and Improvement of High Performance Mold by Metal-based Additive Manufacturing

Building and Improvement of High Performance Mold by Metal-based Additive Manufacturing