Metal-based Additive Manufacturing Research Articles

Measurement of precision and quality characteristics of lattice structures in metal-based additive manufacturing using computer tomography analysis

Additive manufacturing, especially lattice structure fabrication, has garnered interest for its potential in high-precision aerospace and medical applications. Due to porosity and dimensional variation, obtaining mechanical characteristics and accuracy remains a research priority. This study addresses the current knowledge gap by providing a comprehensive methodology for the qualitative evaluation of cellular structures in additive manufacturing, focusing on the critical parameters of relative density, dimensional deviations, and porosity characteristics. The research utilizes advanced μCT analysis to investigate the impact of these factors on the overall quality and functionality of printed objects, particularly in metallic lattice structures fabricated using Laser Powder Bed Fusion (LPBF) technology. The cell structures were constructed using MS1 tool steel as the material base. An extensive investigation was carried out on a diverse array of lattice configurations, including Body-Centered Cubic (BCC), Diamond, Fluorite, Kelvin, Schwarz, and Fischer – Koch. It was observed that discrepancies between actual and designed relative densities in structures such as BCC, Diamond, and Kelvin indicate densification attributable to the manufacturing process, particularly in smaller cell structures. Furthermore, global porosity analysis demonstrated a clear relationship between increased density and reduced porosity. Optimal manufacturing conditions for minimizing defects and enhancing the precision and quality of additive manufactured metallic lattice structures were identified as a cell size of 3 mm and a 50 % relative density.

Reduction of Process Induced Porosity for Ultrafuse 316L through Parameter Optimization of Creality Ender 3 V2 and Makerbot Method X

Metal-based additive manufacturing (MBAM) has enabled rapid prototyping and one-off production, but the cost of equipment has limited widespread adoption. Recent developments in hybrid filaments and processes have created more accessible methods for MBAM, leveraging common fused filament fabrication (FFF) printers and Ultrafuse 316L metal filament. This technique has shown promise but suffered from large pore formations along parallel print paths. To reduce the formation of process-dependent pores, a design of experiments (DOE) was conducted to investigate the effects of varying extrusion parameters such as layer height, line width, and extrusion multiplier for tensile samples produced on a Creality Ender 3 V2 and MakerBot Method X. Characterization techniques included tensile testing, microhardness, density measurements, and optical microscopy; findings were compared to samples produced via laser-powder bed fusion (L-PBF) and from 316L plate. The Method X produced components with approximately 1% porosity and the Ender 4% porosity. Mechanical properties for both FFF printers were comparable to previous research, with an increase in tensile strength for the Method X. Despite the increased porosity in the Ender samples, only a 7% reduction in strength from the average yield in Method X samples (153.6 MPa) was observed. It was found that a combination of increased layer height and extrusion rate led to improved mechanical properties in parts printed on the Ender, while the default Makerbot settings resulted in the best overall performance for Ultrafuse 316L samples.

Research progress of metal-based additive manufacturing in medical implants

Abstract Metal-based additive manufacturing has gained significant attention in the field of medical implants over the past decade. The application of 3D-printing technology in medical implants offers several advantages over traditional manufacturing methods, including increased design flexibility for implant customization, reduced lead time for emergency cases, and the ability to create complex geometry shapes for patient-specific implants. In this review study, the working principles and conditions of metal 3D-printing technologies such as selective laser sintering, selective laser melting, and electron beam melting, as well as their applications and advantages in the medical field, are investigated in detail. The application scenarios and research status of non-degradable metals including titanium alloy, medical stainless steel, etc., and degradable metals like magnesium alloy are introduced as printing materials. We discuss the improvement of mechanical properties and biocompatibility of implants through surface modification, porous structure design, and the optimization of molding processes. Finally, the biocompatibility issues and challenges caused by the accuracy of CT imaging, fabrication, implant placement, and other aspects are summarized.

Fundamental requirements of a machine learning operations platform for industrial metal additive manufacturing

Metal-based Additive Manufacturing (AM) can realize fully dense metallic components and thus offers an opportunity to compete with conventional manufacturing based on the unique merits possible through layer-by-layer processing. Unsurprisingly, Machine Learning (ML) applications in AM technologies have been increasingly growing in the past several years. The trend is driven by the ability of data-driven techniques to support a range of AM concerns, including in-process monitoring and predictions. However, despite numerous ML applications being reported for different AM concerns, no framework exists to systematically manage these ML models for AM operations in the industry. Moreover, no guidance exists on fundamental requirements to realize such a cross-disciplinary platform. Working with experts in ML and AM, this work identifies the fundamental requirements to realize a Machine Learning Operations (MLOps) platform to support process-based ML models for industrial metal AM (MAM). Project-level activities are identified in terms of functional roles, processes, systems, operations, and interfaces. These components are discussed in detail and are linked with their respective requirements. In this regard, peer-reviewed references to identified requirements are made available. The requirements identified can help guide small and medium enterprises looking to implement ML solutions for AM in the industry. Challenges and opportunities for such a system are highlighted. The system can be expanded to include other lifecycle phases of metallic and non-metallic AM.

Cold spray forming: a novel approach in cold spray additive manufacturing of complex parts using 3D-printed polymer molds

AbstractThe solid-state additive manufacturing (AM) process cold spraying (CS) offers advantageous properties such as melt-free near-net-shape part fabrication and high deposition rates. Compared to other metal-based AM processes such as the powder bed fusion of metals (PBF-LB/M) or directed energy deposition (DED) processes such as laser metal deposition (DED-LB), CS features lower part resolution. One solution to increase the achievable level of detail is spraying onto removable molds. No study exists that investigates the general feasibility and manufacturing boundaries, from which design guidelines could be derived. In this paper, the applicability of material extruded and thermally bonded polymer (MEX-TRB/P) shapes, which is especially suitable for flexible low-cost production of small batches, as molds for cold spray additive manufacturing (CSAM) is investigated. For this purpose, material extruded thermoplastics are examined regarding their suitability for the CS process. Furthermore, geometrical and thus constructive restrictions of this new approach “Cold Spray Forming” (CSF) are analyzed using an industry-relevant use case. It was shown that the feasibility of this approach could be determined by the material value hardness of the sprayed polymer substrates.

Review on Multiscale Transport Phenomena in the Metal-Based Additive Manufacturing Process

Review on Multiscale Transport Phenomena in the Metal-Based Additive Manufacturing Process

Internal Heat Transfer Measurement on Metal-Based Additively Manufactured Channels Using a Transient Technique

Abstract The advancements in additive manufacturing (AM) of metals open new possibilities in the design of gas turbine parts. Especially the cooling efficiency of internal channels can be improved with more complex geometries. Naturally, AM channels have a higher surface roughness than conventionally manufactured parts, which influences the cooling air pressure loss as well as the heat transfer. Implementing novel cooling designs using AM can be possible only if the effect of increased surface roughness on the flow and on the heat transfer can be predicted with an appropriate accuracy. The objective of the current study was to measure these parameters experimentally in simple AM channels to build a database for designing complex and efficient cooling designs using the AM technique. A test rig and postprocessing method was elaborated to derive the local internal heat transfer distribution of metal-based AM channels. Six circular single channel coupons made by selective laser melting (SLM) were tested for Reynolds numbers ranging from 20,000 to 50,000. The coupon with the lowest relative roughness shows good agreement with the Dittus–Boelter correlation. All the other coupons show a consistent increase of internal heat transfer and flow friction with the increase of the internal surface roughness.

Special Issue on Recent Trends in Additive Manufacturing

Additive manufacturing (AM) has undergone rapid development in the past decade. Owing to its capacity to produce complex and functional parts in various industries, it has been recognized as a remarkable scientific and industrial technique. It is used to enhance weight-saving production and reduce the number of parts, with metal-based AM techniques in particular being recognized as the most promising AM techniques developed thus far. This is because of their high potential for direct production through the selective solidification of metal materials from three-dimensional, computer-aided design data. The medical, aerospace, and part molding industries are some of the many expected to reap particular benefit from the ability to produce high-quality models with reduced manufacturing costs and lead times. The objective of this special issue is to collect recent research works focused on recent trends in AM. This issue includes 7 papers covering the following topics: - Metal-based powder bed fusion using a laser beam (PBF-LB/M) - Wire and arc-based AM (WAAM) - Fused deposition modeling (FDM) This special issue is expected to help readers understand the recent trends in AM, leading in turn to further research on AM. We deeply appreciate the contributions of all authors and thank the reviewers for their incisive efforts.

A Novel Feature-Based Manufacturability Assessment System for Evaluating Selective Laser Melting and Subtractive Manufacturing Injection Moulding Tool Inserts

Challenges caused by design complexities during the design stages of a product must be coordinated and overcome by the selection of a suitable manufacturing approach. Additive manufacturing (AM) is capable of fabricating complex shapes, yet there are limiting aspects to surface integrity, dimensional accuracy, and, in some instances, design restrictions. Therefore, the goal is essentially to establish the complex areas of a tool during the design stage to achieve the desired quality levels for the corresponding injection moulding tool insert. When adopting a manufacturing approach, it is essential to acknowledge limitations and restrictions. This paper presents the development of a feature-based manufacturability assessment system (FBMAS) to demonstrate the feasibility of integrating selective laser melting (SLM), a metal-based AM technology, with subtractive manufacturing for any given part. The areas on the tool inserts that hold the most geometrical complexities to manufacture are focused on the FBMAS and the design features that are critical for the FBMAS are defined. Furthermore, the structural approach used for developing the FBMAS graphical user interface is defined while explaining how it can be operated effectively and in a user-friendly approach. The systematic approach established is successful in capturing the benefits of SLM and subtractive methods of manufacturing, whilst defining design limitations of each manufacturing method. Finally, the FBMAS developed was validated and verified against the criteria set by experts in the field, and the system’s logic was proven to be accurate when tested. The decision recommendations proved to correlate with the determined recommendations of the field experts in evaluating the feature manufacturability of the tool inserts.

Multi-fidelity Bayesian optimization to solve the inverse Stefan problem

In this work, we propose an efficient solution of the inverse Stefan problem by multi-fidelity Bayesian optimization. We construct a multi-fidelity Gaussian process surrogate model by combining many low-fidelity estimates of a solidification problem with only a few high-fidelity measurements. To solve the inverse problem, we employ the Gaussian process model in a Bayesian optimization approach based on a multi-fidelity knowledge gradient acquisition function. To account for the specific structure of the target function, we reformulate it as a composite function and thus significantly improve the stability of the optimization procedure. Target values can be switched easily, and previously obtained samples and surrogate models can be reused. The proposed method iteratively improves the recommended solution of the inverse problem. Explicitly adding recommended points of previous iterations to the solution procedure enhances the convergence properties of the algorithm.We demonstrate the applicability of the algorithm by solving the inverse problem for a planar solidification front in a single-fidelity setting. Process parameters are identified for targeted crystal-growth velocities during directional dendritic solidification. The relation between these velocities and process parameters, such as undercooling, thermal diffusivity, capillarity, and capillary anisotropy, defines the thermo-mechanical properties of the solidified part in metal-based additive manufacturing. Material design is based on the corresponding inverse problem. Cost-efficiency of solving the inverse problem is improved by introducing a fidelity hierarchy based on coarse-grid approximations of high-fidelity numerical simulations. The open-source simulation framework ALPACA for multiphase flows allows to generate data at all fidelities. We demonstrate the superior convergence properties of the presented multi-fidelity approach by comparison with an approach solely based on high-fidelity measurements of the tip velocity.

Millimeter-Wave Near-Field Evaluations of Polylactic Acid (PLA) Filament Used in Polymer-Based Additive Manufacturing (AM)

ABSTRACT Additive manufacturing (AM) remains to be a rapidly growing industry with applications that are extended beyond metals and to other materials, such as polymers, ceramics, and concrete, to name a few. However, advancement in the development of inspection techniques, particularly in-line nondestructive testing (NDT) methods, lags significantly. Most of the research in developing such methods has focused on metal-based AM. This paper investigates the efficacy of three high-resolution near-field millimeter-wave probes for detecting small voids in the feedstock polymeric filaments used for AM. The electromagnetic (EM) design and optimization of these probes are discussed in this paper. The design of the probes is based on concentrating the interrogating electric field of an open-ended waveguide in a small region corresponding to the area of a thin dielectric slab insert. This results in achieving a higher spatial resolution than when using only the open-ended waveguide. Extending the dielectric slab to an optimum value out of the waveguide makes the electric field more concentrated and potentially further improves the spatial resolution. These modifications also reduce the detection sensitivity as a function of increasing standoff distance. However, the spatial resolution of these probes varies more rapidly as the standoff distance increases. Subsequently, the efficacy of these three probes was studied and compared using a comprehensive set of numerical EM simulations at V-band (50–75 GHz). Afterward, three such probes were fabricated, at V-band (50–75 GHz), and were used to measure the reflection responses of the stock Polylactic Acid (PLA) filaments with a very small hemispherical surface void. Root-Mean-Squared-Error (RMSE), between reference and defective filaments and over the simulated and measured frequency range, was calculated as a criterion to compare the detection capability of the three probes in the entire frequency band. The results showed that at V-band (50–75 GHz) the spatial resolution of the standard open-ended rectangular waveguide is deemed sufficient detecting small surface voids of the stock PLA filaments.

Microstructure and mechanical properties of additively manufactured AlSi10Mg lattice structures from single contour exposure

Metal-based additive manufacturing techniques offer the ability to produce complex, near net shape parts that would be impossible or prohibitively expensive to manufacture via traditional casting and machining methods. Low density lattice structures for lightweight construction are one such example. However, there currently are no well-developed guidelines for designing and reliably manufacturing these structures. Literature on the topic is fragmented and usually only covers a limited aspect of the complex relations between process parameters, microstructure, lattice geometry and mechanical properties. This work covers the edge case of small diameter struts manufactured from AlSi10Mg via Laser Powder Bed Fusion (L-PBF) using the single contour exposure strategy, which is necessary to ensure sufficient geometric accuracy. Various cubic strut-based lattice geometries are manufactured using four laser parameter combinations corresponding to different area energy densities and beam offsets. The influence of process parameters and heat treatment on the microstructure and mechanical properties is investigated. Results show that the microstructure of filigree lattice struts can be tailored by the selection of process parameters, resulting in either uniform or core–shell structures depending on the laser power, while the macroscopic mechanical properties are mainly determined by the lattice geometry and relative density.

Multiphysics multi-scale computational framework for linking process–structure–property relationships in metal additive manufacturing: a critical review

ABSTRACT This review article provides a critical assessment of the progress made in computational modelling of metal-based additive manufacturing (AM) with emphasis on its ability to predict physical phenomena, concepts of microstructural evolution, residual stresses, role of multiple thermal cycles, and formation of multi-dimensional defects along with the achieved degree of experimental validation. The uniqueness of this article stems from the inclusion of comprehensive information on computational progress in the field of fusion-based, sintering-based, and mechanical deformation-based AM. A computational model's role in determining the process framework for the desired outcome of the set properties of the AM components is recognised while presenting the process-microstructure maps, thereby appraising computational ability towards the qualification of products. The inclusion of a detailed discussion on the bi-directional coupling of machine learning and physics-based computational models provides a futuristic roadmap for the digital twin of metal-based AM.

Design De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes

Abstract There is an urgent need for developing collaborative process-defect modeling in metal-based additive manufacturing (AM). This mainly stems from the high volume of training data needed to develop reliable machine learning models for in-situ anomaly detection. The requirements for large data are especially challenging for small-to-medium manufacturers (SMMs), for whom collecting copious amounts of data is usually cost prohibitive. The objective of this research is to develop a secured data sharing mechanism for directed energy deposition (DED) based AM without disclosing product design information, facilitating secured data aggregation for collaborative modeling. However, one major obstacle is the privacy concerns that arise from data sharing, since AM process data contain confidential design information, such as the printing path. The proposed adaptive design de-identification for additive manufacturing (ADDAM) methodology integrates AM process knowledge into an adaptive de-identification procedure to mask the printing trajectory information in metal-based AM thermal history, which otherwise discloses substantial printing path information. This adaptive approach applies a flexible data privacy level to each thermal image based on its similarity with the other images, facilitating better data utility preservation while protecting data privacy. A real-world case study was used to validate the proposed method based on the fabrication of two cylindrical parts using a DED process. These results are expressed as a Pareto optimal solution, demonstrating significant improvements in privacy gain and minimal utility loss. The proposed method can facilitate privacy improvements of up to 30% with as little as 0% losses in dataset utility after de-identification.

An Integrated Value-Addition in Supply Chain Network for Metal-based Additive Manufacturing

The increasing speed of product development and the ability to deliver complex, near-net-shaped engineering metal parts are key benefits of additive manufacturing (AM). Producing or replacing parts by leveraging AM relies heavily on supply chain (SC) functions. Inadequately managed SC could lead to lower productivity and process waste. Hence, the aim is to integrate value-adding activities into the SC network using the principles of lean manufacturing, such as Value Stream Mapping (VSM) and flowchart. A case study of a braking system manufacturer company in South Africa was used to exploit VSM and the flowchart for reducing process waste. The result of the case study revealed that the frequent use of expedited shipping increased transportation costs and lead times required excess raw material inventory, and lengthy supplier order processing. The approach demonstrated the need to adjust the business model by virtually synchronising the flow of information across the SC players in the configuration of the entire AM production and distribution activities. The study provides a framework that can guide AM organisations in improving efficiency and achieving significant cost and time savings.

A review on stress determination and control in metal-based additive manufacturing

Metal additive manufacturing (MAM) is an emerging and disruptive technology that builds three-dimensional (3D) components by adding layer-upon-layer of metallic materials. The complex cyclic thermal history and highly localized energy can produce large temperature gradients, which will, in turn, lead to compressive and tensile stress during the MAM process and eventually result in residual stress. Being an issue of great concern, residual stress, which can cause distortion, delamination, cracking, etc., is considered a key mechanical quantity that affects the manufacturing quality and service performance of MAM parts. In this review paper, the ongoing work in the field of residual stress determination and control for MAM is described with a particular emphasis on the experimental measurement/control methods and numerical models. We also provide insight on what still requires to be achieved and the research opportunities and challenges.

Metal-Based Additive Manufacturing Condition Monitoring: A Review on Machine Learning Based Approaches

The metal-based additive manufacturing (MAM) processes have great potential in wide industrial applications, for their capabilities in building dense metal parts with complex geometry and internal characteristics. However, various defects in the MAM process greatly affect the precision, mechanical properties and repeatability of final parts. These defects limit its application as a reliable manufacturing process, especially in the aerospace and medical industries where high quality and reliability are essential. MAM process monitoring provides a technical basis for avoiding and eliminating defects to improve the build quality. Based on of the nature of the MAM build defects, this article conducts a thorough investigation of monitoring methods, and proposes a machine learning (ML) framework for process condition monitoring. According to the structure of ML models, they are divided into shallow ML-based and deep learning-based methods. The state-of-the-art ML monitoring approaches, as well as the advantages and disadvantages of their algorithmic implementations, are discussed. Finally, the prospects of ML based process monitoring researches are summarized and advised.

Surface finish, microhardness and microstructure of laser metal deposited 17-4PH stainless steel

Laser metal deposition is a metal-based additive manufacturing technology. It is a very sensitive and complex process because of the different process parameters involved and the interrelations between these parameters. A thorough understanding of the underlying physics of the process is essential in developing a comprehensive database of the properties of materials processed with this technology. The main objective of this study was to investigate the effect of laser power on a laser-deposited 17-4 precipitation hardenable stainless steel alloy. The as-built microstructure, phase composition, microhardness and surface finish were analysed. The results show that a defect-free sample with good metallurgical bonding and minimal dilution can be produced using high laser power in the range 1400–2600 W and a scanning speed of 0.6 m/s. The microstructure in the clad layer was dominated by martensite and an improvement in surface finish and maximum hardness was observed with increased laser power. Significance: To fully benefit from the additive manufacturing technology, a comprehensive database of the material properties of alloys produced with this technology is required. This study expands on the body of knowledge related to the additive manufacturing of a 17-4PH stainless steel alloy, particularly highlighting the possibility of producing fully dense parts using higher laser power and scanning speed. These two parameters could significantly reduce the build time.

Influence of Planetary Ball Mill Parameters on Powder Flowability of AlSi10Mg with Niobium Carbide Using Central Composite Design (CCD)

The hypoeutectic aluminum alloy (AlSi10Mg) is a well-known candidate material used predominantly for its processability and inherent characteristics in metal-based additive manufacturing. Besides, transition metal carbide, such as niobium carbide (NbC), is added to the AlSi10Mg, enhancing its mechanical properties and preferably its wear resistance to the matrix. However, in additive manufacturing, the mixed powder’s flowability is a prerequisite for determining the final properties of the specimens. In this study, mixed powder flowability analysis was carried out through the regular mixing of AlSi10Mg with a varying weight percentage of NbC conducted in the planetary ball mill with different time, speed, and NbC composition following the Central Composite Design (CCD), with a total of 20 experiments. Here, regular mixing was preferred to retain the morphology of the AlSi10Mg (spherical shape) instead of ball milling, which contributes to the degradation of the powder’s shape and size. Finally, based on the combined analysis of apparent density (AD), tapped density (TD), and static angle of repose (SAoR), the flowability characteristics of the mixed powders (AlSi10Mg + X % NbC) were evaluated. The optimum combination (AlSi10Mg + X% NbC) was attained based on the composite desirability criteria.

Spatiotemporal Monitoring of Melt-Pool Variations in Metal-Based Additive Manufacturing

Additive manufacturing (AM) provides a higher level of flexibility to build customized products with complex geometries, by selectively melting and solidifying metal powders. However, wide applications of AM beyond rapid prototyping are currently limited by its ability to perform quality assurance and control. Advanced melt-pool monitoring provides a unique opportunity to increase information visibility in the AM process. Stochastic melt-pool variations are closely pertinent to the quality of an AM build. There is a pressing need to investigate the variances of melt pools along the temporal scanning path, as well as within a 3D spatial neighborhood of the focal point by the laser beam. This paper presents a stochastic modeling framework to characterize and monitor spatiotemporal variations of melt-pool imaging data, including tensor decomposition of high-dimensional data, additive Gaussian process modeling of low-dimensional profiles as random variables, and hypothesis testing via the construction of confidence boundary for statistical process monitoring. Experimental results show the effectiveness of tensor decomposition for spatiotemporal monitoring of melt-pool variations in the metal-based AM process.