Additive Manufacturing Components Research Articles

Active and Passive Thermal Management in Wire Arc Additive Manufacturing

This article presents innovative approaches for managing residual stresses and distortion in additive manufacturing (AM) of metal components (baseplate material: EN8; filler wire material: ER70S-6). The experiments are conducted with two approaches for thermal management—passive and active. The passive approach of experiments is performed by varying the selected process parameters to study their effect on residual stresses and distortion. The chosen parameters are current, torch speed, geometry, continuous or a delay in the deposition, and cooling arrangement. Based on the understanding gained from the passive approach, the active approach of thermal management was implemented by insulating the substrate with and without adaptive current and heating the substrate. The experimental results were corroborated with the simulation to understand the process better. A comparative study for hardness was made based on the T8/5 extracted from the simulation. These experiments and simulations endorse passive and active thermal management as effective tools that can alter the distortion and residual stress pattern and the mechanical properties of an AM component. The investigation concludes that the process parameters that lead to higher heat input vis-à-vis an increase in current or a decrease in speed increase the distortion. On the other hand, the parameters that affect the rate of heat distribution vis-à-vis torch speed and geometry affect the residual stresses. When current, traverse speed and a/b ratio were kept the same, active thermal management with a heated base reduced distortion from 1.226 mm to 0.431 mm, a 65% reduction compared to passive thermal management. Additionally, the maximum residual stress was reduced from 492.31 MPa to 250.68 MPa, with residual stresses decreasing from 418.57 MPa to 372 MPa. Overall, active thermal management resulted in a 63% reduction in distortion, lowering it from 1.35 mm to 0.50 mm using external heating. The components that are difficult to complete because of the in-process distortion are expected to be manufactured with thermal management, e.g., heating the substrate is an effective measure to manage the in-process distortion. Thermal management techniques depend on geometry; for instance, a concave surface, because of self-heating, reduces the cooling rate and has relatively less variation in hardness.

Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms

The design against fatigue failures of Additively Manufactured (AM) components is a fundamental research topic for industries and universities. The fatigue response of AM parts is driven by manufacturing defects, which contribute to the experimental scatter and are strongly dependent on the process parameters, making the design process rather complex. The most effective design procedure would involve the assessment of the defect population and the defect size distribution directly from the process parameters. However, the number of process parameters is wide and the assessment of a direct relationship between them and the defect population would require an unfeasible number of expensive experimental tests. These multivariate problems can be effectively managed by Machine Learning (ML) algorithms. In this paper, two ML algorithms for assessing the most critical defect in parts produced by means of the Selective Laser Melting (SLM) process are developed. The probability of a defect with a specific size and the location and scale parameters of the statistical distribution of the defect size, assumed to follow a Largest Extreme Value Distribution, are estimated directly from the SLM process parameters. Both approaches have been validated using literature data obtained by testing the AlSi10Mg and the Ti6Al4V alloy, proving their effectiveness and predicting capability.

Weldability of 316L Parts Produced by Metal Additive Manufacturing

The processes of metal additive manufacturing (AM) are no longer confined to rapid prototyping applications and are seeing increasing use in many fields for the production of tools and finished products. The ability to design parts with practically zero waste, high precision, complex geometry, and on-demand fabrication are among the advantages of this manufacturing approach. One of the drawbacks of this technique is the productivity rate, as the parts are made layer by layer, which also increases the production cost. Moreover, even the working space is limited, especially for the powder bed fusion technique. In view of these disadvantages and in order to guarantee the profitability of this process, it should be oriented to the production of complex components that have a limited volume with a design adapted to additive manufacturing. One solution with which to circumvent these drawbacks is to combine the 3D printing process with conventional manufacturing processes. When designing products, one may choose to use additive manufacturing to create locally complex parts and assemble them with parts produced by conventional processes. On the other hand, and due to the limited AM printing chamber space, it may be necessary to print large parts in multiple smaller parts and then assemble them. In order to investigate the weldability of stainless steel 316L parts produced by laser powder bed fusion (L-PBF), the mechanical behavior of different welding assemblies is tested. Five configurations are studied: non-welded AM specimens, two AM parts welded together, one AM part and one laser cut part welded together, two laser-cut parts welded together, and non-welded laser cut specimens. Welding is performed using the Pulsed Gas Metal Arc Welding process (GMAW-P). Specimen strength is assessed through static and fatigue tests. The results demonstrate that 316L AM parts are weldable, and the tensile and fatigue properties of L-PBF 316L welded components and welded laser cut components are comparable. GMAW-P welding led to lower fatigue results for AM components than for other configurations, but the difference is not important. It was observed that welding defects may have a direct impact on mechanical properties.

Tailoring the theory of critical distances to better assess the combined effect of complex geometries and process-inherent defects during the fatigue assessment of SLM Ti-6Al-4V

This work aims to apply the Theory of Critical Distances (TCD) to the fatigue assessment of additively manufactured (AM) Ti-6Al-4V material produced via the selective laser melting (SLM) process. Modified alternatives to traditional TCD methods are considered. In this sense, it is sought to develop a fatigue prediction model that is better suited to assessing the impact of multiple stress-rising features which are located in close proximity to each other. Hereby, consideration has been given to modelling process-inherent surface roughness in combination with an internally positioned artificial defect, shaped as a feature that is reminiscent of a pore. Simultaneously, the research also seeks to circumnavigate a potential issue with respect to the current TCD methodology. This concerns the matter of applying TCD practices to components whereby the area of interest for conducting stress-distance analytics is on a size scale that is smaller than that of the critical distance length parameter itself. Several different strategies were attempted as a way to try and achieve meaningful modifications to the TCD process. Results show that it is possible to overcome such challenges that can often present themselves during the fatigue appraisal of AM metal parts. In this sense, the optimal novel strategy that was experimented with returned average error margins of 13.7% or better. It is anticipated that such models may assist in further optimising the accuracy of service life evaluation for metallic AM components that are intended for industry.

Effect of Hardening Temperature on Maraging Steel Samples Prepared by Direct Metal Laser Sintering Process

This paper deals with the application of the direct metal laser sintering (DMLS) process, which already has a dominant position in the area of additive manufacturing (AM). This DMLS technology is used in many branches of industry and medicine, especially in piece production, small series, and prototypes. The portfolio of used metal powder materials includes aluminum alloys, austenitic steels, maraging steels, special alloys of nickel and titanium. The properties of these products are very often improved by further heat treatment after printing, such as a hardening process, by which microstructure and hardness can be increased. Heat treatment processes of metal AM components are already described, but experiments focused on optimization of these processes are still missing. In the article, the maraging steel samples printed by the DMLS method are subjected to testing after hardening processes, which differ by reducing the maintaining time at a defined temperature, recommended by the manufacturer. The result of the evaluation will be the reaching of similar results, which are set by the powder manufacturer, however, with shorter time of samples treatment. Therefore, the elevated temperature is selected, with the purpose of monitoring the shortest possible time of a temperature impact. The experimental temperature was set 590 °C with different durations at this temperature, for 1, 2, 3, 4, 5 and 6 h. The cooling process runs controlled in the furnace or in the still air. The maintaining time proved to be the most ideal already at 1 h exposure and cooled in the still air, where a higher hardness value of around 50 HRC was reached. During the resulting microstructure evaluations, fine carbids and martensitic lamellae were observed. More uniform and finer lamellar microstructure occurred at 5 and 6 h temperature intervals.

Automated Trajectory Generation for Region of Interest Computed Tomography using Industrial Robots

Computed Tomography (CT) is widely used as reference non-destructive testing method (NDT method) to analyze the inner structure of safety-critical parts e.g., in automotive or aerospace industry. Especially additive manufactured (AM) components often with their bionic design will lead to very complex structured parts, which are light weighted and thus will reduce the CO2 emission of cars or airplanes respectively. For metallic AM components produced in a powder bed fusion process pores, inclusions or lack-of-fusion errors (LoF) may occur typically. The latter one arises if powder could not be melted properly, i.e., it is an inclusion of loose powder inside the part. Such LoF are very low in contrast compared to the surrounding solid material and therefore harder to detect the smaller they are and the thicker the penetrated material is. Monitoring systems integrated in the 3D-Printer help to detect indications of where a defect could have occurred. Exactly on each of these predefined positions the CT-System will do a region-of-interest scan (ROI scan). In this study we are using an industrial robot as manipulator of the component. Its dedicated trajectory using only the optimal projection directions of the current ROI is derived by using our newly developed Digital Twin containing the robot, the AM component and the X-Ray imaging chain. We will show the integration of an automated trajectory planning into a robotic CT system for ROI scans of complex AM parts.

Potentials of Numerical Methods for Increasing the Productivity of Additive Manufacturing Processes

Thanks to the layer-by-layer creation of components, additive manufacturing (AM) processes enable the flexible production of components with highly complex geometries, that were previously not realizable or only with very great effort. While AM technologies are very widespread in the research sector, they have so far only been used industrially in a few individual areas of application. The manufacturing costs are one reason for this. In this work, a new approach for the optimized arrangement of components in the building box and its potential for reducing the manufacturing costs are presented, illustrated by a selected example, and a discussion. Three types of cylinders, which differ in geometry and/or inclination, are required in quantities of around 1000 each. The optimization aims at an arrangement with the smallest possible number of printing jobs. Compared to the solution obtained by the current automatic software tool that is based on the bounding box method, the optimized arrangement leads to a 70% increase in the number of components on a building platform or, in other words, to a 44% reduction in the number of building platforms needed to manufacture 980 components of each type. Finally, a three-step method is proposed, to optimize the manufacturing preparation for AM components automatically in the future.

Prediction of high cycle fatigue strength for additive manufactured metals by defects incorporated crystal plasticity modeling

The durability assessment of additive manufacturing (AM) components is an open issue due to the large scatter in their high cycle fatigue performance. The intrinsic defects inherited from AM processes, especially the irregular defects, pose great challenge to numerical modeling of AM metals and prediction of their fatigue behavior. In this paper, a computational method is proposed to predict the high cycle fatigue (HCF) properties of AlSi10Mg alloy fabricated by laser powder bed fusion (LPBF). A virtual laboratory framework including model generation, crystal plasticity calculation and fatigue performance evaluation is established. In the framework, a representative volume element (RVE) model integrating the polycrystal matrix and defects is developed; the defects are reconstructed based on X-ray Computed Tomography (XCT) measurement and then incorporated into the synthetic polycrystal matrix as the second phase; a crystal plasticity (CP) model using a FFT solver is employed to calculate the mechanical response under cyclic loading; finally, the fatigue strength is determined by using the staircase method. The proposed method well predicts the varied fatigue strengths of specimens containing different defects.

Development of a numerical simulation model for self-piercing riveting of additive manufactured AlSi10Mg

Laser additive manufacturing processes are used for the production of highly complex geometric structures due to their high geometric freedom. Additive manufacturing processes, in particular powder-based selective laser melting, are used to produce metallic additive manufactured components for the automotive and aerospace industries. Different materials are often joined together to realize sustainable lightweight construction. The production of such mixed construction joints is often realized using mechanical joining technology (e.g. self-piercing riveting). However, there is currently very little experience with the mechanical joining of metallic additive manufacturing components. Furthermore, there is insufficient knowledge about the effects that occur during the mechanical joining of additive manufacturing components. In this article, a method is presented to investigate the joinability of additively manufactured components with conventionally manufactured components using a numerical simulation of the self-piercing riveting process. For this purpose, the additive manufacturing materials are characterized experimentally, the simulation model is configured, and the joining process with additive manufacturing materials is represented in the numerical simulation. Furthermore, the influence of the building direction on the mechanical properties is shown using miniature tensile specimens. Besides the configuration of the simulation model, the influence of heat treatment on the self-piercing riveting process is presented.

Retraction Note: Microstructure and Tensile Properties of Wire Arc Additive Manufactured Low Carbon Steel Components Made by Pulsed Current Arc Welding Processes

Retraction Note: Microstructure and Tensile Properties of Wire Arc Additive Manufactured Low Carbon Steel Components Made by Pulsed Current Arc Welding Processes

Nondestructive Assessment of Quality Characteristics of As-Built Additive Manufacturing Aeronautical AlSi10Mg and Inspection of Offset.

AlSi10Mg is widely used in additively manufacturing aeronautical lattice and honeycomb structures. The unclear complex quality characteristics has been an obstacle of nondestructive testing of additive manufacturing (AM) AlSi10Mg, especially at its as-built state. This article attempts to reveal the quality characteristics of as-built AM aeronautical AlSi10Mg based on its conductivity characteristics. Experimental results show that the quality of manufacturing is nonuniform. The nonuniformity of each sample surface is systematically evaluated. Along the building direction, the quality is best in the initial few layers and decreases as the built height increases. On this basis, a reference curve is obtained and used to describe the quality characteristics of as-built AM component along the building direction. Based on the quality characteristics, the inspection of small offsets caused by accidental failures during the manufacturing process is carried out. The offsets not <37 μm are detected with the help of the reference curve, whereas they are not discovered by the radiography testing. It proves that the proposed method of offset inspection is highly sensitive.

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.

Cryogenic tensile behavior of laser additive manufactured CoCrFeMnNi high entropy alloys

In the present work the microstructural evolution, tensile properties, and deformation behavior of additively manufactured equiatomic CoCrFeMnNi high entropy alloy (HEA) were investigated at a cryogenic temperature (77 K). Metal additive manufacturing components are known to have exceptional yield strength, without compromising ductility. Here, laser powder bed fusion (LPBF) and direct energy deposition (DED) additive manufacturing processes were chosen as the fabrication techniques and wrought CoCrFeMnNi HEA was also fabricated for comparison. The tensile behaviors at different temperatures (298 K and 77 K) indicated temperature-dependent strength and ductility in all the samples. In particular, the HEA processed by DED exhibited excellent strength and ductility, with exceptional strain hardening, compared to the LPBF and wrought samples at 77 K. Post-tensile microstructures revealed the formation of deformation twinning in addition to dislocation slip as deformation mechanism at 77 K. Our results suggest that cellular structure plays an essential role in yield strength. Moreover, the interactions of the deformation twins and cellular dislocation structures increase strain hardening and help retard plastic instability, thereby improving ductility at 77 K.

Laser Cladding With Combined NIR and Blue Diode Laser Including In-Line Atomic Emission Spectroscopy

For Directed Energy Deposition processes (DED) lasers in the near infrared (NIR) as well as in the infrared (IR) range are predominantly used. Recent developments have also made high-power lasers in the visible spectrum available. As the DED process is used for cladding of surfaces, repairing and additive manufacturing of components, process monitoring and control methods are necessary to ensure a consistent manufacturing quality. Optical emission spectroscopy (OES) of the process radiation can provide information on process conditions and the deposition layer during DED processes. However, DED processes are in the heat conduction regime and superimposed broad spectral emissions dominate the wavelength specific signals. The object of this work is to compare the process behavior using a NIR and blue diode laser separately as well as in combination. The influence of the laser wavelength on the cladding result as well as on the emitted process radiation is to be determined. Therefore, single tracks of Co-based powder (MetcoClad21) were clad on an S235 base material by using each laser source separately as well as in combination. Both laser beams were combined within a single hybrid optic. While the scan speed and powder feed rate remained constant, the laser power was varied. Single spectra have been recorded from the process using a spectrometer. Single spectra are sorted and element lines were identified. Only non-ionised elements could be detected, with chromium appearing frequently. It was shown that comparable results in terms of cladding quality can be produced independently from the laser wavelength. In fact, less laser power (app. 30% less, 1 kW at 980 nm (NIR) compared to 0.7 kW at 450 nm (blue)) was needed aiming for comparable results in geometrical factors (as dilution, height, depth, width) and homogeneity (chemical composition distribution) by using blue laser irradiation. Furthermore, more spectrometric signals (approx. 2–28 times more) were detected compared to experiments using only NIR irradiation with the same laser power. This effect is particularly high at low laser powers and decreases with increasing power. Hence, it is possible to enable in-line process analysis by adding blue laser irradiation to the mix of the beam.

Magnetic Behavior of a Laser Deposited Fe–Ni–Mo Alloy

Magnetic shielding in spacecraft is a mission‐critical issue that must be addressed in a timely and effective manner. The high permeability of Fe–Ni–Mo alloys, makes them excellent candidates for magnetic shielding. This article explores a new and innovative approach, enabled by additive manufacturing (AM), to design, build, and test geometrically complex magnetic shields. A Fe–79.7Ni–4.1Mo alloy is additively manufactured using blown powder laser‐directed energy deposition (DED). AM build conditions are explored in the production of magnetic test rings and magnetic shield prototypes. Magnetic hysteresis test data are obtained, allowing for the determination of magnetic permeability, saturation, and coercivity. Detailed microstructural characterization is carried out. Three different prototype shield designs are printed and magnetic shield attenuation data is obtained. The magnetic field attenuation (shield effectiveness) obtained for the AM components is comparable to wrought equivalents. The values reported here for the magnetic permeability are the highest, and that for the magnetic coercivity the lowest, for any blown powder DED‐printed material currently known. The magnetic behavior is discussed with regard to grain size and orientation, as well as grain boundary effects, with all of these attributes contributing to the ultimate performance.

Investigation on a predetermined point of failure for stainless steel 316L pressure loaded components made by laser powder bed fusion through stress analysis and experimental testing

The qualification process of pressure vessels in general is subject to governmental restrictions. Thus, introducing additive manufacturing (AM) components to the market is challenging due to incomplete standardization. To increase component safety and trust, predetermined points of failure can be integrated by design using direct manufacturing methods. A predetermined point of failure using a surface notch is one option to avoid dangerous part failure (e.g. explosion) and increase safety. For implementation, a design approach with experimental proof of concept is striven to guide manufacturers and demonstrate the behavior of the material and the component as a whole. Finite element analysis is used for investigations on the influence of surface notch geometries applied to wall structures on structural stress super-elevation. Analytical strength assessment using FKM guideline and experimental validation of PBF-LB/M-manufactured specimen behavior by static axial tensile and burst testing is carried out. Fracture surface and plastic elongation evaluation using light microscopy and 3-D surface scanning clarify the material behavior. The analytical and experimental approval of an integrated predetermined point of failure for static overload is achieved without reducing the maximum burst pressure value. The integration fulfills all theoretical requirements for structural strength. All tested specimens meet the expectations regarding static strength and failure behavior. As expected, component elongation decreases using surface notches for failure provocation. Results lead to a proposed guideline for the application of an integrated predetermined point of failure by the use of a surface notch.

The influence of surface finish and build orientation on the low cycle fatigue behaviour of laser powder bed fused stainless steel 316L

Additive manufacturing (AM) processes are currently under consideration for marine based components, predominantly due to the numerous benefits that the techniques have to offer over more conventional manufacturing routes. However, there are multiple engineering challenges and questions associated with the introduction of AM based parts into safety critical applications related to the mechanical behaviour of such components. One of the main factors influencing the cyclic performance of a component is the surface finish. As-built AM parts typically exhibit a rough surface owing to partially melted powder being present at the surface and the layer-by-layer nature of the AM process, which together will likely hinder the fatigue response of the component. This behaviour is further influenced by the build orientation of the AM component, with alternative orientations providing a different surface profile alongside a contrasting microstructural morphology. Therefore, alternative finishing methods have been explored to maximise the fatigue performance of components whilst also considering cost and time. This research will explore the low cycle fatigue (LCF) behaviour of laser powder bed fused (LPBF) stainless steel 316L (SS316LN) built in two principal orientations (vertical (90°) and diagonal (45°)) and subsequently subjected to several post-manufacture finishing processes in order to identify the optimal finish for mechanical performance. The mechanical results are supported by microstructural, fractographic and advanced surface profilometry assessments, which have revealed that surface roughness can not be considered alone to be the controlling influence on LCF behaviour. An as-built surface finish will inherently provide a greater number of surface breaking stress raisers, however, a novel mass finishing polishing procedure has been found to produce a similar effective stress concentration factor compared to conventional longitudinal polishing, offering a more viable and less time consuming alternative. Several other key factors must also be considered when assessing the fatigue performance of LPBF built materials, including build direction and the resulting grain orientation, density of the additive structure and the material's sensitivity to the presence of notched features at the surface. Finally, the generated mechanical data has also been interpreted through empirical modelling, and the various data sets have been successfully correlated to enable longer fatigue life predictions.

Mechanical properties characterisation of metallic components produced by additive manufacturing using miniaturised specimens

ABSTRACT The various process-specific differences in techniques compared to traditional techniques can produce significantly different mechanical behaviour in additively manufactured (AM) parts compared to traditional bulk counterparts. Components produced by AM are built layer by layer via localised melting. Therefore, both location- and orientation-dependent properties can be expected. Since many AM parts take advantage of the design and topology freedom provided by AM, properties characterisation with the use of standard specimens is not always possible, requiring the use of small-sized specimen techniques. In the current paper, three AM-produced IN-718, Ti-6Al-4V and H13 parts using electron beam powder bed fusion and laser powder bed fusion are evaluated. Local mechanical properties have been assessed with the use of mini-tensile tests that were developed for cases where limited amounts of material are available. The results obtained demonstrate the ability to measure location- and orientation-dependent properties in AM components using such approaches and highlight that additional work by the AM community remains in order to determine the source(s) of such differences.

Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts

The fatigue response of Additive Manufacturing (AM) components is driven by manufacturing defects - whose size mainly depends on process parameters - and by the resulting microstructure - mainly affected by heat treatments and process parameters. In the paper, Machine Learning (ML) algorithms are applied to estimate the fatigue response from AM process parameters and heat treatment properties. Feed-forward neural networks (FFNN) and physics-informed neural network (PINN) algorithms are designed and validated on literature datasets of AM AlSi10Mg alloy, proving the effectiveness of physics-based ML approaches in predicting the fatigue response of AM parts. Leveraging PINN interpretability, the authors analyse the relationship between process parameters and fatigue response.

The Effect of B4C Powder on Properties of the WAAM 2319 Al Alloy

With ER2319 and B4C powder as feedstocks and additives, respectively, a wire arc additive manufacturing (WAAM) system based on double-pulse melting electrode inert gas shielded welding (DP-MIG) was used to fabricate single-pass multilayer 2319 aluminum alloy. The results showed that, compared with additive manufacturing component without B4C, the addition of which can effectively reduce the grain size (from 43 μm to 25 μm) of the tissue in the deposited layer area and improve its mechanical properties (from 231 MPa to 286 MPa). Meanwhile, the mechanical properties are better in the transverse than in the longitudinal direction. Moreover, the strengthening mechanism of B4C on the mechanical properties of aluminum alloy additive manufacturing mainly includes dispersion strengthening from fine and uniform B4C granular reinforcing phases and fine grain strengthening from the grain refinement of B4C. These findings shed light on the B4C induced grain refinement mechanism and improvement of WAAM 2319 Al alloy.