Manufacturing Of Metallic Components Research Articles

Effect of Interlayer Machining Interventions on the Geometric and Mechanical Properties of Wire Arc Directed Energy Deposition Parts

Abstract Wire Arc Directed Energy Deposition (Wire Arc DED) has become a popular metal additive manufacturing technique for its capability to print large metal parts at a high deposition rate while being economically efficient. However, the Wire Arc DED process exhibits geometric inaccuracies resulting from the variability in the bead geometry and demonstrates heterogeneity in microstructure and mechanical properties. This study investigates the use of tailored periodic machining interventions during the Wire Arc DED process to address these shortcomings. The as-built geometry and surface finish, microstructure, and microhardness of multi-layer wall structures produced with and without machining interventions carried out at different temperatures are compared. The machining interventions are found to reduce the uncertainty in bead geometry evolution and significantly improve the surface roughness of the as-built walls, thus reducing the need for further post-processing of the wall surfaces. Although the microstructure constituents of the as-built wall structures with and without machining interventions are similar, the machining interventions result in finer grains in the interior of the part. Machining interventions are found to yield a statistically significant increase in microhardness, indicating increased strength compared to Wire Arc DED alone. In addition, the spread of the microhardness distribution is reduced in Hybrid-Wire Arc DED, indicating improved homogeneity of the grain size distribution compared to Wire Arc DED alone. The study shows that the proposed hybrid manufacturing technique has the potential to control and improve the geometric and mechanical properties of additively manufactured metal components.

CONTROL DE MICRO MECANIZADO NO CONVENCIONAL BASADO EN ANÁLISIS DE PARÁMETROS MULTIVARIABLES UTILIZANDO UN SISTEMA DE INFERENCIA DIFUSA

Non-conventional manufacturing processes, such as pulsed electrochemical machining, are strategies for manufacturing metal components that optimize tool life. This is achieved by implementing non-traditional techniques based on electrolysis to remove metal in a controlled environment without generating contact between the tool and the workpiece. These characteristics allow a reduction in the resources required for the development of components with complex morphology on high-strength materials in many application fields, such as aerospace, medical, and precision tools. However, the characteristic features of non-linearity in this kind of process increase the complexity of the development of automatic control through traditional algebraic techniques in the regulation of uniform material wear. In this context, a non-traditional solution based on techniques derived from artificial intelligence bounded by fuzzy logic and genetic algorithms is presented for the tuning and automatic control of the pulsed electrochemical machining process. Furthermore, the efficiency is evaluated by analyzing the over-cutting phenomenon and the precision in developing linear polygonal machining on a micrometer scale. Keywords: Modern manufacturing, fuzzy logic, automatic control, electrolysis, genetic algorithms.

The Potential of Additive Manufacturing of Metal Components to Reduce Environmental Impacts

The Potential of Additive Manufacturing of Metal Components to Reduce Environmental Impacts

Enhancing Wear Resistance in Functionally Graded Metallic Components: Insights from Nanoindentation and Mechanical Analysis.

To manufacture metallic components with high wear resistance, treatments such as nitriding and carburising followed by quenching and tempering (NQT and CQT, respectively) are applied to various types of steel to increase the hardness (H) of the friction surface. However, the wear mechanism of the resulting functionally graded materials has not been fully understood. In this study, specimens of industrial 99.82% pure iron treated with NQT at 913 and 1033 K, and CQT at 1203 K, as well as hot-rolled sheets without heat treatment were examined by performing nanoindentation tests to measure changes in their H, reduced Young's moduli (Er), elastic deformation energies (We), and plastic deformation energies (Wp) along the depth direction. The relationship between Wp/We and the elastic strain resistance (H/Er) can be expressed for all specimens via the equation Wp/We = -1.0 + 0.16 (H/Er)-1. Furthermore, the obtained H/Er av measured at 5 µm intervals based on the specimen profile and wear volume has a good correlation depending to the sliding distance, as confirmed by the results of the ring-on-plate sliding tests conducted for the carbon-treated, nitrogen-treated, and hot-rolled specimens. This study provides a new approach, using H/Er parameters to identify the dominant factors affecting wear resistance at the initial stage of wear that may contribute to the development of wear-resistant materials.

Research on the Deformation Prediction Method for the Laser Deposition Manufacturing of Metal Components Based on Feature Partitioning and the Inherent Strain Method

Laser deposition manufacturing (LDM) has drawn unprecedented attention for its advantages in manufacturing large-scale and complex metal components. During the process of LDM, a large thermal gradient is generated due to thermal cycling and heat accumulation. As a result, large residual stress and deformation are formed in the LDM metal components. Then, the dimensional accuracy of the metal components becomes poor. To achieve deformation control and increase dimensional accuracy, the deformation prediction of metal components is very meaningful and directional. However, the traditional thermoelastic–plastic method can only achieve deformation prediction for small-scale LDM metal components. Because of the low computational efficiency, it is extremely difficult to meet deformation prediction demand for large-scale metal components. Based on feature partitioning and the inherent strain method, a rapid deformation prediction method is proposed for large-scale metal components in this manuscript. Firstly, to solve the problem of poor consistency of formation quality due to the randomness of the partition process, the partitioning process was established according to typical geometric features. Secondly, the inherent strain values for different partitions were obtained by considering the effects of the extraction method, mesh size, equivalent value layer, and partition size on the inherent strain values. Then, using the inherent strain method, the deformation of large-scale components was predicted rapidly. Comparing the simulation results with the experimental results, the following conclusions were obtained. The deformation predicted by the method proposed in this manuscript is consistent with the deformations predicted using the traditional thermoelastic–plastic method and the experimental method. Significantly, applying the method proposed in this manuscript to predict the deformation of LDM metal components, computational efficiency is improved by 27.25 times compared with results using the conventional thermoelastic–plastic method.

A Design Strategy for Removing the Debinding and Sintering Gas in Additive Manufactured Samples of a Bronze/Polylactic Acid Filament

To lower the cost of additive manufacturing of metallic components, the goal of this work is to investigate and optimize a multi‐step process by material extrusion (MEX) of a polylactide filament loaded with bronze to remove the gas produced during the debinding and sintering steps. First, by adjusting the infill (10%, 50%, and 100%), and then by designing and constructing internal passages to aid in the expulsion of gases that occur during the debinding, a calibration cube is created. Additionally, the impact of the cooling period during the debinding is examined. To assess how the technique changes shape, sizes, and internal structure, all the samples are ultimately weighed, scanned, and cut. In order to reduce deformation occurred during the debinding and sintering gases, a new design method has been developed. The method consists of the generation of internal channels which connect the voids of the infill and allow gases to flow out through a central channel. As seen, samples with ejection channels and an intermediate infill (i.e., 50%) both exhibit better attributes.

Characterization of intrinsic interfaces between fibre-reinforced composites and additively manufactured metal for designing hybrid structures

The combination of additively manufactured metal components with thermoset fibre-reinforced composites provides the possibility to produce hybrid structures with increased functionality and reduced mass. The application in the high-performance sector, for example the implementation of such a hybrid structure in electric drive units in aviation, provides the potential to achieve the high power densities required. The challenges in this regard are the manufacturing, design and dimensioning of the interface between the two components regarding the technical requirements, such as the high temperature range. In this publication, metal specimens are manufactured using selective laser melting (SLM) and then pre-treated. The joint with the composite is obtained in the subsequent infiltration process when the composite part is manufactured. For the experimental characterization of the interface different combinations of fibre-reinforced composites and metals are used. Within roughness measurement the surface of the different materials due to the treatment were analysed and the intrinsic interfaces were microscopically examined. The joint strength is investigated in double lap shear test at different temperatures and the results are discussed based on the fabrication process and the characteristics of the hybrid interface. The results provide the basis for the future design and numerical description of the interfaces.

Deep learning approaches for instantaneous laser absorptance prediction in additive manufacturing

The quantification of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additively manufactured metal components. The geometry of a vapor depression formed during laser melting is closely related to laser energy absorption. This relationship has been observed by the state-of-the-art in situ high-speed synchrotron X-ray visualization and integrating sphere radiometry. These two techniques create a temporally resolved dataset consisting of vapor depression images and corresponding laser absorptance. In this work, we propose two different approaches to predict instantaneous laser absorptance. The end-to-end approach uses deep convolutional neural networks to learn implicit features of X-ray images automatically and predict the laser energy absorptance. The two-stage approach uses a semantic segmentation model to engineer geometric features and predict absorptance using classical regression models. While having distinct advantages, both approaches achieved a consistently low mean absolute error of less than 3.3%.

High-cycle fatigue life prediction of L-PBF AlSi10Mg alloys: a domain knowledge-guided symbolic regression approach.

The large scatter in high-cycle fatigue (HCF) life poses significant challenges to safe and reliable in-service assessment of additively manufactured metal components. Previous investigations have indicated that inherent manufacturing defects are a critical factor affecting the fatigue performance of the components, and the HCF life is significantly influenced by the geometric parameters of the critical defects inducing crack nucleation. Therefore, it is highly important to elucidate the correlation of the HCF life with the geometric parameters of critical defects. This study proposes a new fatigue life prediction model for laser additively manufactured AlSi10Mg alloys by including the combined effects of loading stress and defect geometries (size, location and morphology) in terms of domain knowledge-guided symbolic regression (SR). Domain knowledge is extracted from the semi-empirical Murakami, Z-parameter and X-parameter fatigue life models to establish the variable subtrees. The results show that compared with these semi-empirical models, the domain knowledge integration-based SR model has higher prediction accuracy and generalization ability. Moreover, compared with traditional 'black box' machine learning models, SR excels at balancing prediction accuracy and model interpretability, which provides useful insights into the relationship between fatigue life and defect geometries. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

Correlation of Energy Density and Manufacturing Variables of AA6061 through Laser Powder Bed Fusion and Its Effect on the Densification Mechanism

Aluminum alloy processing via additive manufacturing (AM) technologies has increased in usage during the last decade. AM now enables manufacturing complex geometries not previously achieved through traditional manufacturing. Aluminum is usually processed using laser powder bed fusion (LPBF) technologies, which are used to manufacture metallic components of high geometrical complexity and dimensional accuracy and good mechanical, electrical, and chemical properties, which is why this technology is quite popular at industrial levels. To further develop quality control systems and new aluminum alloys using LPBF, there is a need to establish a predictive relationship between the parameters of the material. A study was carried out to investigate the relationship between energy density and process parameters such as laser power, scan speed, hatch spacing, scan pattern, and laser focus and its influence on the densification mechanism in additively manufactured components with aluminum alloys. AA6061 was selected due to its wide usage in different industries, given its low density and high mechanical performance. Relative density was analyzed using the Archimedes principle, and the quality and morphology of the AA6061 powder were analyzed through metallographic analysis. The process parameter selection was performed according to the best results obtained according to the laser power and energy density factors. The best manufactured samples had an energy density between 30 and 40 J/mm3, with relative densities above 99%.

Segmentation-based closed-loop layer height control for enhancing stability and dimensional accuracy in wire-based laser metal deposition

Laser metal deposition (LMD) with wire is a versatile additive manufacturing process used for the production of near-net-shape metal components as well as for modification and repair applications. Major advantages of using wire as the feedstock material as opposed to powder include the elimination of hazardous metal dust in the process environment and the lower costs. However, the process is highly sensitive to disturbances and requires a significant effort for parameter tuning. Thus, in order to achieve a stable process as well as defined geometric properties over many layers, dedicated approaches for monitoring and control are essential. In particular, maintaining a constant distance between the workpiece surface and the deposition head is an important prerequisite for process stability. Therefore, in this work, a layer height control system for wire-based LMD was implemented. The objective was to ensure a constant layer height corresponding to the specified height increment even in case of disturbances. Using a laser line scanner, the height profile of the part was obtained after each deposited layer. The weld beads were then divided into small segments to obtain a fully discrete height profile, and the wire feed rate for the next layer was set by an individual controller within each segment. The implemented control system was tested for its effectiveness under different disturbances, whereby significant height differences could be fully compensated within few layers. The developed segmentation approach was found to be an effective method to ensure the dimensional accuracy of LMD components. This work thus constitutes a major contribution to the advancement of fully automated additive manufacturing of metallic components.

Machine Learning-Enabled Quantitative Analysis of Optically Obscure Scratches on Nickel-Plated Additively Manufactured (AM) Samples.

Additively manufactured metal components often have rough and uneven surfaces, necessitating post-processing and surface polishing. Hardness is a critical characteristic that affects overall component properties, including wear. This study employed K-means unsupervised machine learning to explore the relationship between the relative surface hardness and scratch width of electroless nickel plating on additively manufactured composite components. The Taguchi design of experiment (TDOE) L9 orthogonal array facilitated experimentation with various factors and levels. Initially, a digital light microscope was used for 3D surface mapping and scratch width quantification. However, the microscope struggled with the reflections from the shiny Ni-plating and scatter from small scratches. To overcome this, a scanning electron microscope (SEM) generated grayscale images and 3D height maps of the scratched Ni-plating, thus enabling the precise characterization of scratch widths. Optical identification of the scratch regions and quantification were accomplished using Python code with a K-means machine-learning clustering algorithm. The TDOE yielded distinct Ni-plating hardness levels for the nine samples, while an increased scratch force showed a non-linear impact on scratch widths. The enhanced surface quality resulting from Ni coatings will have significant implications in various industrial applications, and it will play a pivotal role in future metal and alloy surface engineering.

Effect of TaC on microstructure and mechanical properties of 316L stainless steel by selective laser melting

Selective laser melting (SLM) is a rapid processing technology used to directly manufacture metal terminal components. TaC is often utilized to enhance mechanical properties of metal due to its excellent performance. In this study, SLM was employed to manufacture TaC/316 L composites with varying TaC volume content. The effect of TaC content on the microscopic appearance and mechanical properties of SLM-316 L stainless steel was then explored. The TaC admixture was efficiently and effectively distributed on 316 L powder particle surfaces using short-duration high energy ball milling. Under the optimal parameters for SLM, the relative densities of the specimens reached over 99.0% because of the high wettability of 316 L with TaC. Moreover, grain refinement was observed after microstructural analysis due to interactions between the TaC and 316 L. Tensile, Charpy impact, and microhardness tests demonstrated that the adjunction of 0.5 vol% and 1.5 vol% TaC provided a significantly enhanced yield strength (increased by 7.4% and 30.9%), ultimate tensile strength (increased by 5.6% and 27.1%), and microhardness (increased by 9.9% and 22.7%). The impact toughness (reduced by 20.3% and 51.4%) and ductility (reduced by 23.1% and 39.5%) were maintained.

Microstructural analysis of the influences of platform preheating and post-build heat treatment on mechanical properties of laser powder bed fusion manufactured AlSi10Mg alloy

Platform preheating is a common strategy towards reducing residual stress and distortion of additively manufactured metal components by powder bed fusion – laser beam (PBF-LB) due to the lowered thermal gradient. However, platform preheating can lead to in-situ ageing for aluminium alloy components in the building process, which will affect the mechanical properties. In this work, the microstructures of PBF-LB AlSi10Mg built on preheated (150 °C) and un-preheated (ambient) platforms have been characterized in detail by various electron microscopy techniques in order to better understand the influence of platform preheating on mechanical properties. Furthermore, the influences of T5 ageing and solution treatment plus ageing (T6) on microstructure and mechanical properties have also been examined. Platform preheating to 150 °C improves strength, which is associated with the in-situ precipitation of granular or rod-shaped Si phase and needle-like β'' phase. To the best of our knowledge, this is the first report on the observation of β'' precipitates in PBF-LB Al–Si–Mg alloys in the as-built state. Upon T5 treatment, the ambient platform material exhibits significant age hardening while only marginal age hardening is observed for the 150 °C platform material. Consequently, the materials built at different platform temperatures show similar mechanical properties after T5 treatment. The age hardening response is more pronounced when a rapid solution treatment (RST, 520 °C for 10 min) is adopted instead of the conventional solution treatment (CST, 520 °C for 2 h) owing to the precipitation of a higher number density of needle-like β'' phase during ageing. The T5 treatment improves both yield strength and tensile strength, but the improvements in strength are accompanied by a reduction in ductility. On the other hand, the RST-T6 treatment substantially improves ductility and reduces the tensile strength while maintaining the yield strength for the 150 °C platform material. Based on the microstructural analysis, the influences of platform preheating and post-build heat treatment on mechanical properties of PBF-LB AlSi10Mg are discussed.

Simulation of Wire Arc Additive Manufacturing in the Reinforcement of a Half-Cylinder Shell Geometry.

Wire arc additive manufacturing (WAAM) is an additive manufacturing process based on gas metal arc welding. It allows the fabrication of large-volume metal components by the controlled deposition and stacking of weld beads. Next to the near-net-shape manufacturing of metal components, WAAM is also applied in the local reinforcement of structural parts, such as shell geometries. However, this procedure can lead to undesired thermally induced distortions. In this work, the distortion caused by the WAAM reinforcement of half-cylinder shell geometries was investigated through experiments and transient thermo-mechanical finite element simulations. In the experiments, the weld beads were applied to the specimen, while its thermal history was measured using thermocouples. The developing distortions were registered using displacement transducers. The experimental data were used to calibrate and validate the simulation. Using the validated model, the temperature field and the distortions of the specimens could be predicted. Subsequently, the simulation was used to assess different deposition patterns and shell thicknesses with regard to the resulting part distortions. The investigations revealed a non-linear relation between shell thickness and distortion. Moreover, the orientation and the sequence of the weld beads had a significant impact on the formation of distortion. However, those effects diminished with an increasing shell thickness.

Modeling the process behavior in laser additive manufacturing of metallic materials—A tribute to professor Jyoti Mazumder

This paper provides an overview of Professor Jyoti Mazumder's contribution in modeling of laser-matter interaction during materials processing over the past four decades, particularly in the area of laser-based metal additive manufacturing of metallic components, followed by the work at The University of Manchester in modeling and simulation of powder/wire-feeding laser additive manufacturing, in-space laser additive manufacturing, high deposition rate laser metal deposition, and multiple-material laser powder bed fusion additive manufacturing processes. Future challenges and opportunities in this field are discussed.

Modeling the thermomechanical processes and residual stresses in additive manufacturing of metallic components

AbstractIn additive manufacturing of metallic components and fusion welding, undesirable deformations and residual stresses are common drawbacks, which directly influence the performance and functionality of the manufactured components. In this work, a thermomechanical continuum model is developed by using the phase‐field method to track the evolution of the melting/remelting‐solidification occurring due to the incident of localized heat input sources, e.g, by means of a laser heat source. The result of the thermal analysis is then employed for the mechanical analysis to predict the residual stresses. In the thermal and mechanical analysis, the thermomechanical properties are considered to evolve with the phase‐field variable. In addition, a scalar‐valued history variable is introduced to distinguish between consolidated and loose powder materials. This enables single‐track additive manufacturing simulations with a possible extension to multi‐layer and multi‐track simulations. To verify and validate the modeling approach, the coupled system of equations is implemented in the open‐source FEniCS project and a numerical example for additive manufacturing is solved and compared qualitatively and quantitatively with reference results from the literature.

Effects of surface roughness on fatigue behavior of additively manufactured stainless steel 316L

Surface roughness plays an important role which affects the fatigue performance of additively manufactured metal components. Surface roughness generally contains micro-notches which form stress concentration and significantly deteriorate the fatigue life. Based on nonlinear elastoplasticity finite element (FE) analyses, the roughness model of additively manufactured stainless steel 316L (SS316L) was performed, and the cyclic behavior of surface notch root material under different fatigue stress loads was captured. The stress concentration factor on the selected critical surface position is calculated and the notch root strain analysis are proved by depending on the applied load level. Finally two distinct deformation modes are found: elastic shakedown and ratcheting deformation.

Development of Fused Deposition Modeling of Multiple Materials (FD3M) Through Dynamic Coaxial Extrusion.

Multimaterial additive manufacturing is expanding the design space realizable with 3D printing, yet is largely constrained to sequential deposition of each individual material. The ability to coextrude two materials and change the ratio of materials while printing would enable custom-tailored polymer composites. Here, the evolution of a dynamic material coextrusion process for additive manufacturing capable of printing any ratio between and including two neat input materials is described across 3 hot-end generations and 14 implemented design iterations. The designs evolved with increased understanding of manufacturing constraints associated with the additive manufacturing of metal components with internal flow bore diameters on the order of 2 mm and typical bore length around 50 mm. The second generation overcame this issue by partitioning the design into two pieces to locate the flow channel geometry at the interface between the components so that the details could be easily printed on the components' external surfaces. The third concept generation then focused on minimizing flow channel volume to reduce the average length when transitioning between materials by 92%. The third-generation design was also used to investigate the improvements in dimensional stability during annealing of acrylonitrile butadiene styrene (ABS) made possible by coextruding ABS with a polycarbonate (PC) core. The standard deviation of part shrinkage after annealing was 7.08% for the neat ABS but reduced to 0.24% for the coextruded ABS/PC components.

Finite element analysis of the surface finishing of additively manufactured 316L stainless steel by ball burnishing

As additive manufacturing of metal components is becoming increasingly significant due to high levels of design freedom yet features insufficient surface quality, finishing processes need to be further developed. As an alternative to machining, investigating the ball burnishing of powder bed fusion (PBF) workpieces provides opportunities to advance fundamental research. Studying this method's capabilities, finite element analyses (FEA) were conducted based on a flow stress model parameterized with Split-Hopkinson Pressure Bar (SHPB) tests. The roughness smoothing and a macroscopic approach mapping residual stress distributions was modelled and validated. Improvements in surface integrity were confirmed in both the FEA and experimentally.