Manufacturing Of Metallic Components Research Articles

Deformation behavior of additive manufactured 316 L stainless steel using in situ neutron diffraction

Manufacturing of metallic components using additive manufacturing technique is of great interest for the industrial applications. Here, the mechanical and microstructural responses of a 316 L stainless steel (316LSS) built by selective laser melting (SLM) with XOY and XOZ directions were revealed by performing in situ neutron diffraction tensile tests. The tensile strength of the XOY-printed samples reaches 700 MPa, while the tensile strength of the XOZ-printed samples is less than 600 MPa. The as-printed 316LSS exhibits anisotropy in tensile property due to the formation of different fractions of nano-sized dimples, twin boundaries, diffraction peak and lattice constants during tensile tests at room temperature. Therefore, choosing right printing direction plays a vital role in forming of metal parts using SLM for further application.

Development of 17-4 PH Stainless Steel for Low-Power Selective Laser Sintering.

Selective laser sintering (SLS) is one of the prominent methods of polymer additive manufacturing (AM). A low-power laser source is used to directly melt and sinter polymer material into the desired shape. This study focuses on the utilization of the low-power laser SLS system to successfully manufacture metallic components through the development of a metal-polymer composite material. In this study, 17-4 PH stainless powders are used and mixed with polyoxymethylene (POM) and high-density polyethylene (HDPE) to prepare the composite powder material. The polymeric mixture is removed during the thermal degreasing process and subsequent sintering results in a solid metallic component. Sinterit Lisa with a 5 W, 808 nm laser source is used to fabricate the green part. For the printing parameters of 140 °C, laser power of 35.87 mJ/mm2, and layer thickness of 100 μm, the printed samples achieved a maximum density of 3.61 g/cm3 and a complete shape. After sintering at 1310 °C for 180 min, the tensile strength of the shrunk sample is 605.64 MPa, the hardness is HRC 14.8, the average shrinkage rate is 22%, and the density is 7.57 g/cm3, which can reach 97% of the theoretical density. This process allows the use of a wide range of particle sizes that the usual AM technologies have, making it a low-cost, low-energy-consumption, high-speed AM technology.

A Review of Modeling, Simulation, and Process Qualification of Additively Manufactured Metal Components via the Laser Powder Bed Fusion Method

Metal additive manufacturing (AM) has grown in recent years to supplement or even replace traditional fabrication methods. Specifically, the laser powder bed fusion (LPBF) process has been used to manufacture components in support of sustainment issues, where obsolete components are hard to procure. While LPBF can be used to solve these issues, much work is still required to fully understand the metal AM technology to determine its usefulness as a reliable manufacturing process. Due to the complex physical mechanisms involved in the multiscale problem of LPBF, repeatability is often difficult to achieve and consequently makes meeting qualification requirements challenging. The purpose of this work is to provide a review of the physics of metal AM at the melt pool and part scales, thermomechanical simulation methods, as well as the available commercial software used for finite element analysis and computational fluid dynamics modeling. In addition, metal AM process qualification frameworks are briefly discussed in the context of the computational basis established in this work.

Thermal damage diagnosis in micro grinding processes based on raw acoustic emission signals and convolution neural networks

A micro-grinding process is usually the last machining operation among the various processes employed in the manufacturing of metallic components. It is generally the first option when the manufactured products require a combination of low surface roughness values and narrow dimensional tolerances. However, one of the main challenges of a micro-grinding process is avoiding high specific energy and the corresponding intense heat generation, as this can damage the workpiece. The energy consumed during micro-grinding is almost entirely converted into heat, making the component susceptible to thermal damage. As the occurrence of this damage compromises the service life of manufactured components, the implementation of monitoring systems is essential to guarantee the quality of the manufactured products improving process efficiency. In this study, a new approach based on convolution neural networks (CNNs) is proposed to predict the occurrence of thermal damage in the micro-grinding process. This approach uses raw acoustic emission (AE) signals as inputs into CNN, which is a more technically feasible approach to performing real-time monitoring. To obtain the AE data, micro-grinding experiments were conducted on N2711 grade steel (which is susceptible to thermal damage) under different cutting conditions (ae = 5–50 µm). The obtained AE signals were labeled based on the offline analyses performed on the workpieces, which identified the occurrence of thermal damage through visual inspections, microhardness, and microstructural analysis. For the conditions employed in this work, thermal damage was obtained after grinding with depth of cut values > 10 µm. Total 3960 AE signals were obtained of which 2200 of them were associated with thermal damage. The results depict that the proposed CNN model was able to successfully classify normal and thermal damage AE signals reaching an accuracy of 98.6% over the dataset (0.4% false positives and 1.0% false negatives).

The Influence of Ultrasonic Irradiation of a 316L Weld Pool Produced by DED on the Mechanical Properties of the Produced Component

Additive manufacturing of metallic components often results in the formation of columnar grain structures aligned along the build direction. These elongated grains can introduce anisotropy, negatively impacting the mechanical properties of the components. This study aimed to achieve controlled solidification with a fine-grained microstructure to enhance the mechanical performance of printed parts. Stainless steel 316L was used as the test material. High-intensity ultrasound was applied during the direct energy deposition (DED) process to inhibit the formation of columnar grains. The investigation emphasized the importance of amplitude changes of the ultrasound wave as the system’s geometry continuously evolves with the addition of multiple layers and assessed how these changes influence the grain size and distribution. Initial tests revealed significant amplitude fluctuations during layer deposition, highlighting the impact of layer deposition on process uniformity. The mechanical results demonstrated that the application of ultrasound effectively refined the grain structure, leading to a 15% increase in tensile strength compared to conventionally additively manufactured samples.

Influence of Printing Parameters on the Morphological Characteristics of Plasma Directed Energy-Deposited Stainless Steel

This study investigated the influence of printing parameters and strategies on the morphological characteristics of austenitic stainless steel beads deposited on carbon steel substrates, using plasma directed energy deposition (DED). The experimental setup varied the welding current, wire feed speed, and torch travel speed, and we analyzed three printing strategies: simple-linear, overlapping, and oscillating. Moreover, advanced 3D scanning and computational analysis were used to assess the key morphological features, including bead width and height. The results showed that the computational model developed by using parabolic assumptions accurately predicted the geometric outcomes of the overlapping beads. The oscillating printing strategy was the one that showed improved morphological uniformity and bead substrate wettability, so these features were used for multi-layer component manufacturing. The use of equivalent wavelength–amplitude values resulted in maximum combinations of bead height and width. Moreover, cost-effective carbon steel substrates were feasibly used in microstructural and elemental analyses, with the latter ones confirming the alignment of the bead composition with the wire-fed material. Overall, this study provides practical insights for optimizing plasma DED processes, thus enhancing the efficiency and quality of metal component manufacturing.

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.