Microstructure In Additive Manufacturing Research Articles

Emergence of rapid solidification microstructure in additive manufacturing of a Magnesium alloy

Abstract Bioresorbable Mg-based alloys with low density, low elastic modulus, and excellent biocompatibility are outstanding candidates for temporary orthopedic implants. Coincidentally, metal additive manufacturing (AM) is disrupting the biomedical sector by providing fast access to patient-customized implants. Due to the high cooling rates associated with fusion-based AM techniques, they are often described as rapid solidification processes. However, conclusive observations or rapid solidification in metal AM — attested by drastic microstructural changes induced by solute trapping, kinetic undercooling, or morphological transitions of the solid-liquid interface — are scarce. Here we study the formation of banded microstructures during laser powder-bed fusion (LPBF) of a biomedical-grade Magnesium-rare earth alloy, combining advanced characterization and state-of-the-art thermal and phase-field modeling. Our experiments unambiguously identify microstructures as the result of an oscillatory banding instability known from other rapid solidification processes. Our simulations confirm that LPBF-relevant solidification conditions strongly promote the development of banded microstructures in a Mg-Nd alloy. Simulations also allow us to peer into the sub-micrometer nanosecond-scale details of the solid-liquid interface evolution giving rise to the distinctive banded patterns. Since rapidly solidified Mg alloys may exhibit significantly different mechanical and corrosion response compared to their cast counterparts, the ability to predict the emergence of rapid solidification microstructures (and to correlate them with local solidification conditions) may open new pathways for the design of bioresorbable orthopedic implants, not only fitted geometrically to each patient, but also optimized with locally-tuned mechanical and corrosion properties.

Understanding the strain localization in additively manufactured materials: Micro-scale tensile tests and crystal plasticity modeling

Metallic parts fabricated by additive manufacturing (AM) usually exhibit unique microstructures and non-negligible residual stresses compared with the counterparts produced by conventional manufacturing. These inherent microstructural factors strongly affect the mechanical response of the as-built AM parts. In this study, we focus on the strain localization behavior of 316L stainless steel produced by laser powder-bed-fusion. In-situ tensile tests under a scanning electron microscope are performed, and the digital image correlation method is used to measure the strain distribution combined with electron backscatter diffraction. Meanwhile, a dislocation-based crystal plasticity finite element model incorporating residual stresses is developed to study the origins of the strain localization in the AM material. The results indicate that strain localization in AM materials is closely associated with microstructural features, encompassing behaviors related to slip activities, interactions with neighboring grains and dislocation evolutions. Additionally, the columnar grain features also render the strain distribution sensitive to the loading direction. The strain localization is serious in some small grains with high residual stresses, while in large grains the effect is less significant. These factors collectively contribute to the increasing likelihood of strain localization occurring in the AM microstructures with heterogeneous grain size and texture distribution. This work provides detailed insights into the strain localization in AM materials and would facilitate the manufacturing parameter optimization of AM materials by tuning the microstructure to reduce deformation inhomogeneity.

High fatigue resistance in a titanium alloy via near-void-free 3D printing.

The advantage of 3D printing-that is, additive manufacturing (AM) of structural materials-has been severely compromised by their disappointing fatigue properties1,2. Commonly, poor fatigue properties appear to result from the presence of microvoids induced by current printing process procedures3,4. Accordingly, the question that we pose is whether the elimination of such microvoids can provide a feasible solution for marked enhancement of the fatigue resistance of void-free AM (Net-AM) alloys. Here we successfully rebuild an approximate void-free AM microstructure in Ti-6Al-4V titanium alloy by development of a Net-AM processing technique through an understanding of the asynchronism of phase transformation and grain growth. We identify the fatigue resistance of such AM microstructures and show that they lead to a high fatigue limit of around 1 GPa, exceeding the fatigue resistance of all AM and forged titanium alloys as well as that of other metallic materials. We confirm the high fatigue resistance of Net-AM microstructures and the potential advantages of AM processing in the production of structural components with maximum fatigue strength, which is beneficial for further application of AM technologies in engineering fields.

Magnetron Sputtering as a Microstructural Screening Tool for Laser Additive Manufacturing: Study on Ni Superalloys

In this work, magnetron sputtering coupled with a heat treatment is demonstrated as a route to obtain surrogate microstructures comparable to those achieved by laser‐based additive manufacturing (AM) for Ni superalloys. Furthermore, this technique is shown to be a novel and efficient way of screening elemental additions with high compositional resolution for AM, without the need for designing and characterizing custom‐atomized powders. Herein, Inconel 718 films are sputtered from both arc‐melted and AM targets to capture any compositional changes and to develop a comprehensive methodology. Films are characterized via electron microscopy techniques to establish similarities between sputtered plus heat‐treated films and bulk AM Inconel 718 microstructures reported in literature. Since Inconel 718 is susceptible to high‐temperature S embrittlement, films were then co‐sputtered with small amounts of Zr or Hf, which are found to facilitate sulfur segregation via nano secondary ion mass spectrometry analysis. Overall, this work highlights how magnetron sputtering plus heat treatment can be leveraged to rapidly screen novel AM microstructures, thereby accelerating the development and deployment of AM materials.

Selective Surface Feature Electropolishing of Additively Manufactured 316L Using Pulse Electrochemistry

Electrochemical finishing of metallic surfaces is a scalable, relatively cost-effective technique that is also versatile. One of the main detractions is that the current distribution is uneven and unspecific, often leading to a lack of shape retention and, if it goes too far, inducing corrosion. Pulsing the electrical input creates an unsteady state during electropolishing, controlling the current distribution across the part. The pulse wave prevention of general non-uniform current distribution across the entire geometry instead produces local non-uniformities in the current distribution at given surface features. Many pulse electropolishing parameters can be varied to target individual surface features, including the electrolyte, pulse height, pulse width (equally and unequally distributed), time of polishing as well as the overall waveform.Additive manufacturing (AM) can produce unique, complex structures rapidly. The fast local melting of metal AM parts cool quickly due to the small area, causing unique microstructures, pores, and larger surface roughness compared to cast. AM microstructures change mechanical and chemical properties, such as strength and corrosion, compared to traditionally manufactured parts. Variations in build parameters and composition creates a complicated, feature dependent, corrosion response. By polishing the surface, some of the features, namely the corrosion resistance, wear, and aesthetics can be standardized and enhanced.In this work, we explore the underlying electrochemistry that governs pulse electropolishing. We show that controlling the electrolyte with environmentally benign chemicals ultimately influences the part dimensions that can be polished, the ability to polish, and the propensity to corrode. The pulse height, pulse width (Fig. 1), and total charge passed have an impact on different surface feature removal. We will demonstrate impact of the pulse parameters on the polishing of additively manufactured stainless steel. We go beyond simple planar geometry and apply our understanding to larger scale complex geometry parts, while maintaining near-net shape and minimizing mass loss. Figure 1

Control of texture and microstructure in additive manufacturing of stainless steel 316 L

This investigation deals with exploring the effect of rotation scan strategy on microstructural evolution and hence mechanical behaviour of 316 L stainless steel prepared by additive manufacturing using selective laser melting technique. Higher anisotropy has been observed for samples where no hatch rotation was employed compared to the samples where a hatch rotation was involved during printing. The morphology of grains gets perturbed for samples prepared by hatch rotation 60˚ and hatch rot 74˚ when compared to the samples prepared using no hatch rotation or a hatch rotation of 90˚. The crystallographic texture component of the sample consists of a combination of < 100 > and < 110 > orientation (// building direction) except for the hatch rot 60˚ sample where the texture comprises of only < 110 > . It has been noticed that texture control is possible through giving a rotation such as 60˚ or 90˚. The samples prepared without any hatch rotation exhibited higher strain hardening due to combined action of slip and twin activity when compared to hatch rot 60˚ sample where < 110 > texture reduces the deformation mechanism to slip only. This study paves a new pathway to control the mechanical properties by tuning the appropriate texture along the BD.

High Temperature Tensile Properties and Microstructure of Ni20Cr Alloy Fabricated by Laser Powder Bed Fusion

Additive Manufacturing (AM) brings about an array of modifications in microstructure with respect to conventional routes transforming mechanical performances. These new microstructure features depend on process parameters and especially on volume energy-density delivered by the laser on powder layer. Among the different alloys manufactured by AM, Ni-alloys exhibit high-strength at elevated temperature opening the way of fabrication of gas turbines and jet-engine parts. Ni-superalloys experience precipitation hardening due to the formation of γ′ and γ′′ phases leading to complex microstructures. To better study the influence of the AM microstructure on Ni-alloys mechanical properties, in particular at elevated temperatures, a theoretically monophasic and binary Ni20Cr-alloy manufactured by laser powder-bed fusion was studied in this work. Remarkable Yield Strength (400 MPa) and Ultimate Tensile Strength (UTS) (600 MPa) were observed at 500°C with hardly any loss of properties from room temperature, owing to the thermal stability of cellular dendrites till 700°C. Ductility drop was reported at 700°C due to anomalous brittle behaviour of Ni-alloys. Hardening behaviour vanished at 900°C signifying the deletion of dendrites, disappearance of dislocations, diffusion of Cr from dendritic walls and growth of oxides.

Understanding the corrosion behaviour of Al-Mg alloy fabricated using a Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) process

Metal additive manufacturing (AM) is an emerging disruptive technology capable of manufacturing complex shaped components that are difficult to manufacture through conventional methods. However, the corrosion behaviour of AM fabricated parts must be considered for safety critical applications. For this reason, we have studied the relationship between AM fabricated Scalmalloy (Al-Mg-Sc-Zr) microstructures and their corresponding corrosion behaviours. This comparison has been drawn against a comparable commercial Al-Mg alloy (5182). The corrosion resistance of the samples in salt water was assessed via various electrochemical analytics techniques. It was observed that Scalmalloy produced better corrosion resistance than 5182 Al-alloy. This can be attributed to the spontaneous formation of a passive film on refined AM microstructure and the presence of Sc and Zr, specifically when samples were fabricated with higher density (less porosity). The alloys’ corrosion mechanisms were dependent on immersion time and the microstructural features of the samples.

Comparison of shape memory properties between Cu-12.5Al–3Mn and Cu-12.5Al–3Mn–1Ni produced by additive manufacturing technology

In this work, two Cu–Al–Mn shape memory alloys (SMAs), Cu-12.5Al–3Mn and Cu-12.5Al–3Mn–1Ni, were prepared by wire arc melting deposition, and their shape recovery rates, ultimate recoverable strains, additive manufacturing microstructures, memory recovery behaviors, and phase transition properties were comparatively investigated. Meanwhile, the mechanism of Ni in these two alloys was discussed by combining the differences in phase composition of the two alloys. Results showed that the shape recovery rate of Cu-12.5Al–3Mn alloy decreased linearly with increasing pre-strain, from 91.23% at 4% pre-strain to 62.41% at 10% pre-strain, and its ultimate recoverable strain was approximately 7%; while the shape recovery rate of Cu-12.5Al–3Mn–1Ni SMA was 100% at 4%–8% pre-strain, and remained at 96.57% until the pre-strain was increased to 10%, i.e., the recoverable strain was more than 9%. In addition, the phase change temperature of the alloy with Ni addition decreased compared to that of the alloy without Ni addition, especially the temperature hysteresis was narrower. The results indicate the formation of a new κ phase after adding Ni to the alloy. The pinning effect of this new phase significantly improves the order of martensite within the alloy. Moreover, Ni also induces the transformation of the alloy, inhibiting the precipitation of the brittle phase γ2 and leading to a marked increase in the shape memory recovery rate and the recoverable strain of the additively fabricated Cu–Al–Mn alloy. Note that Ni transforms the alloy from hyper-eutectoid components to eutectoid components. Moreover, after adding Ni, the thermoelastic martensite phase transition temperature and temperature hysteresis decreased together with its eutectoid temperature, which made the additive manufacturing Cu–Al–Mn SMA exhibit an explosive response behavior.

A Bézier curve fit to melt pool geometry for modeling additive manufacturing microstructures

This work describes a Metropolis Monte Carlo model for modeling additively manufactured metallic microstructures which employs Bézier curves to formulate melt pool geometry induced by a moving laser heat source. With this formulation, a wide range of melt pool geometries can be modeled and their effects on microstructure morphologies can be evaluated. Melt pool geometries are fit with Bézier curves using melt pool dimensions of width, length, and depth obtained from FEM calculations of the thermal process. Assuming a steady state pool geometry, microstructures are simulated incrementally in the heat affected zone in much the same way an additively manufactured material part is built. The approach is computationally tractable as microstructure simulation is only required in the fusion and heat affected zones, thus requiring less computational resources than would be needed if the entire material/part domain was taken into account. Using the model, microstructures are simulated for LENS™ processed stainless steel. Demonstrations show the model can replicate microstructure morphologies seen in electron beam backscatter diffraction data.

Machine learning-based identification of interpretable process-structure linkages in metal additive manufacturing

The use of data-driven methods for metal additive manufacturing (AM) is currently gaining importance as indicated by the increasing number of scientific literature in this field. Incorporation of data-driven methods has the potential to eliminate current bottlenecks in microstructure design given the diverse and complex nature of microstructures in additively manufactured metals. So far, coupling of existing simulation methods, e.g. physics-based process and microstructure models, to simulate AM microstructures with desired morphological characteristics requires extensive computational resources, high computation times and therefore, allows no scalable output. The extension of experimental- and simulation-based approaches by machine learning (ML) algorithms enables fast and computationally efficient predictions. However, the underlying architecture of ML algorithms often does not allow domain experts to interpret how predictions of the model were made and which features are responsible to what extent. This is why ML models are often referred to as black- box models. In this study, we present a data-driven framework based on physics-based simulation data to reveal explainable process-(micro)structure (P-S) linkages for metal AM. We provide an open-source dataset of 960 unique 3D microstructures created by simulation of powder bed fusion in metal AM. We employed the stochastic parallel particle kinetic simulator (SPPARKS) that is based on the kinetic Monte Carlo (kMC) method as an exemplary AM microstructure generator. Selected ML regression algorithms aim to predict 3D chord length distributions (CLDs), as a morphology descriptor, depending on the associated process parameter combinations. Various dimension reduction algorithms are applied for computationally efficient use of the data space. The proposed methodology allows (i) microstructure predictions under given processing conditions and (ii) to navigate experts in the process parameter space to achieve target microstructures. In this context, SHAP (SHapley Additive exPlanations) values are used to decipher the contribution of individual process parameters to the microstructure evolution. In particular, SHAP values calculated in this study unfold the width of the melt pool and the heat-affected zone as dominant features on the model output. We provide open-access to the used dataset and methods for the scientific community to gain experience with the proposed approach.

Numerical simulation for the effect of scanning speed and in situ laser shock peening on molten pool and solidification characteristics

The unique thermal cycle of selective laser melting (SLM) significantly affects the undesirable formability and mechanical properties of the deposited parts, especially for materials with complex compositions. Laser shock peening (LSP) is a strengthening technology that can refine grain, convert tensile stress to compressive stress, and improve fatigue strength. In situ LSP is a technology that combines LSP and SLM without ablative coating. The combination can strengthen the additive manufacturing microstructure layer by layer. Some literature has verified the feasibility of no absorption layer and pressure confining layer LSP. However, little research reported the effects of the in situ combination on the molten pool. In this work, the finite element method (FEM) has systematically investigated the impact of scanning speed and in situ LSP on fluid flow behavior, heat transfer, and the solidification process of the molten pool. The flow velocity and the size of the molten pool decrease as the scanning speed increases. The solidification rate shows an increasing-decreasing-increasing process at low scanning speed during the solidification process. Moreover, the value of R is consistently stable at high scanning speeds. The temperature gradient increases gradually and decreases sharply with the scanning speed increase. The in situ LSP reduces the temperature and the fluid flow of the molten pool, which decreases the heat convection and the value of Peclet number, but has little effect on the solidification process of the molten pool.

Evaluation of computational homogenization methods for the prediction of mechanical properties of additively manufactured metal parts

It is well known that the strongly location-dependent microstructures observed in metal parts made using additive manufacturing (AM) processes differ from those found in components produced via traditional manufacturing processes. This is primarily because the microstructures in AM parts strongly depend on spatially-varying cooling rates that are caused by several factors including part geometry and proximity to the build plate. Consequently, it is necessary to take a fresh look into methods used for calculating resulting mechanical properties, as the increased spatial resolution that must be applied to the description of properties of AM parts calls for vast improvements in computational efficiency. Computational homogenization methods are employed for the calculation of these mechanical properties based on the polycrystalline microstructures typically encountered in metallic materials. Such calculations that enable the description of properties as a function of position increase the accuracy of a vital link in the structure–process–property–performance continuum that falls within the Integrated Computational Materials Engineering (ICME) paradigm. In this work we describe and critically review six of these methods and their application to AM microstructures. We find that the Fast Fourier Transform (FFT) method and the Finite Element Method (FEM) are the optimal choices based on several factors including accuracy, efficiency, and applicability to polycrystalline microstructures. Provided the microstructure is amenable to the use of uniform grids and the assumption of periodic boundary conditions, the FFT method has greater computational efficiency. Where unstructured meshes, adaptive remeshing and/or non-periodic boundary conditions are desired, the FEM is the method of choice. Our recommendations are equally applicable to functionally graded AM parts where site-specific microstructures are engineered using multiple materials or different process parameters. They can also be utilized for other material systems and circumstances where increased accuracy is achieved by describing mechanical properties as a function of location.

A modular framework to obtain representative microstructural cells of additively manufactured parts

The repeating nature of additive manufacturing (AM) translates into quasi-periodic repeating hierarchical microstructures, which vary depending on the working principle of the AM technology. The geometry and internal characteristics of these repeating microstructures affect the geometrical accuracy and mechanical properties of AM parts. We propose a modular and generic framework for AM microstructures to automatically determine a representative microstructural cell with average shape and microstructural information based on micrograph processing and morphological shape analyses. A general microstructural mapping procedure is presented to map and average different microstructural features inside the representative cell. Moreover, an extension of the method is proposed to recover a representative cell with a periodic shape as required for subsequent numerical micromechanical simulations under periodic boundary conditions to unravel process-structure–property relationships. The framework is demonstrated on a virtually generated microstructure and on the metallic and polymeric microstructure of two parts manufactured with different AM processes, showing the framework's versatility for different materials and AM technologies. The three test cases show the framework's modularity, where different procedures were applied to overcome the challenges of each particular case while keeping the primary method the same. We also discuss applications of the framework, such as enabling statistical investigations of spatial variations of the microstructural features across AM parts and providing essential input for microstructural and mechanical numerical simulations.

A Study on the Thermo-Mechanical History, Residual Stress, and Dynamic Recrystallization Mechanisms in Additively Manufactured Austenitic Stainless Steels

In this work, both numerical simulations and experimental characterization were used to obtain a broad understanding of the thermo-mechanical history, residual stress, and microstructure of the directed energy deposition (DED) process of austenitic stainless steels. To investigate the effect of process factors on residual stress, the global sensitivity analysis approach based on D-MORPH-HDMR was utilized. The results of the research reveal that the amplified effect of the influence of the three input variables (layer thickness, L; laser power, P; and scanning speed, v) on the transverse residual stress and thickness-direction residual stress is L &gt; P &gt; v; in contrast, the influence of longitudinal residual stress is P &gt; L &gt; v. We also found that general tendencies in local plastic strain accumulation are analogous to the relative distribution of geometrically necessary dislocations (GNDs). Additionally, we investigated post-solidification structures connected to residual stress, such as submicron dislocation cells and dynamic recrystallization (DRX) in austenitic stainless steels during DED. The investigation revealed that the DDRX and CDRX phenomena were caused by the bulging of initial grain boundaries and progressive sub-grain rotation (PSR). The fact that the sample bottom had more thermo-mechanical cycles than the top led to a higher dislocation density and hence more DDRX. This study presents a unique perspective on the link between residual stress and microstructure in additive manufacturing.

Analyzing effects of temperature gradient and scan rate on metal additive manufacturing microstructure by using phase field-finite element method

Predicting the evolutionary behavior of microstructures with the help of numerical simulation techniques has become an essential tool for studying the solidification process of metal additive manufacturing. As a mesoscopic model based on the diffusion interface theory, phase field method (PFM) can be used to predict the evolution of solidification microstructure. The open-source PFM framework PRISMS-PF can not only efficiently solve systems of equations with billions of degrees of freedom, but also provide a simple adaptive mesh control module. In this paper, based on the open-source PFM framework PRISMS-PF, a phase field-finite element method (PFM-FEM) simulation flow for the solidification process of A356 aluminum alloy additive manufacturing in the two-dimensional case was established. The effects of temperature gradient, scan rate and initial solid-phase morphology on solute concentration, dendrite spacing and dendrite morphology were analyzed and compared with experimental results for verification. Analyzing the results for different temperature gradients and scan rates cases, it was found that the increase of temperature gradient or scan rate made the primary dendrite arm space decrease; as the ratio of temperature gradient to scan rate decreased, the solidification morphology gradually changed from flat crystal to cellular crystal, columnar crystal, and even dendritic structure. Analyzing the results for different initial solid-phase morphology cases, it was found that the influence of initial solid-phase morphology on dendrite growth increased as the ratio of temperature gradient to scan rate decreased. The above influence rules were mainly related to the composition overcooling zone under different conditions. This paper is expected to provide a theoretical support for the effective regulation of solidification microstructure in metal additive manufacturing.

Development of statistical models for porosity from digital optical micrographs with application to metal additive manufacturing microstructure

Using techniques widely available in digital image processing, machine learning and spatial statistics, this paper proposes a novel workflow that generates two dimensional spatial models using objects extracted from digital micro-graphs of material micro-structure that can be used in statistical reconstruction modeling within a numerical procedure, such as finite element analysis. This paper also reviews the relevant image processing techniques, spatial statistical theories and reconstruction (modeling) algorithms with unique contributions. As an end-to-end illustration, the workflow is applied to a two dimensional, digital micro-graph of hydrogen porosity taken of as-fabricated AlSi10Mg manufactured using laser powder bed fusion adopted from the literature.

Effects of trace Mn and B on microstructure and memory properties of additive manufacturing Cu-Al-Fe alloy

In this paper, the Cu-13Al-4Fe alloy was selected as the object, and the powder core wire with the “skin coated powder” damage-resistant structure was selected as the material for additive manufacturing. Three kinds of alloys: Cu-13Al-4Fe, Cu-13Al-4Fe-0.6Mn, and Cu-13Al-4Fe-0.2B were prepared by wire arc additive manufacturing technology. An optical microscope observed the as-deposited and quenched microstructures of the alloys before and after adding trace fourth phase elements. Their shape recovery rates under different pre-strain amounts (4%, 6%, and 8%) were tested by the bending deformation method, and the results were obtained. The mechanism of action of the fourth phase elements is discussed. The results show that the addition of trace amounts of Mn and B not only refines the microstructure of additively manufactured Cu-Al-Fe alloys but also improves the memory properties of Cu-13Al-4Fe alloys. Especially the Cu-13Al-4Fe alloys with trace amounts of B added. the shape recovery rate is 100% under the pre-strain of 4% and 6%, and the shape recovery rate is still 98.39% under the pre-strain of 8%.

Use of Alloying to Effect an Equiaxed Microstructure in Additive Manufacturing and Subsequent Heat Treatment of High-Strength Titanium Alloys

This paper addresses the use of alloying additions to titanium alloys for additive manufacturing (AM) with the specific objective of producing equiaxed microstructures. The additions are among those that increase freezing ranges such that significant solutal undercooling results when combined with the rapid cooling rates associated with AM, and so be effective in inducing a columnar-to-equiaxed transition (CET). Firstly, computational thermodynamics has been used to provide a simple graphical means of predicting these additions; this method has been used to explore additions of Ni and Fe to the alloy Ti–6Al–4V (Ti64). Secondly, an experimental means of determining the minimum concentration of these alloying elements required to effect the CET has been developed involving gradient builds. Thirdly, it has been found that additions of Fe to Ti64 cause the alloy to change from an α/β Ti alloy to being a metastable β-Ti alloy, whereas additions of Ni do not produce the same result. This change in type of Ti alloy results in a marked difference in the development of microstructures of these compositionally modified alloys using heat treatments. Finally, hardness measurements have been used to provide a preliminary assessment of the mechanical response of these modified alloys.

Modeling study of porosity’s effect on grain evolution during directed energy deposition

In this work, porosity’s influence on grain evolution during Directed Energy Deposition was studied with the developed Kinetic Monte Carlo model. In the first case, grain evolution was simulated in a purely solid domain without any porosity. In the second case, some internal spheres were inlaid in the simulation domain as internal pores. The porosity’s effect on grain evolution was investigated by comparing the predicted grain morphologies in both cases. This study is the first work focused on porosity’s influence on additive manufacturing microstructure.