scholarly journals A Single Crystal Process Window for Electron Beam Powder Bed Fusion Additive Manufacturing of a CMSX-4 Type Ni-Based Superalloy

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3785
Author(s):  
Julian Pistor ◽  
Christoph Breuning ◽  
Carolin Körner
Keyword(s):  
Additive Manufacturing ◽  
Single Crystal ◽  
Single Crystals ◽  
Process Window ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
High Fraction ◽  
Scanning Strategies ◽  
Melt Pool Size

Using suitable scanning strategies, even single crystals can emerge from powder during additive manufacturing. In this paper, a full microstructure map for additive manufacturing of technical single crystals is presented using the conventional single crystal Ni-based superalloy CMSX-4. The correlation between process parameters, melt pool size and shape, as well as single crystal fraction, is investigated through a high number of experiments supported by numerical simulations. Based on these results, a strategy for the fabrication of high fraction single crystals in powder bed fusion additive manufacturing is deduced.

3D Build Melt Pool Predictive Modeling for Powder Bed Fusion Additive Manufacturing

2020 ◽  
Author(s):  
Zhuo Yang ◽  
Yan Lu ◽  
Ho Yeung ◽  
Sundar Kirshnamurty
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Pool Size ◽  
Powder Bed Fusion ◽  
Neural Network Approach ◽  
Validation Data ◽  
Powder Bed ◽  
Melt Pool ◽  
Scan Pattern ◽  
Melt Pool Size

Abstract Melt pool size is a critical intermediate measure that reflects the outcome of a laser powder bed fusion process setting. Reliable melt pool predictions prior to builds can help users to evaluate potential part defects such as lack of fusion and over melting. This paper develops a layer-wise Neighboring-Effect Modeling (L-NBEM) method to predict melt pool size for 3D builds. The proposed method employs a feedforward neural network model with ten layer-wise and track-wise input variables. An experimental build using a spiral concentrating scan pattern with varying laser power was conducted on the Additive Manufacturing Metrology Testbed at the National Institute of Standards and Technology. Training and validation data were collected from 21 completed layers of the build, with 6,192,495 digital commands and 118,928 in-situ melt pool coaxial images. The L-NBEM model using the neural network approach demonstrates a better performance of average predictive error (12.12%) by leave-one-out cross-validation method, which is lower than the benchmark NBEM model (15.23%), and the traditional power-velocity model (19.41%).

A Physics-Informed Two-Level Machine Learning Model for Predicting Melt-Pool Size in Laser Powder Bed Fusion

2021 ◽  
Author(s):  
Yong Ren ◽  
Qian Wang ◽  
Panagiotis (Pan) Michaleris
Keyword(s):  
Machine Learning ◽  
Initial Temperature ◽  
Pool Size ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Input Feature ◽  
Melt Pool Size ◽  
Melt Pool Characteristics

Abstract Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine learning (ML) model to predict the melt-pool size during the scanning of a multi-track build. To account for the effect of thermal history on melt-pool size, a so-called (pre-scan) initial temperature is predicted at the lower-level of the modeling architecture, and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the Autodesk's Netfabb Simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.

scholarly journals Virtual Development of Process Parameters for Bulk Metallic Glass Formation in Laser-Based Powder Bed Fusion

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 450
Author(s):  
Johan Lindwall ◽  
Andreas Lundbäck ◽  
Jithin James Marattukalam ◽  
Anders Ericsson
Keyword(s):  
Additive Manufacturing ◽  
Metallic Glass ◽  
Bulk Metallic Glass ◽  
Process Parameters ◽  
Glass Formation ◽  
Laser Power ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Scanning Strategies ◽  
Hatch Spacing

The development of process parameters and scanning strategies for bulk metallic glass formation during additive manufacturing is time-consuming and costly. It typically involves trials with varying settings and destructive testing to evaluate the final phase structure of the experimental samples. In this study, we present an alternative method by modelling to predict the influence of the process parameters on the crystalline phase evolution during laser-based powder bed fusion (PBF-LB). The methodology is demonstrated by performing simulations, varying the following parameters: laser power, hatch spacing and hatch length. The results are compared in terms of crystalline volume fraction, crystal number density and mean crystal radius after scanning five consecutive layers. The result from the simulation shows an identical trend for the predicted crystalline phase fraction compared to the experimental estimates. It is shown that a low laser power, large hatch spacing and long hatch lengths are beneficial for glass formation during PBF-LB. The absolute values show an offset though, over-predicted by the numerical model. The method can indicate favourable parameter settings and be a complementary tool in the development of scanning strategies and processing parameters for additive manufacturing of bulk metallic glass.

New Hammerstein Modeling and Analysis for Controlling Melt Pool Width in Powder Bed Fusion Additive Manufacturing

2021 ◽  
pp. 1-7
Author(s):  
Dan Wang ◽  
Xinyu Zhao ◽  
Xu Chen
Keyword(s):  
Finite Element ◽  
Additive Manufacturing ◽  
Process Variations ◽  
Element Model ◽  
Hammerstein Model ◽  
Powder Bed Fusion ◽  
Modeling And Analysis ◽  
Powder Bed ◽  
Melt Pool ◽  
Linearized Model

Abstract Despite the advantages and emerging applications, broader adoption of powder bed fusion (PBF) additive manufacturing is challenged by insufficient reliability and in-process variations. Finite element modeling and control-oriented modeling have been identified fundamental for predicting and engineering part qualities in PBF. This paper first builds a finite element model (FEM) of the thermal fields to look into the convoluted thermal interactions during the PBF process. Using the FEM data, we identify a novel surrogate system model from the laser power to the melt pool width. Linking a linearized model with a memoryless nonlinear submodel, we develop a physics-based Hammerstein model that captures the complex spatiotemporal thermomechanical dynamics. We verify the accuracy of the Hammerstein model using the FEM and prove that the linearized model is only a representation of the Hammerstein model around the equilibrium point. Along the way, we conduct the stability and robustness analyses and formalize the Hammerstein model to facilitate the subsequent control designs.

scholarly journals An Overview of Additive Manufacturing Technologies—A Review to Technical Synthesis in Numerical Study of Selective Laser Melting

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3895 ◽  
Author(s):  
Abbas Razavykia ◽  
Eugenio Brusa ◽  
Cristiana Delprete ◽  
Reza Yavari
Keyword(s):  
Numerical Simulation ◽  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
Powder Bed Fusion ◽  
Laser Melting ◽  
Powder Bed ◽  
Part Quality ◽  
Melt Pool ◽  
Wide Range ◽  
Am Processes

Additive Manufacturing (AM) processes enable their deployment in broad applications from aerospace to art, design, and architecture. Part quality and performance are the main concerns during AM processes execution that the achievement of adequate characteristics can be guaranteed, considering a wide range of influencing factors, such as process parameters, material, environment, measurement, and operators training. Investigating the effects of not only the influential AM processes variables but also their interactions and coupled impacts are essential to process optimization which requires huge efforts to be made. Therefore, numerical simulation can be an effective tool that facilities the evaluation of the AM processes principles. Selective Laser Melting (SLM) is a widespread Powder Bed Fusion (PBF) AM process that due to its superior advantages, such as capability to print complex and highly customized components, which leads to an increasing attention paid by industries and academia. Temperature distribution and melt pool dynamics have paramount importance to be well simulated and correlated by part quality in terms of surface finish, induced residual stress and microstructure evolution during SLM. Summarizing numerical simulations of SLM in this survey is pointed out as one important research perspective as well as exploring the contribution of adopted approaches and practices. This review survey has been organized to give an overview of AM processes such as extrusion, photopolymerization, material jetting, laminated object manufacturing, and powder bed fusion. And in particular is targeted to discuss the conducted numerical simulation of SLM to illustrate a uniform picture of existing nonproprietary approaches to predict the heat transfer, melt pool behavior, microstructure and residual stresses analysis.

On Process Temperature in Powder-Bed Electron Beam Additive Manufacturing: Model Development and Validation

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Bo Cheng ◽  
Steven Price ◽  
James Lydon ◽  
Kenneth Cooper ◽  
Kevin Chou
Keyword(s):  
Thermal Conductivity ◽  
Electron Beam ◽  
Additive Manufacturing ◽  
Thermal Characteristics ◽  
Pool Size ◽  
Process Temperature ◽  
Thermal Imager ◽  
Powder Bed ◽  
Melt Pool ◽  
Melt Pool Size

Powder-bed beam-based metal additive manufacturing (AM) such as electron beam additive manufacturing (EBAM) has a potential to offer innovative solutions to many challenges and difficulties faced in the manufacturing industry. However, the complex process physics of EBAM has not been fully understood, nor has process metrology such as temperatures been thoroughly studied, hindering part quality consistency, efficient process development and process optimizations, etc., for effective EBAM usage. In this study, numerical and experimental approaches were combined to research the process temperatures and other thermal characteristics in EBAM using Ti–6Al–4V powder. The objective of this study was to develop a comprehensive thermal model, using a finite element (FE) method, to predict temperature distributions and history in the EBAM process. On the other hand, a near infrared (NIR) thermal imager, with a spectral range of 0.78 μm–1.08 μm, was employed to acquire build surface temperatures in EBAM, with subsequent data processing for temperature profile and melt pool size analysis. The major results are summarized as follows. The thermal conductivity of Ti–6Al–4V powder is porosity dependent and is one of critical factors for temperature predictions. The measured thermal conductivity of preheated powder (of 50% porosity) is 2.44 W/m K versus 10.17 W/m K for solid Ti–6Al–4V at 750 °C. For temperature measurements in EBAM by NIR thermography, a method was developed to compensate temperature profiles due to transmission loss and unknown emissivity of liquid Ti–6Al–4V. At a beam speed of about 680 mm/s, a beam current of about 7.0 mA and a diameter of 0.55 mm, the peak process temperature is on the order around 2700 °C, and the melt pools have dimensions of about 2.94 mm, 1.09 mm, and 0.12 mm, in length, width, and depth, respectively. In general, the simulations are in reasonable agreement with the experimental results with an average error of 32% for the melt pool sizes. From the simulations, the powder porosity is found critical to the thermal characteristics in EBAM. Increasing the powder porosity will elevate the peak process temperature and increase the melt pool size.

Hybrid Modeling Approach for Melt Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing

2020 ◽  
Author(s):  
Tesfaye Moges ◽  
Zhuo Yang ◽  
Kevontrez Jones ◽  
Shaw Feng ◽  
Paul Witherell ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Hybrid Model ◽  
Hybrid Models ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Powder Bed Fusion ◽  
Cfd Model ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool

Abstract Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process-structure-property-performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser powder bed fusion (L-PBF) processes. Our unbiased, model integration method combines physics-based data and measurement data for approaching more accurate prediction of melt-pool width. Both a high-fidelity computational fluid dynamics (CFD) model and experiments utilizing optical images are used to generate a combined dataset of melt-pool widths. From this aggregated dataset, a hybrid model is developed using data-driven modeling techniques, including polynomial regression and Kriging methods. The performance of the hybrid model is evaluated by computing the average relative error and comparing it with the results of the simulations and surrogate models constructed from the original CFD model and experimental measurements. It is found that the proposed hybrid model performs better in terms of prediction accuracy and computational time. Future work includes a conceptual introduction on the use of an AM ontology to support improved model and data selection when constructing hybrid models. This study can be viewed as a significant step towards the use of hybrid models as predictive models with improved accuracy and without the sacrifice of speed.

A numerical investigation of thermal property effects on melt pool characteristics in powder-bed electron beam additive manufacturing

2016 ◽  
Vol 232 (9) ◽  
pp. 1615-1627 ◽  
Author(s):  
Bo Cheng ◽  
Kevin Chou
Keyword(s):  
Thermal Conductivity ◽  
Electron Beam ◽  
Thermal Properties ◽  
Additive Manufacturing ◽  
Melting Point ◽  
Thermal Property ◽  
Manufacturing Process ◽  
Powder Bed ◽  
Melt Pool ◽  
Melt Pool Size

Powder-bed electron beam additive manufacturing has the potential to be a cost-effective alternative in producing complex-shaped, custom-designed metal parts using various alloys. Material thermal properties have a rather sophisticated effect on the thermal characteristics such as the melt pool geometry in fabrications, impacting the build part quality. The objective of this study is to achieve a quantitative relationship that can correlate the material thermal properties and the melt pool geometric characteristics in the electron beam additive manufacturing process. The motivation is to understand the interactions of material property effect since testing individual properties is insufficient because of the change of almost all thermal properties when switching from one to the other material. In this research, a full-factorial simulation experiment was conducted to include a wide range of the thermal properties and their combinations. A developed finite element thermal model was applied to perform electron beam additive manufacturing process thermal simulations incorporating tested thermal properties. The analysis of variance method was utilized to evaluate different thermal property effects on the simulated melt pool geometry. The major results are summarized as follows. (1) The material melting point is the most dominant factor to the melt pool size. (2) The role of the material thermal conductivity may outweigh the melting point and strongly affects the melt pool size, if the thermal conductivity is very high. (3) Regression equations to correlate the material properties and the melt pool dimension and shape have been established, and the regression-predicted results show a reasonable agreement with the simulation results for tested real-world materials. However, errors still exist for materials with a small melt pool such as copper.

Powder-Scale Meshfree Simulations of Powder Bed Fusion Based Additive Manufacturing Processes

2019 ◽  
Author(s):  
Zongyue Fan ◽  
Hao Wang ◽  
Bo Li
Keyword(s):  
Additive Manufacturing ◽  
Optimal Transportation ◽  
The Novel ◽  
Powder Bed Fusion ◽  
Computational Framework ◽  
Powder Bed ◽  
Materials Behaviors ◽  
Melt Pool ◽  
Powder Particles ◽  
Am Processes

Abstract We present a powder-scale meshfree direct numerical simulation (DNS) capability for the powder bed fusion (PBF) based additive manufacturing (AM) processes using the novel Hot Optimal Transportation Meshfree (HOTM) method. The HOTM method is an incremental Lagrangian meshfree computational framework for materials behaviors under extreme thermomechanical loading conditions, which combines the Optimal Transportation Meshfree (OTM) method and the variational thermomechanical constitutive updates. The realistic multi-layer powder bed geometry is modeled explicitly in the HOTM simulations based on experimental data. A phase-aware constitutive model is developed to predict the phase change and multiphase mixing during the PBF AM processes automatically. The governing equations including the linear momentum and energy conservation equations are solved for the multiphase flow simultaneously to predict the deformation, temperature and local state of the powder particles. The powder-scale DNS is employed to study the influence of various laser powers on the melt pool thermodynamics.

Ultrasonics for monitoring melt pool dynamics and in situ sensing of microstructure during powder bed fusion additive manufacturing

2021 ◽  
Vol 150 (4) ◽  
pp. A307-A307
Author(s):  
Christopher M. Kube ◽  
Nathan Kizer ◽  
Abdalla Nassar ◽  
Edward Reutzel ◽  
Haifeng Zhang ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
In Situ Sensing
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