scholarly journals A greedy algorithm for optimal heating in powder-bed-based additive manufacturing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Robert Forslund ◽  
Anders Snis ◽  
Stig Larsson
Keyword(s):  
Additive Manufacturing ◽  
Spot Size ◽  
Beam Power ◽  
Heat Accumulation ◽  
Powder Bed ◽  
Control Functions ◽  
Melt Pool ◽  
Local Properties ◽  
Beam Parameters ◽  
Maximum Temperatures

AbstractPowder-bed-based additive manufacturing involves melting of a powder bed using a moving laser or electron beam as a heat source. In this paper, we formulate an optimization scheme that aims to control this type of melting. The goal consists of tracking maximum temperatures on lines that run along the beam path. Time-dependent beam parameters (more specifically, beam power, spot size, and speed) act as control functions. The scheme is greedy in the sense that it exploits local properties of the melt pool in order to divide a large optimization problem into several small ones. As illustrated by numerical examples, the scheme can resolve heat conduction issues such as concentrated heat accumulation at turning points and non-uniform melt depths.

Thermo-Fluid Characterizations of Ti-6Al-4V Melt Pool in Powder-Bed Electron Beam Additive Manufacturing

2016 ◽  
Author(s):  
M. Shafiqur Rahman ◽  
Paul J. Schilling ◽  
Paul D. Herrington ◽  
Uttam K. Chakravarty
Keyword(s):  
Electron Beam ◽  
Additive Manufacturing ◽  
Complex Geometry ◽  
High Energy ◽  
Cfd Modeling ◽  
Beam Power ◽  
Rate Variation ◽  
Beam Diameter ◽  
Powder Bed ◽  
Melt Pool

The powder-bed electron beam additive manufacturing (EBAM) process is one of the relatively new additive manufacturing (AM) technologies in which the metal powder is melted in a vacuum environment utilizing a high-energy heat source to fabricate metallic parts in a layer by layer manner. Different metallic alloys (especially, high entropy alloys such as Ti-6Al-4V) have been widely studied as a powder-bed material for the EBAM. Despite the unique advantages of designing complex geometry and tooling-free manufacturing, there are still considerable challenges in the EBAM, e.g., obtaining desired metallurgical behavior, part accuracy, reliability, and quality consistency. Therefore, a better understanding of the thermo-fluid and mechanical properties of the EBAM process is indispensable to meet the challenges. In this study, transient computational fluid dynamics (CFD) modeling of Ti-6Al-4V melt pool has been done using ANSYS Fluent 15.0 to characterize the process parameters associated with the EBAM process including the melt pool geometry, beam power, beam speed, beam diameter, and temperature profile along the melt scan. In fact, the dynamics and the solidification of the melt pool have been investigated numerically and results for cooling rate, variation in density, pressure, velocities, and liquid fraction have been obtained to illustrate the versatility of the analysis.

scholarly journals Additive Manufacturing of Bulk Nanocrystalline FeNdB Based Permanent Magnets

Micromachines ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 538
Author(s):  
Dagmar Goll ◽  
Felix Trauter ◽  
Timo Bernthaler ◽  
Jochen Schanz ◽  
Harald Riegel ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Melt Spinning ◽  
Permanent Magnets ◽  
Maximum Energy ◽  
Laser Melting ◽  
Powder Bed ◽  
Melt Pool ◽  
Bulk Nanocrystalline ◽  
Ribbon Structures ◽  
Nanocrystalline Ribbon

Lab scale additive manufacturing of Fe-Nd-B based powders was performed to realize bulk nanocrystalline Fe-Nd-B based permanent magnets. For fabrication a special inert gas process chamber for laser powder bed fusion was used. Inspired by the nanocrystalline ribbon structures, well-known from melt-spinning, the concept was successfully transferred to the additive manufactured parts. For example, for Nd16.5-Pr1.5-Zr2.6-Ti2.5-Co2.2-Fe65.9-B8.8 (excess rare earth (RE) = Nd, Pr; the amount of additives was chosen following Magnequench (MQ) powder composition) a maximum coercivity of µ0Hc = 1.16 T, remanence Jr = 0.58 T and maximum energy density of (BH)max = 62.3 kJ/m3 have been achieved. The most important prerequisite to develop nanocrystalline printed parts with good magnetic properties is to enable rapid solidification during selective laser melting. This is made possible by a shallow melt pool during laser melting. Melt pool depths as low as 20 to 40 µm have been achieved. The printed bulk nanocrystalline Fe-Nd-B based permanent magnets have the potential to realize magnets known so far as polymer bonded magnets without polymer.

scholarly journals Heavy-ion-induced hydrodynamic motion in lead targets

2000 ◽  
Vol 18 (4) ◽  
pp. 573-581 ◽  
Author(s):  
S. STÖWE ◽  
U. NEUNER ◽  
R. BOCK ◽  
M. DORNIK ◽  
V.E. FORTOV ◽  
...  
Keyword(s):  
Ion Beam ◽  
Heavy Ion ◽  
Spot Size ◽  
Hydrodynamic Motion ◽  
Deposition Power ◽  
Metal Volume ◽  
Beam Parameters ◽  
Maximum Temperatures ◽  
Volume Targets ◽  
Hydrodynamic Code

The hydrodynamic response of metal targets to volume heating by energy deposition of intense heavy-ion beams was investigated experimentally. Recent improvements in beam parameters led to a marked increase in specific deposition power: 2·101040Ar18+ ions of 300 MeV/u focused to a spot size of 300 μm (σ) × 540 μm (σ) yield a specific deposition energy in solid lead of approximately 1 kJ/g in the Bragg peak, delivered within 250 ns [full width at half maximum (FWHM)]. This value allowed us for the first time to observe heavy-ion-beam-induced hydrodynamic expansion of metal volume targets. Measurements comprise expansion velocities of free surfaces of up to 290 ± 20 m/s, surface temperatures of ejected target matter of 1600–1750 K, and pressure waves in solid metal bulk targets of 0.16 GPa maximum absolute value and 0.8 μs FWHM. The experimental results agree well with the results of a 2D hydrodynamic code. Inside the interaction zone, which can only be accessed by simulation, maximum temperatures are 2800 K and maximum pressures are 3.8 GPa.

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.

Melt pool sensing and size analysis in laser powder-bed metal additive manufacturing

2018 ◽  
Vol 32 ◽  
pp. 744-753 ◽  
Author(s):  
Bo Cheng ◽  
James Lydon ◽  
Kenneth Cooper ◽  
Vernon Cole ◽  
Paul Northrop ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Size Analysis ◽  
Powder Bed ◽  
Metal Additive Manufacturing ◽  
Melt Pool ◽  
Metal Additive

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.

Simulation of metallic powder bed additive manufacturing processes with the finite element method: A critical review

2016 ◽  
Vol 231 (1) ◽  
pp. 96-117 ◽  
Author(s):  
Babis Schoinochoritis ◽  
Dimitrios Chantzis ◽  
Konstantinos Salonitis
Keyword(s):  
Finite Element ◽  
Additive Manufacturing ◽  
Experimental Validation ◽  
Metallic Powder ◽  
Manufacturing Processes ◽  
Powder Bed ◽  
Modeling Methodology ◽  
Melt Pool ◽  
Element Simulation ◽  
Melt Pool Characteristics

This article provides a literature review of finite element simulation studies for metallic powder bed additive manufacturing processes. The various approaches in the numerical modeling of the processes and the selection of materials properties are presented in detail. Simulation results are categorized according to three major findings’ groups (i.e. temperature field, residual stresses and melt pool characteristics). Moreover, the means used for the experimental validation of the simulation findings are described. Looking deeper into the studies reviewed, a number of future directions are identified in the context of transforming simulation into a powerful tool for the industrial application of additive manufacturing. Smart modeling approaches should be developed, materials and their properties should be further characterized and standardized, commercial packages specialized in additive manufacturing simulation have to be developed and simulation needs to become part of the modern digital production chains. Finally, the reviewed studies are organized in a table and characterized according to the process and material studied, the modeling methodology and the experimental validation method used in each of them. The key findings of the reviewed studies are also summarized.

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