Nanoparticle Sintering Model: Simulation and Calibration Against Experimental Data

2018 ◽  
Vol 6 (4) ◽  
Author(s):  
Obehi G. Dibua ◽  
Anil Yuksel ◽  
Nilabh K. Roy ◽  
Chee S. Foong ◽  
Michael Cullinan
Keyword(s):  
Experimental Data ◽  
Model Simulation ◽  
Selective Laser Sintering ◽  
Melting Process ◽  
Phase Field Modeling ◽  
Feature Size ◽  
Field Modeling ◽  
Minimum Feature Size ◽  
Am Processes ◽  
Metal Additive

One of the limitations of commercially available metal additive manufacturing (AM) processes is the minimum feature size most processes can achieve. A proposed solution to bridge this gap is microscale selective laser sintering (μ-SLS). The advent of this process creates a need for models which are able to predict the structural properties of sintered parts. While there are currently a number of good SLS models, the majority of these models predict sintering as a melting process which is accurate for microparticles. However, when particles tend to the nanoscale, sintering becomes a diffusion process dominated by grain boundary and surface diffusion between particles. As such, this paper presents an approach to model sintering by tracking the diffusion between nanoparticles on a bed scale. Phase field modeling (PFM) is used in this study to track the evolution of particles undergoing sintering. Changes in relative density are then calculated from the results of the PFM simulations. These results are compared to experimental data obtained from furnace heating done on dried copper nanoparticle inks, and the simulation constants are calibrated to match physical properties.

Nanoparticle Sintering Model, Simulation and Calibration Against Experimental Data

2018 ◽  
Author(s):  
Obehi G. Dibua ◽  
Anil Yuksel ◽  
Nilabh K. Roy ◽  
Chee S. Foong ◽  
Michael Cullinan
Keyword(s):  
Experimental Data ◽  
Model Simulation ◽  
Selective Laser Sintering ◽  
Melting Process ◽  
Phase Field Modeling ◽  
Feature Size ◽  
Field Modeling ◽  
Minimum Feature Size ◽  
Am Processes ◽  
Metal Additive

One of the limitations of commercially available metal Additive Manufacturing (AM) processes is the minimum feature size most processes can achieve. A proposed solution to bridge this gap is microscale selective laser sintering (μ-SLS). The advent of this process creates a need for models which are able to predict the structural properties of sintered parts. While there are currently a number of good SLS models, the majority of these models predict sintering as a melting process, which is accurate for microparticles. However, when particles tend to the nanoscale, sintering becomes a diffusion process dominated by grain boundary and surface diffusion between particles. As such, this paper presents an approach to model sintering by tracking the diffusion between nanoparticles on a bed scale. Phase Field Modeling (PFM) is used in this study to track the evolution of particles undergoing sintering. Part properties such as relative density, porosity, and shrinkage are then calculated from the results of the PFM simulations. These results are compared to experimental data gotten from a Thermogravimetric Analysis done on dried copper nanoparticle inks, and the simulation constants are calibrated to match physical properties.

Simulation and Characterization of Nanoparticle Thermal Conductivity for a Microscale Selective Laser Sintering System

2021 ◽  
Author(s):  
Joshua Grose ◽  
Obehi G. Dibua ◽  
Dipankar Behera ◽  
Chee S. Foong ◽  
Michael Cullinan
Keyword(s):  
Thermal Conductivity ◽  
Selective Laser Sintering ◽  
Primary Source ◽  
Laser Sintering ◽  
Metal Nanoparticle ◽  
Feature Size ◽  
Cad Models ◽  
Minimum Feature Size ◽  
Geometric Arrangements ◽  
Thermal Simulations

Abstract Additive Manufacturing (AM) technologies are often restricted by the minimum feature size of parts they can repeatably build. The microscale selective laser sintering (μ-SLS) process, which is capable of producing single micron resolution parts, addresses this issue directly. However, the unwanted dissipation of heat within the powder bed of a μ-SLS device during laser sintering is a primary source of error that limits the minimum feature size of the producible parts. A particle scale thermal model is needed to characterize the thermal properties of the nanoparticles undergoing sintering and allow for the prediction of heat affected zones (HAZ) and the improvement of final part quality. Thus, this paper presents a method for the determination of the effective thermal conductivity of metal nanoparticle beds in a microscale selective laser sintering process using finite element simulations in ANSYS. CAD models of nanoparticle groups at various timesteps during sintering are developed from Phase Field Modeling (PFM) output data, and steady state thermal simulations are performed on each group. The complete simulation framework developed in this work is adaptable to particle groups of variable sizes and geometric arrangements. Results from the thermal models are used to estimate the thermal conductivity of the copper nanoparticles as a function of sintering duration.

Design and Modeling of a Microscale Selective Laser Sintering System

2016 ◽  
Author(s):  
Nilabh Roy ◽  
Anil Yuksel ◽  
Michael Cullinan
Keyword(s):  
Heat Transfer ◽  
Additive Manufacturing ◽  
Selective Laser Sintering ◽  
Laser Sintering ◽  
Feature Size ◽  
Powder Bed ◽  
Metal Additive Manufacturing ◽  
Wave Nature ◽  
Field Radiation ◽  
Metal Additive

The development of micro and nanoscale additive manufacturing methods in metals and ceramics is important for many applications in the aerospace, medical device, and electronics industries. Unfortunately, most commercially available metal additive manufacturing tools have feature-size resolutions of greater than 100 μm, which is too large to precisely control the microstructure of the parts they produce. A few research-grade metal additive manufacturing tools do exist, but their build rate is generally too slow for commercial applications. Therefore, this paper presents a new microscale selective laser sintering (μ-SLS) that can be used to improve the minimum feature-size resolution of metal additively manufactured parts by up to two orders of magnitude, while still maintaining the throughput of traditional additive manufacturing processes. In order to achieve this goal, several innovative design features like the use of (1) ultra-fast lasers, (2) a micro-mirror based optical system, (3) nanoscale powders, and (4) a precision spreader mechanism, have been implemented. The micro-SLS system is capable of achieving build rates of approximately 1 cm3/hr while achieving a feature-size resolution of approximately 1 μm. This paper will also present new molecular scale models that have been developed for the micro-SLS to quantify and certify the micro-SLS build process. Modeling of the micro-SLS process is challenging, because most macroscale models of the SLS process contain assumptions that are no longer valid when the size of the particles that are being sintered is smaller than the wavelength of the laser being used to sinter them. Therefore, in modeling the micro-SLS process we must account for the wave nature of light and can no longer rely on the ray tracing models commonly used to model the SLS process. Also, heat transfer in the micro-SLS process is dominated by near-field radiation due to the diffraction of the light off the nanoparticles in the powder bed and the ultrafast lasers that are used in the micro-SLS system. This means that the assumptions of heat transfer by conduction and far-field radiation in the macroscale SLS systems are no longer valid for the micro-SLS system. Finally, the agglomeration of nanoparticles in the powder bed must be accurately modeled in order to precisely predict the formation of defects in the final parts produced. Overall, the goal of this modeling effort is to be able to predict the quality of a part produced using any given processing conditions, in order to produce parts that are “born certified” and do not need to be tested post fabrication.

A process for estimating minimum feature size in selective laser sintering

2018 ◽  
Vol 24 (2) ◽  
pp. 436-440 ◽  
Author(s):  
Benjamin Weiss ◽  
Olaf Diegel ◽  
Duane Storti ◽  
Mark Ganter
Keyword(s):  
Interpolation Method ◽  
Selective Laser Sintering ◽  
Dimensional Accuracy ◽  
Minimum Size ◽  
Design Rule ◽  
Feature Size ◽  
Content Type ◽  
Wide Range ◽  
Rule Sets ◽  
Minimum Feature Size

Purpose Manufacturer specifications for the resolution of an additive manufacturing (AM) machine can be ten times smaller (more optimistic) than the actual size of manufacturable features. Existing methods used to establish a manufacturable design rule-set are conservative piecewise-constant approximations. This paper aims to evaluate the effectiveness of a first-order model for producing improved design rule-sets for feature manufacturability, accounting for process variation. Design/methodology/approach A framework is presented which uses an interpolation method and a statistical model to estimate the minimum size for a wide range of features from a set of iterative experiments. Findings For an SLS process, using this approach improves the accuracy and reliability of minimum feature size estimates for a wider variety of features than assessed by most existing test artifacts. Research limitations/implications More research is needed to provide better interpolation models, broaden applicability and account for additional geometric and process parameters which significantly impact the results. This research focuses on manufacturability and does not address dimensional accuracy of the features produced. Practical implications An application to the design of thin channels in a prosthetic hand shows the utility of the results in a real-world scenario. Originality/value This study is among the first to investigate statistical variation of “pass/fail” features in AM process characterization, propose a means of estimating minimum feature sizes for shapes not directly tested and incorporate a more efficient iterative experimental protocol.

scholarly journals Parameter identification for phase-field modeling of fracture: a Bayesian approach with sampling-free update

2020 ◽  
Author(s):  
T. Wu ◽  
B. Rosić ◽  
L. De Lorenzis ◽  
H. G. Matthies
Keyword(s):  
Experimental Data ◽  
Mechanical Properties ◽  
Bayesian Approach ◽  
Phase Field ◽  
Computational Cost ◽  
Bending Test ◽  
Bayesian Posterior Probability ◽  
Phase Field Modeling ◽  
Numerical Predictions ◽  
Field Modeling

AbstractPhase-field modeling of fracture has gained popularity within the last decade due to the flexibility of the related computational framework in simulating three-dimensional arbitrarily complicated fracture processes. However, the numerical predictions are greatly affected by the presence of uncertainties in the mechanical properties of the material originating from unresolved heterogeneities and the use of noisy experimental data. The objective of this work is to apply the Bayesian approach to estimate bulk and shear moduli, tensile strength and fracture toughness of the phase-field model, thus improving accuracy of the simulations with the help of experimental data. Conventional approaches for estimating the Bayesian posterior probability density function adopt sampling schemes, which often require a large amount of model estimations to achieve the desired convergence, thus resulting in a high computational cost. In order to alleviate this problem, we employ a more efficient approach called sampling-free linear Bayesian update, which relies on the evaluation of the conditional expectation of parameters given experimental data. We identify the mechanical properties of cement mortar by conditioning on the experimental data of the three-point bending test (observations) in an online and offline manner. In the online approach the parameter values are sequentially updated on the fly as the new experimental information comes in. In contrast, the offline approach is used only when the whole history of experimental data is provided once the experiment is performed. Both versions of estimation are discussed and compared by validating the phase-field fracture model on an unused set of experimental data.

scholarly journals Beamless Metal Additive Manufacturing

Materials ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 922 ◽  
Author(s):  
Mohammad Vaezi ◽  
Philipp Drescher ◽  
Hermann Seitz
Keyword(s):  
Additive Manufacturing ◽  
Selective Laser Sintering ◽  
Metal Powders ◽  
Metal Additive Manufacturing ◽  
Wide Range ◽  
Anisotropic Mechanical Properties ◽  
High Production ◽  
High Production Cost ◽  
Am Processes ◽  
Metal Additive

The propensity to manufacture functional and geometrically sophisticated parts from a wide range of metals provides the metal additive manufacturing (AM) processes superior advantages over traditional methods. The field of metal AM is currently dominated by beam-based technologies such as selective laser sintering (SLM) or electron beam melting (EBM) which have some limitations such as high production cost, residual stress and anisotropic mechanical properties induced by melting of metal powders followed by rapid solidification. So, there exist a significant gap between industrial production requirements and the qualities offered by well-established beam-based AM technologies. Therefore, beamless metal AM techniques (known as non-beam metal AM) have gained increasing attention in recent years as they have been found to be able to fill the gap and bring new possibilities. There exist a number of beamless processes with distinctively various characteristics that are either under development or already available on the market. Since this is a very promising field and there is currently no high-quality review on this topic yet, this paper aims to review the key beamless processes and their latest developments.

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