Microscale Selective Laser Sintering Research Articles

Characterizing process window for microscale selective laser sintering

This paper outlines the experimental approach to create a sintering window for a microscale metal additive manufacturing (AM) process. The experimental framework discussed in the paper involves fabricating sintered features by varying pattern area and laser irradiance. The sintered features are then imaged under a microscope and the processed images are compared against an ideal image to quantify the uncertainty in near-net shape of the feature. Evaluating this data defines the process window within which good sintering and near-net shaped features can be expected in the microscale AM process.

Predicting Electrical Resistivity of Sintered Copper Nanoparticles From Simulations for the Microscale Selective Laser Sintering Process

Abstract One of the main challenges facing the expansion of Additive Manufacturing (AM) is the minimum feature sizes which these processes are able to achieve. Microscale Selective Laser Sintering (μ-SLS) is a novel Additive Manufacturing process created to meet this limitation by precisely laser sintering nanoparticles to give a better control over feature sizes. With the development of this new process, there is a concurrent need for models, which can predict the material properties of the sintering nanoparticles. To this end, this paper presents a novel simulation created to predict the electrical resistivity of sintered copper nanoparticles. Understanding the electrical resistivity of nanoparticles under sintering is useful for quantifying the rate of sintering and has applications such as predicting how the nanoparticles will fuse together when subjected to laser irradiation. Such a prediction allows for in situ corrections to be made to the sintering process to account for heat spreading beyond the intended laser irradiation targets. For these applications, it is important to ensure that the predictions of electrical resistivity from the simulations are accurate. This validation must be done against experimental data and since such experimental data does not currently exist, this paper also presents electrical resistivity data for the laser sintering of copper nanoparticles. In summary, this paper details the simulation methodology for predicting electrical resistivity of laser-sintered copper nanoparticles as well as validation of these simulations using electrical resistivity data from original sintering experiments. The key findings of this work are that the simulations can be used to predict electrical resistivity measurements for sintering of actual copper nanoparticles when the copper nanoparticles do not include other materials such as polymer coatings.

Data-Efficient Surrogate Model for Rapid Prediction of Temperature Evolution in a Microscale Selective Laser Sintering System

Abstract Current metal additive manufacturing (AM) systems suffer from limitations on the minimum feature sizes they can produce during part formation. The microscale selective laser sintering (μ-SLS) system addresses this drawback by enabling the production of parts with minimum feature resolutions of the order of a single micrometer. However, the production of microscale parts is challenging due to unwanted heat conduction within the nanoparticle powder bed. As a result, finite element (FE) thermal models have been developed to predict the evolution of temperature within the particle bed during laser sintering. These thermal models are not only computationally expensive but also must be integrated into an iterative model-based control framework to optimize the digital mask used to control the distribution of laser power. These limitations necessitate the development of a machine learning (ML) surrogate model to quickly and accurately predict the temperature evolution within the μ-SLS particle bed using minimal training data. The regression model presented in this work uses an “Element-by-Element” approach, where models are trained on individual finite elements to learn the relationship between thermal conditions experienced by each element at a given time-step and the element's temperature at the next time-step. An existing bed-scale FE thermal model of the μ-SLS system is used to generate element-by-element tabular training data for the ML model. A data-efficient artificial neural network (NN) is then trained to predict the temperature evolution of a 2D powder-bed over a 2 s sintering window with high accuracy.

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

Abstract Current additive manufacturing (AM) technologies are typically limited by the minimum feature sizes of the parts they can produce. This issue is addressed by the microscale selective laser sintering system (μ-SLS), which is capable of building parts with single micrometer resolutions. Despite the resolution of the system, the minimum feature sizes producible using the μ-SLS tool are limited by unwanted heat dissipation through the particle bed during the sintering process. To address this unwanted heat flow, a particle scale thermal model is needed to characterize the thermal conductivity of the nanoparticle bed during sintering and facilitate the prediction of heat affected zones. This would allow for the optimization of process parameters and a reduction in error for the final part. This paper presents a method for the determination of the effective thermal conductivity of copper nanoparticle beds in a μ-SLS system using finite element simulations performed in ansys. A phase field model (PFM) is used to track the geometric evolution of the particle groups within the particle bed during sintering. Computer aided design (CAD) models are extracted from the PFM output data at various time-steps, and steady-state thermal simulations are performed on each particle group. The full simulation developed in this work is scalable to particle groups with variable sizes and geometric arrangements. The particle thermal model results from this work are used to calculate the thermal conductivity of the copper nanoparticles as a function of the density of the particle group.

Passive intensity modulation of a pattern for fabricating near-net shaped features in microscale metal additive manufacturing

A large variation in part size across the projection area of the microscale selective laser sintering process is caused by non-uniform intensity projected onto the substrate by the digital micromirror device (DMD). A corrective gray scale mask with varying intensities was mapped according to a sintered array of parts. The variance in part size across the area was reduced by as much as 45% by using this technique. This approach can be extended to in real-time process control to create a more uniform distribution of heat and in turn better near net shape parts across the projection area.

Calibration uncertainty in nanoparticle sintering simulations

Previous papers have demonstrated how nanoparticles in the Microscale Selective Laser Sintering process densify under conditions of isothermal heating, yielding a time calibration factor which relates the predicted sintering time to simulation time. However, it is important to quantify the uncertainty related to the time calibration factor in order to state with any degree of confidence the sintering window during which the physical process can be expected to achieve the degree of sintering predicted by the simulation. As such, the goal of this paper is to quantify the uncertainty related to the time calibration factor.

Slot-Die Coating Operability Window for Nanoparticle Bed Deposition in a Microscale Selective Laser Sintering Tool

Abstract This work seeks to develop a fundamental understanding of slot-die coating as a nanoparticle bed deposition mechanism for a microscale selective laser sintering (μ-SLS) process. The specific requirements of the μ-SLS process to deposit uniform sub-5 μm metal nanoparticle films while enabling high throughput fabrication make the slot-die coating process a strong candidate for layer-by-layer deposition. The key challenges of a coating system are to enable uniform nanoparticle ink deposition in an intermittent layer-by-layer manner. Identifying the experimental parameters to achieve this using a slot-die coating process is difficult. Therefore, the main contribution of this study is to develop a framework to predict the wet film thickness and onset of coating defects by simulating the experimental conditions of the μ-SLS process. The single-layer deposition characteristics and the operational window for the slot-die coating setup have been investigated through experiments and two-dimensional computational fluid dynamics simulations. The effect of coating parameters such as inlet speed, coating speed, and coating gap on the wet film thickness has been analyzed. For inlet speeds higher than the coating speed, it was found that the meniscus was susceptible to high instabilities leading to coating defects. Additionally, the study outlines the conditions for which the stability of the menisci upstream and downstream of the slot-die coater can affect the uniformity and thickness range of the coating.

Thermal Transport in Nanoparticle Packings Under Laser Irradiation

Abstract Nanoparticle heating due to laser irradiation is of great interest in electronic, aerospace, and biomedical applications. This paper presents a coupled electromagnetic-heat transfer model to predict the temperature distribution of multilayer copper nanoparticle packings on a glass substrate. It is shown that heat transfer within the nanoparticle packing is dominated by the interfacial thermal conductance between particles when the interfacial thermal conductance constant, GIC, is greater than 20 MW/m2K, but that for lower GIC values, thermal conduction through the air around the nanoparticles can also play a role in the overall heat transfer within the nanoparticle system. The coupled model is used to simulate heat transfer in a copper nanoparticle packing used in a typical microscale selective laser sintering (μ-SLS) process with an experimentally measured particle size distribution and layer thickness. The simulations predict that the nanoparticles will reach a temperature of 730 ± 3 K for a laser irradiation of 2.6 kW/cm2 and 1304 ± 23 K for a laser irradiation of 6 kW/cm2. These results are in good agreement with the experimentally observed laser-induced sintering and melting thresholds for copper nanoparticle packing on glass substrates.

A novel microscale selective laser sintering (\u03bc-SLS) process for the fabrication of microelectronic parts

One of the biggest challenges in microscale additive manufacturing is the production of three-dimensional, microscale metal parts with a high enough throughput to be relevant for commercial applications. This paper presents a new microscale additive manufacturing process called microscale selective laser sintering (μ-SLS) that can produce true 3D metal parts with sub-5 μm resolution and a throughput of greater than 60 mm3/hour. In μ-SLS, a layer of metal nanoparticle ink is first coated onto a substrate using a slot die coating system. The ink is then dried to produce a uniform nanoparticle layer. Next, the substrate is precisely positioned under an optical subsystem using a set of coarse and fine nanopositioning stages. In the optical subsystem, laser light that has been patterned using a digital micromirror array is used to heat and sinter the nanoparticles into the desired patterns. This set of steps is then repeated to build up each layer of the 3D part in the μ-SLS system. Overall, this new technology offers the potential to overcome many of the current limitations in microscale additive manufacturing of metals and become an important process in microelectronics packaging applications.

Fast Trajectory Tracking of a Flexure-Based, Multiaxis Nanopositioner With 50-mm Travel

The precise, high-speed control of nanopositioning stages is critical for many microscale additive manufacturing systems, such as microscale selective laser sintering (μ-SLS), where high throughput is needed. In μ-SLS, the positioning stage requires achieving 10 Hz steps with sub-100 nm accuracy and a travel range of 50 mm. The open-loop resolution and settling time of the flexure stage presented in the study are found to be 63 nm and 1.27 s, respectively. This settling time is too large for its application in high-throughput μ-SLS. To improve the tracking performance of the stage for fast varying signals up to 10 Hz and achieve better resolution, this paper presents the design of a finite horizon linear quadratic regulator controller for the XY stage. Owing to the good damping properties of the controller, the resolution is improved to 8 nm and input signals with 10-Hz frequency are effectively tracked. With the addition of a resonant shifting control, the tracking bandwidth is improved to 23 Hz. The paper presents a unique combination of low cost, large travel, sub-10 nm resolution and high-bandwidth XY positioning system, which is not to be found in either off-the-shelf nanopositioning stages or in research laboratories.

Experimental Study of the Subsystems in a Microscale Additive Manufacturing Process

High-throughput manufacturing of complex 3D architectures for microscale products such as microelectronics is limited by the resolution of existing additive manufacturing processes. This paper presents experimental testing and validation of the major subsystems in a microscale selective laser sintering (µ-SLS) process that is capable of fabricating true-3D metallic micro-architectures with microscale feature size resolutions. In µ-SLS, the part quality and throughput of the sintering process are largely determined by the precision, accuracy, and speed of the subsystems including: (1) the optical subsystem, (2) the global positioning mechanism, (3) the XY nanopositioning stage, and (4) the powder bed dispensing system. This paper shows that each of these subsystems can maintain the sub-micrometer precision and accuracy required to produce metal parts with microscale resolutions. Preliminary sintering results with optimized process parameters show the potential of the µ-SLS process to fabricate complex metal parts with sub-10-μm resolution at high rates.

Nanoparticle Sintering Model: Simulation and Calibration Against Experimental Data

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.

Single shot, large area metal sintering with micrometer level resolution.

- This paper presents the optics design for a microscale Selective Laser Sintering (μ-SLS) system that aims to allow large areas of nanoparticles to be sintered simultaneously while still maintaining micrometer scale feature resolutions in order to improve the throughput of the microscale additive manufacturing process. The optics design is shown to be able to sinter a 2.3 mm by 1.3 mm area of metal nanoparticles that have been spread into a ~400 nm thick layer with a feature resolution of ~3 μm in a single shot. The optical resolution of this system is shown to be ~1.2 μm indicating that only about 2-3 pixels are needed to form a good sintered part. In addition, using the optical design presented in this paper, it is estimated that the μ-SLS system should be able to achieve a volumetric throughput of ~63 mm3/hr, making this process one of the highest throughput processes available today for the microscale additive manufacturing of three-dimensional metal structures.

Modeling of nanoparticle agglomeration and powder bed formation in microscale selective laser sintering systems

Additive manufacturing (AM) has received a great deal of attention for the ability to produce three dimensional parts via laser heating. One recently proposed method of making microscale AM parts is through microscale selective laser sintering (μ-SLS) where nanoparticles replace the traditional powders used in standard SLS processes. However, there are many challenges to understanding the physics of the process at nanoscale as well as with conducting experiments at that scale; hence, modeling and computational simulations are vital to understand the sintering process physics. At the sub-micron (μm) level, the interaction between nanoparticles under high power laser heating raises additional near-field thermal issues such as thermal diffusivity, effective absorptivity, and extinction coefficients compared to larger scales. Thus, nanoparticle's distribution behavior and characteristic properties are very important to understanding the thermal analysis of nanoparticles in a μ-SLS process. This paper presents a discrete element modeling (DEM) study of how copper nanoparticles of given particle size distribution pack together in a μ-SLS powder bed. Initially, nanoparticles are distributed randomly into the bed domain with a random initial velocity vector and set boundary conditions. The particles are then allowed to move in discrete time steps until they reach a final steady state position, which creates the particle packing within the powder bed. The particles are subject to both gravitational and cohesive forces since cohesive forces become important at the nanoscale. A set of simulations was performed for different cases under both Gaussian and log-normal particle size distributions with different standard deviations. The results show that the cohesive interactions between nanoparticles has a great effect on both the size of the agglomerates and how densely the nanoparticles pack together within the agglomerates. In addition, this paper suggests a potential method to overcome the agglomeration effects in μ-SLS powder beds through the use of colloidal nanoparticle solutions that minimize the cohesive interactions between individual nanoparticles.