Luding, S.

Shaheen, M.Y.

Thornton, A.R.

Weinhart, T.

Wits, W.W.

English

NARCIS 

Multiscale modelling of agglomeration: Selective laser sintering

University of Twente Research Information

MESA+ Meeting, 25 September 2017.Multiscale Modelling of AgglomerationSelective Laser SinteringM.Y.Shaheen1, T.Weinhart1, W.W.Wits2, A.R.Thornton1, S.Luding11 Multiscale Mechanics, MESA+, Engineering Technology, University of Twente.2 Design, Production & Management, Engineering Technology, University of Twente.IntroductionAdditive manufacturing (AM) is part of the 4th industrial revolution.Optimising the process will allow engineers to realise previously impos-sible designs, expanding the realm of possible application. The trendsin industry towards AM and 3D printing have motivated our interest tosimulate such processes.One AM method is Selective Laser Sintering (SLS), in which objects areproduced by depositing successive layers of powder particles (plastic,metal, ceramics, or glass), and sintering parts by selectively scanning thepowder bed with a laser, as shown in Fig.1. The technology is used invarious fields, e.g. industrial design, automotive, biotech, aerospace andmany other.Figure 1: Schematic of SLS process (source: wikipedia.org)Aims and Challenges• The aim of this study is to develop a multiscale model of powderagglomeration that will allow us to predict bulk processes in additivemanufacturing, pharmaceutics,...etc. We will first apply this modelto the selective laser sintering process.• Research approach:– Temperature dynamics.– Coupling between the individual scales.ApproachA novel two-scale approach (micro-meso and meso-macro) is used to couplethe different scales, shown in Fig.2:• Microscale contact model (including rapid temperature changes).• Transition micro- to mesoscale variables: calibration by experiments.• Mesoscale: particles do not represent single particle but ensembles.• Transition from meso- to macro-variables: ’coarse-graining’.• Predict and optimise processes with macro-modelling.Figure 2: Schematic overview of the new two-stage approachSub-processes• Powder deposition methods: slider, roller, rake,...etc.• The use of virgin powder vs used powder.• Powder particles distribution.• Process parameters effect on product quality (temperature, orienta-tion,...etc).Experiments• In-house experiments will be conducted to take into account therapid surface melting and sintering process in SLS.– Sintratec kit, see Fig.3, is used for the experimental trials.– Initial control parameters: laser speed, chamber temperature,surface temperature, layer thickness, number of primeters,perimeter offset, hatch offset, and hatch spacing.• Industrial validation and case-study experiments of the method areconducted in collaboration with other organizations and institutes.Figure 3: Sintratec kitresultsPreliminary work:Contact model for sintering[1-2]Figure 4: left: two-particle contact with overlap δ. middle: contact model forthe normal (repulsive) contact force as a function of the overlap. right: schematicplot of the stiffness k1 as a function of the temperature.Deposition method[3]An answer to these questions will be provided by nine simulations based on a full factorial of two parameters with three settings. These two parameters are the powder size distribution and the compaction method. 2 PARAMETRICAL SIMULATION SETUP  In this research, three significant different powder size distributions are selected. These three represent the different possibilities for the powder size distribution within SLS. They have been chosen in order to co pare their influence on the density after applying a compaction method. The first powder size distribution (uniform) has been compared with the second (Gaussian) due to the differences in distribution of the mean particle diameter and the surrounding diameters. The second and third (mono dispersed) powder size distributions have been compared because they differ in Standard Deviation (σ). Finally, the mono dispersed distribution has been compared with the uniform distribution in terms of the influence on the density if one particle size is chosen instead of a broad scale of equal distributed particle sizes. Nine simulations are executed as a result of a full factorial of two parameters with three settings. The settings of the powder size distribution parameter are shown in table 1.  Table 1. Settings of parameter powder size distribution  Uniform dispersed distribution Gaussian distribution Mono dispersed distribution Units Min. particle diameter 37.5 - - µm Max. particle diameter 62.5 - - µm Std. deviation 25 25 5 % Mean particle diameter 50 50 50 µm  Besides the powder size distribution, settings for the compaction methods are defined. These are shown in figure 1. The blade which is used in combination with the forward rotating roller, has a thickness of 0.5 mm. This is the same thickness as Budding and Vaneker (2013) used in their experiments. Their  research discussed methods of powder deposition and carried out practical experiments using different  Fig. 1. Different compactions methods: forward rotating roller, counter rotating roller, and a forward rotating roller with a blade in front of it. compaction methods.   The roller that was used in the simulations has a diameter of 22 mm, which is in accordance with one of the rollers used in the experiments of Budding and Vaneker (2013). Traversing speed and rim speed of the roller are both set at 100 mm/s, in accordance with Niino’s (2009) experimental result that has led to a successful build of a test part. The mentioned roller diameter and thickness are scaled down by a factor hundred, and the roller speed is scaled down by a factor ten, to gain a more realistic result on the small scale on which the simulations are executed. A box of particles with a size (before compaction) of 1.0×0.25×0.12 mm3 is used. About 600 particles (dependent on the applied particle size distribution) fit in this box and will be simulated.  Some more general simulation settings, which are constant for every unique simulation, are selected as well. The powder material is Pa12, as this is one of the most common materials used for SLS (Idacavage & Stansbury, 2016). The material density is adopted as 1025 kg/m3, the elastic modulus is set at 1900 MPa and the shear modulus is set at 400 MPa (Matbase, Engineering polymers). The simulated particles have a spherical form. In reality, these particles do not have a (fully) spherical form which leads to a certain friction between the particles (Bernard et al., 2010). To get a more realistic simulation, this friction is implemented in the simulation by an adjustable friction coefficient, which is set at 0.5. This ensures the particles to avoid sliding unrealistic smoothly past each other. Another general simulation setting is the layer thickness before compaction. It is set at 0.12 mm, whereby the gap between the roller (and optionally blade) and the previously sintered layer is set at 0.06 mm, which corresponds to a compaction factor of 2.0. A compaction factor of 2.0 has been chosen because this is the maximum factor that will not lead to craters on the powder bed surface according to 2  Budding and Vaneker (2013). A layer of 0.6 mm is chosen since compaction of thin layers leads to a higher density after compaction in comparison to the compaction of relatively thick layers (Asad & Broek, 2016, Budding & Vaneker, 2013).   With all the previously discussed settings defined in MercuryDPM (Thornton et al., 2013), the combination of a particle size distribution and compacting method which delivers the highest powder bed density can be found. 3 RESULTS OF SIMULATIONS The parameter that describes the quality of the packing density is the volume fraction of the particles. This parameter is defined as the total volume of the particles divided by the volume of the powder bed taken by these particles. Figure 2 shows the volume fraction for thr e out of ni e numerical simulations after compaction. These three are shown because they have the most desirable powder bed density, when compared with the other six simulations. Figure 2A shows that the highest volume fraction is 0.7 for the combination of a mono dispersed powder size distribution with a counter rotating roller. If the powder bed consists of uniform distributed sizes of particles which are compacted by the use of a forward rotating roller, the highest obtained volume fraction is 0.65, which can be seen from figure 2B. The use of uniform size distributed particles and a counter rotating roller results in the highest volume fraction of 0.7, which is shown in figure 2C. T  simu ation result of the simulat on corresponding to figure 2C is visualised in figure 3.   The roller is translated into the particle bed until the correct depth is reached, after which the roller starts moving in the direction to the right.     Fig. 3. Simulation results of uniform powder distribution in combination with a counter rotating roller. (Chronological in normal reading order from top-left to bottom-right.) Figure 2 shows an X-axis (length of powder bed) a  Z-axis (depth of powder bed).  The simulations are continuous, which results in the effect of particles exiting the right boundary. These particles are added to the right side of the left boundary. This effect is visible in the bottom-right picture of figure 3. However, this is definitely not realistic. The realistic effect of the compaction method on the powder bed is only present close behind the moving roller, i.e. the particles that are not influenced by the added particles at the left boundary. Therefore, the value of the X-dimension in figure 2 starts at 6.5·10-4 m and ends at 8·10-4 m, because this represents the part of the powder bed that is the least influenced by the particles entering from the left. Lastly, it can be noticed from figure 2 that the highest volume fraction of the numerical simulations is present around Z = 3·10-4 m.  4 DISCUSSION According to the discussed results, it can be concluded that 2C has a higher volume fraction (0.7) than the volume fraction of 2B (0.65). Figure 2B and 2C have been compared because they both have the 3  Figure 5: left: different compactions methods: forward rotating roller, counterrotating roller, and a forward rotating roller with a blade in front of it. right:simulation results of unifor powder di tribution in combination with a counterrotating roller.H at di spation[4]Table 2. Powder bed properties Dimension Value Length  500 µm Width 500 µm Depth 500 µm Polydispersity 10% Initial temperature 300 K  In t  setup, s own in figure 4, one particle in the middle of the top layer is heated to its final temperature, providing for the possibility to sinter with the surrounding particles (Tg).   Figure 4: Simulation setup with one heated particle in the middle of the top layer, providing for the possibility to sinter with the surrounding particles. 3 VERIFICATION To verify the simulation made with MercuryDPM (Thornton, et al., 2013) a simpl  setup shown in figure 5 is sim lated and compared with ma ual calculations using the equations and properties described in chapter 2.1 and 2.2.   Figure 5: Simple verification setup The setup consists of only two particles on top of each other. The top particle is at the final temperature of 450 K. The setup is needed to test how long it takes for e bottom particle to r ach the final temperature. The initial temperature of th  bottom particle is 300 K. The results e shown in figure 6.  Figure 6: Verification test The simulation and calculation show almost the same results. Due to the different integration methods, there is a slight inco sistency. 4 RESULTS As shown in the verification, MercuryDPM is a program that can correctly simulate the heat transfer between particles in a SLS 3D’s printing process.   Figure 7 shows a plot of the heat transfer in the simulation setup described in chapter 2.3 after 0.5 seconds of heating.  The Y axes is the depth of the powder bed and X axes is the radial distance to the heated particle in the horizontal plane. The colour of the graph represents the temperature of the particles.  This figure shows how the heat is transferred from the heated particle to the surrounding particles by conductivity.  The heat transfers more quickly beneath the particle due to the force of gravity. Gravity forces the particles down, resulting in a bigger contact area and higher heat transfer.    Figure 7: Plot of the heat transfer in a 500*500*500 µm powder bed after 0.5 seconds of heating.  5 CONCLUSION The heat transfer in the powder bed in selective laser sintering is numerically calculated by a DEM model program: MercuryDPM. In this model, it is assumed that thermal conduction causes most of the heat flow. Leading heat flow by radiation and convection to be disregarded. The simulation can accurately predict the thermal conductivity in the powder bed.  A simple verification setup is used to support this statement. This setup is simulated and verified by comparing the results with manual calculations.  The plot (figure 7) of heat transfer in a powder bed shows that the particles are being heated more quickly in the vertical direction than in the horizontal direction. An interesting future direction would be to simulate a laser causing to heat multiple particles throughout the powder layer as opposed to the constant heating of only one particle. Then one should find out, once more, what effects conductive heat flow has on the surrounding particles.  ACKNOWLEDGEMENTS The authors would like to thank Thomas Weinhart, Anthony Thornton and Wessel Wits for the helpful discussions, information and the providing code for the simulation.  REFERECES Ganeriwala, R., & Zohdi, T. I. (2015). A coupled discrete element-finite difference model of selective laser sintering. Springer-Verlag Berlin Heidelberg 2016. Konstantinou, I., & Vosniakos, G. C. (2015). Rough-cut fast numerical investigation of temperature fields in Selective Laser Sintering/Melting. Athens: National Technical Universtiy of Athens. Luding, S. (1998). Collisions & Contacts between Two Particles. NATO ASI Series, 285-304. Thornton, A., Krijgsman, D., Fransen, R., Gonzalez, S., Tunuguntla, D., te Voortwis, A., . . . Weinhart, T. (2013). Mercury-DPM: Fast particle simulations in complex geometries. Newsletter EnginSoft, 10, 48-53. Tolochko, N., Arshinov, M., Gusarov, A., Titov, V., Laoui, T., & Froyen, L. (2003). Mechanisms of selective laser sintering and heat transfer in Ti powder. Rapid Prototyping Journal, 9(5), 314-326. Zeng, K., Stucker, B., & Pal, D. (2012). A review of thermal analysis methods in laser sintering and Selective Laser Melting. Louisville: University of Louisville.     Figure 6: left: simualtion of heat dissipation in po der bed. right: plot f thetemperature distribution in a 500*500*500 µm powder bed after 0.5 eco ds ofheating.Referenc s[1] S Ludi g et al, J Mech Phy Solids 53(2), 455-491, 2005.[2] F chs et al, EPJ Web C nf. 140, 2017.[3] R. A ad, D.S. ten Broek, Pr maste project, UT, 2016.[4] R.E. Rusticus, J.H. Zaaijer, Premaster project, UT, 2016.1

Multiscale modelling of agglomeration:Selective laser sintering

Multiscale modelling of agglomeration:Selective laser sintering

Abstract

Similar works

Full text

Available Versions

NARCIS

University of Twente Research Information