Modeling of Powder Bed Manufacturing DefectsH.-W. Mindt, O. Desmaison, M. Megahed, A. Peralta, and J. Neumann

(Submitted October 10, 2016; in revised form December 27, 2016)

Powder bed additive manufacturing offers unmatched capabilities. The deposition resolution achieved isextremely high enabling the production of innovative functional products and materials. Achieving thedesired final quality is, however, hampered by many potential defects that have to be managed in due courseof the manufacturing process. Defects observed in products manufactured via powder bed fusion have beenstudied experimentally. In this effort we have relied on experiments reported in the literature and—whenexperimental data were not sufficient—we have performed additional experiments providing an extendedfoundation for defect analysis. There is large interest in reducing the effort and cost of additive manu-facturing process qualification and certification using integrated computational material engineering. Aprerequisite is, however, that numerical methods can indeed capture defects. A multiscale multiphysicsplatform is developed and applied to predict and explain the origin of several defects that have beenobserved experimentally during laser-based powder bed fusion processes. The models utilized are brieflyintroduced. The ability of the models to capture the observed defects is verified. The root cause of thedefects is explained by analyzing the numerical results thus confirming the ability of numerical methods toprovide a foundation for rapid process qualification.

Keywords additive manufacturing, defect modeling, powder bedfusion, powder coating, residual stresses and distortion

1. Introduction

During powder bed manufacturing processes thin layers(microns) of metal particles are spread on a processing table. Alaser or an electron beam melts the metallic powder in certainareas of the powder bed. These areas then solidify to become asection of the final build. An additional powder layer is thenadded, and the process is repeated. At the end of the buildprocess the un-melted powder is removed to reveal theworkpiece created (Ref 1, 2).

Certification identifies applicable standards to which com-pliance must be demonstrated and the means by which thiscompliance will be demonstrated. When pursued for additivemanufacturing safety issues that may arise due to uniquedesigns and materials are the focus of standardization efforts.Unknown defects unique to additive manufacturing or thosethat might have a larger impact on products due to the uniquemanufacturing process are of particular interest (Ref 3-5).

The traditional certification approach tends to be a sequen-tial and iterative process especially in material development andmanufacturing process development. Given the very largenumber of process parameters involved in the manufacturingprocess (Ref 2), the sequential and iterative procedure makes

the certification process costly and lengthy. Peralta et al.suggested the use of integrated computational engineering toreduce the time and cost associated with the qualification ofadditive manufacturing (Ref 4). A key requirement to enableintegrated computational materials engineering (ICME)-basedqualification is the ability of models to capture and predictdefects originating from the process and the manufacturingplan.

The most pertinent defects related to metal additivemanufacturing processes in general result from residual stressesaccumulated in the workpiece during the build process and thecorresponding distortion. In powder bed processes distortionsduring the build process can hinder the coater arm leading tothe build process being terminated. Excessive residual stressesdistort the workpiece that the final shape is no longer conformalto the originally intended CAD specification (Ref 6, 7).Porosity and cracking are the next large issue in powder bedsystems. Experimental studies systematically change processparameters to manufacture specimens that are analyzed todetermine material porosity. Materials studied include steel(Ref 8, 9), titanium alloys (Ref 10), nickel alloys (Ref 11),aluminum alloys (Ref 12), copper (Ref 13, 14) and gold (Ref15) just to list some examples. Choren et al. (Ref 16) gatheredcorrelations describing material properties as functions ofvolume porosity finding that most are limited in predictiveability.

Powder bed-based products usually display increased sur-face roughness. The roughness might be due to partially moltenpowder particles sticking to the product surface as well as dueto surface tension effects. Meier et al. (Ref 8) identified a heatsource power range that leads to a material density close to100% and low surface roughness. The wettability of a surfacemay be affected by enclosures or oxides leading to balling,which in their turn leads to increased porosity (Ref 17). Fosteret al. (Ref 18) studied a variety of other defects related topowder bed processes including insufficient powder spread onthe processing table and interaction between large particulateswith the coater arm.

This article is an invited paper selected from presentations at ‘‘RecentDevelopment in Additive Manufacturing: Process and EquipmentDevelopment and Applications,’’ held during MS&T�16, October 23-27, 2016, in Salt Lake City, UT, and has been expanded from theoriginal presentation.

H.-W. Mindt, O. Desmaison, and M. Megahed, ESI Group, Paris,France; and A. Peralta and J. Neumann, Honeywell Aerospace,Phoenix, AZ. Contact e-mail: Mustafa.Megahed@esi-group.com.

JMEPEG �ASM InternationalDOI: 10.1007/s11665-017-2874-5 1059-9495/$19.00

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This paper discusses the application of an ICME platform tonumerically capture defects generated during laser powder bedprocesses. We reference earlier team publications where ICMEcomponents� background was discussed in detail together withfundamental verification and validation results. Powder char-acteristics and process parameters shown to have led to certaindefects are investigated numerically. The ability (or the lack ofthis ability) of numerical techniques to capture defects ofpowder bed additive manufacturing is assessed by comparingnumerical predictions with experimental observations. Due tothe difficulty in designing experiments to create and quantifydefects, the comparisons are sometimes qualitative in nature.Models not only explain the origin of the defects but alsoprovide a basis for trend comparisons between differentmanufacturing conditions and show possible mitigation strate-gies to avoid such defects.

1.1 Numerical Models

Whereas the powder particles are a few tens of microns indiameter, the workpiece can be several cubic centimeters largerequiring the heat source to travel several thousands of metersduring the build process. The heat source powder interactiontime is in the order of a few microseconds, and the build timecan be several hours or days. A computational environmentmust therefore resolve very large differences in length and timescales, if defects are to be identified and results are to be used asa foundation for qualifying the process and the products. Toaddress the challenge of multiple length and time scales as wellas the multiple physics representations required, the authorshave subdivided the problem into micro-, macro- andmesoscale models. The micromodel characterizes the meltpool with length scales ranging from a few microns to a fewmillimeters delivering small-scale information pertinent to theprocess and material behavior. Identified local defects can behomogenized for use in higher scale macromodels, where theworkpiece and the build process are modeled to determine theresidual stresses and distortion. Mesoscale models providerequired material properties as functions of temperature andthermal gradients throughout the computational domains (Ref19).

1.2 Micromodels

Once powder quality requirements are fulfilled (Ref 20-22),powder coating is the first powder bed process affecting finalproduct quality. Using a discrete element model (DEM) toobtain the powder bed characteristics including the distributionof packing density Mindt et al. showed how the balancebetween powder size distribution and the gap between thecoater arm and the base material can affect the coating quality(Ref 23). The numerical background and details of how thetable displacement relates to fresh and consolidated powderlayer thickness were discussed in Ref 24. The numericallyobtained powder bed geometry is then transferred to the meltpool models incorporating the Navier-Stokes equations. Theheat source particle interaction is resolved using a discreteordinate radiation model. As the base material and the powderparticles are heated, phase changes (melting/solidification andevaporation) are accounted for in the momentum and energyequations. Surface tension by its dependence on the localtemperature gives rise to Marangoni forces in the melt pool.Capillary forces control the motion of the liquid front and

surface. The models were verified for single materials with andwithout powder (Ref 25, 26).

1.3 Macromodels

The large amount of heat supplied to the material at theupper build layers is transferred to the rest of the workpiece byconduction resulting in a global thermal expansion of theproduct. During both stages of cooling and solidification, theelastic and plastic strains caused by the shrinkage and by theconstraints of clamping devices will lead to residual stresses.After clamp release and separation from the base plate theworkpiece reaches its final shape. Macromodels are dedicatedto modeling the evolution of residual stresses during and afterthe build process. Stresses and strains are mainly induced bythermal loads applied during the build process. The effects ofphase changes on thermo-mechanical properties can beneglected as a first approach.

Whereas the physics governing micromodels depend sig-nificantly on the process details, macromodels are mainlydriven by thermal loads (or thermal cycles). This enables asimplification of the physics models allowing a coarserdiscretization and finally facilitating the computation of com-plete industrial workpieces (Ref 19, 25, 27-29).

The energy absorbed by the material and the dimensions ofthe melt pool are transferred from the micro- to the macroscalemodels. The macromodel is used to assess as-built residualstresses and final workpiece distortion.

1.4 Material Properties

Material properties are required by models across multiplethermodynamic states: solid, liquid and vapor. The solid andmost of the liquid properties are obtained from the supplier datasheet (Ref 30). The material properties are digitized andprovided by the mesoscale models as needed by the micro- andmacromodels. The temperature range covered by the data sheetis extended via thermodynamic calculations coupled withdiffusion models (Ref 31, 32). Optical properties includingabsorption and emissivity are assumed constant and correspondto those of polished alloy base bulk material (Ref 33). Theeffective absorption and emissivity is calculated by theradiation model taking the powder bed geometry into account.

2. Experiments

Foster et al. (Ref 18) studied several manufacturing defectsof powder bed fusion processes. The defect reported by Forsteret al. has been studied numerically in the sections below. Forother defects, the authors ran dedicated experiments using anEOS M280 machine and ATI IN-718Plus� powder.

2.1 Experiment 1

To study the influence of powder size distribution andprocess parameters on material quality seven cylindricalspecimens, each 10 mm in diameter and height, are built foreach parameter combination. Different powder size distribu-tions were investigated to capture the influence of powderparticle size distribution on final build quality. Process param-eters including laser power, laser scan speed, hatch spacing and

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table displacement were varied systematically to capture theinfluence of process parameters on defect generation.

No stress relieving was performed on the specimens. Thecylinders were detached from the base plate and cut along avertical plane for microstructural evaluation. Micrographs wereused to determine the as-built porosity. The experimentalresults are averaged and compared with numerical predictions.

2.2 Experiment 2

Process parameters leading to 99.9% material density areused to build an engine mount. It was observed that theseconditions lead to the coater arm hitting the build upper surfaceendangering the manufacturability of the component.

This component has also been studied by Foster et al. (Ref18) using different process parameters leading to differentissues during the build process. Numerical studies reportedbelow will address both build scenarios and correspondingdifficulties.

3. Results and Discussion

The results are arranged in the following sections by processinput and/or parameters that lead to certain defects. Thediscussion relies on experimental data to characterize thedefect. Numerical predictions are compared with experimentaldata and are analyzed to explain the origin of the defect studies.

3.1 Powder Size Distribution

Powder quality is generally accepted as first-order inputparameter influencing the final product quality. Multiple qualitymetrics have been defined including powder size distribution,powder particle shape and morphology as well as powderflowability (Ref 20, 21). The influence of the powder sizedistribution on the final material density is studied bycomparing two size distributions of IN-718Plus�, as shownin Fig. 1. The finer powder (designated DMLS—short fordirect metal laser sintering) corresponds to recommendationsfor the processing of this material using an EOS M280. Thecoarser powder is more suited for electron beam melting (EBM)

Fig. 1 IN718Plus powder sizes compared numerically. The finerpowder (DMLS) has a mean diameter of 22 lm, while the coarsepowder (EBM) has a mean diameter of 90 lm

Table 1 Composition of both DMLS and EBM powder

Element

Wt.%

Powder size distribution

DMLS2140 +170 Mesh EBM2325 Mesh

Al 1.58 1.58C 0.019 0.02Co 9.14 9.19Cr 17.81 17.7Fe 9.64 9.59O 0.006 0.013Mo 2.6 2.51N 0.006 0.006Nb 5.63 5.62Ni Bal. Bal.S 0.001 0.001Ti 0.77 0.78W 1.02 1P 0.02 0.017

Fig. 2 Micrographs of DMLS (left) and EBM (right) powder

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powder bed processes. The cost of the coarse powder is lower,hence the interest to analyze whether it can be used to obtainhigh-quality material. Both powders (supplied by ATI) werefound to be very similar in compositions apart from a slightlyhigher uptake of oxygen in the finer powder (Table 1).Micrographic analysis of both powders shows spherical powderparticles with small amounts of defects and satellites (Fig. 2).During preliminary tests, it was found that fine particles of theDMLS powder reduced powder flowability significantly lead-

ing to difficulties in spreading the powder. Particles below10 lm were therefore filtered out when running experiment 1.

Coating models corresponding to the procedure describedby Mindt et al. (Ref 23) were performed for both powders usinga table displacement of 30 lm. Figure 3 shows top views of thenumerically coated bed for both the DMLS and EBM powders.The coater arm moves from left to right. It can be seen that thefiner DMLS powder has a higher coverage of the base material.The center line of each of the numerical powder beds is used to

Fig. 3 Numerically coated processing table using DMLS and EBM powders

Fig. 4 Numerically predicted porosity for DMLS and EBM powders using identical processing parameters

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investigate the laser powder interaction and the materialconsolidation. Single tracks were studied for a laser power of370 W and a scan speed of 3780 mm/s. Figure 4 shows howthe porosity of the consolidated material evolves as the tracklength increases; the time is measured from start of laser trackin microseconds. It can be readily seen that the coarser EBMpowder leads to significantly higher porosity. 3D images of thenumerically predicted tracks are also shown for both powders.The melt pool track is clipped to visualize the shape of theprocessed material. In spite of the high power applied, the highscan speed does not allow for sufficient time for the large EBMparticles to melt. Under the molten surface large bubblesremain between particles that do not fully melt or consolidateand join the base material. In contrast the DMLS powder ismuch more uniform with much smaller bubbles captured in theconsolidated material. Example experimental micrographs forDMLS and EBM powders are also included in Fig. 4 showingthat EBM powder consolidation leads to higher materialporosity than the finer DMLS powder. The numericallypredicted porosity levels are lower than those measured. Thisis attributed to the size of the numerical specimen, where a

single hatch was considered neglecting reheating and remelting.The good trend correlation between levels of porositiespredicted and measured confirms that numerical models cancapture the influence of powder size distribution and can beused to decide whether a powder is suitable for a targetapplication. Future effort will focus on achieving a quantitativecomparison of porosity levels.

In general, it can be summarized that finer powder studied ispreferable since the coating process leads to a more uniformpowder layer. Further, when using the same process parametersbetter consolidation and lower porosity values are achievedwith the finer powder. The coarse powder is not considered forany further analysis efforts.

3.2 Powder Coating Defects

Foster et al. (Ref 18) reported powder bed defects due todragging of fused clumps of powder or spatter from the meltpool. Mindt et al. (Ref 23) showed a similar effect if the powdersize distribution includes powder particles that are larger thanthe gap between the coater arm and the material being coated.Within the scope of this study the powder was foreseen to havea few particles representative of fused clumps or spatter

Fig. 5 Dragging of large particles being pushed ahead of the coater arm/comparison between experimental (Ref 18) and numerical observations

Fig. 6 Comparison of powder bed porosity as a function of dosage

Fig. 7 Build porosity for different scan speeds at fixed power of370 W and table displacement of 40 lm

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resulting from the melting process. Figure 5 shows how a largeparticle pushed ahead of a rigid coater arm creates a linethrough the powder bed where no particles will be deposited.The same can also be observed if the powder size distributionand the table displacements are not optimized with respect toone another. It can be deduced that powder with large particlesshould not be used with excessively small table displacements;otherwise, similar defects as shown in Fig. 5 occur.

3.3 Powder Dosage

A further problem of the coating process is related to thepowder dosage. If the powder dosage is not sufficient, powderwill not be available to coat the complete processing table (Ref18). Numerical parameter studies show that the lower thedosage, the higher the coated powder bed porosity (Fig. 6).Four doses are modeled: The standard amount of powder(Dose = 1.0) is compared to one half, twice and four times thestandard amount. The average powder bed porosity is com-pared for each dosage showing that a higher dosage leads to adesirable higher packing density of the powder bed. Reducingthe dosage below 0.5 of the standard dosage will lead topowder starvation.

Whereas this ‘‘finding’’ is probably intuitive and all powderbed operators already take this into account it is important tonote that capturing the dosage when documenting processparameters is essential. Changes in dosage values (e.g., toreduce powder consumption) may lead to non-uniform powderbeds and changes in product quality.

3.4 Deposition Strategy and Porosity

The energy density is defined as the ratio of the power to theproduct of hatch spacing and scan speed (E ¼ P

Hv). It is oftenused to characterized process parameters with a single value.Multiple 19191 cm3 specimens were built using a fixedpower of 370 W, different scan speeds and different hatchspacings. The porosities obtained are shown in Fig. 7. For allhatch spacing values investigated, the porosity exhibits a valleyat about 900 mm/s, where maximum material density can beachieved.

Figure 8 shows images of irregular shaped voids typicallyobserved at high scan speeds similar to those used to comparethe different powder size distributions. The high scan speedcorresponds to low energy densities. The irregular porosity ischaracteristic for lack of fusion where powder particles are only

Fig. 8 Magnified porosity images at low global energy density values (GED< 1.0)

Fig. 9 Magnified porosity images at high global energy density (6<GED< 8)

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partially molten. It would seem that the larger the particlediameters the more often partial melting is observed. Figure 9shows the corresponding images for low scan speeds (corre-sponding to high energy density) where spherical voids areobserved indicating gas porosity and probably key holing.

Multiple specimens were manufactured using an EOS M280to study the influence of process parameters on as-built materialquality (experiment 1). The results are arranged in the matrixshown in Fig. 10. The rows show micrographs for threetable displacements, 20, 30 and 40 lm. The columns are fordifferent global energy densities (GED) achieved by varyingthe hatch spacing and the laser scan speed. Micromodeling ofthe marked process parameters was performed; the results areshown in Fig. 11. The powder bed geometries were obtainedfrom numerical coating models. The worse conditions studiednumerically (table displacement: 40 lm, scan speed: 3780 mm/s and hatch spacing: 100 lm) show significant lack of fusion.The numerically predicted porosity is 32%, very close to theexperimental value of 30% in spite of the smaller specimen sizecompared to the experiment. The good quantitative comparisonis attributed to the high porosity observed, which will not bereduced by subsequent laser scans. For lower porosity valuesthe size of the numerical specimen limits the quantitativeaccuracy. The lowest porosity was obtained using a table dis-placement of 20 lm at GED of 3. The corresponding numericalresults show a melt pool reaching to the base metal with verysmall gas enclosures. The largest bubble is about 4 lm indiameter. The intermediate case studied with GED 1.2 showed

much more and larger bubbles in the melt pool. As solidifi-cation begins the bubbles are enclosed in the metal contributingto the porosities measured. For cases with low porosity thenumerical procedure captures the qualitative trend reliably;future work will focus on a methodology to achieve quantita-tive comparisons.

3.5 Balling

Denudation of the powder bed and balling of the moltenmaterial was reported in several studies (Ref 34, 35). Duringthe experimental series number 1 and 2 described above, highGED values led to significant interaction between the coaterarm and the build surface leading to vibration and eventuallythe build stopped. This was not attributed to distortions assuggested in (Ref 18) but rather to the interaction of largeamounts of the molten material with the coater arm. The moltenmaterial gathered due to surface tension forces into a heap thatis larger than the space between the build surface and the coaterarm. Figure 12 shows numerical results for power of 370 W,table displacement of 30 lm, hatch spacing of 90 lm and ascan speed of 2250 mm/s, where the powder particles aroundthe laser track were pulled into the melt by surface tensionforces. Due to the high power input, the temperatures and thesurface tension are high leading to increased gathering of theliquid into a ball with an approximate height of 90 lm. Usingthe simplified assessment of (Ref 23) the gap size between thecoater arm and the build is around 60 lm.

Fig. 10 Experimental DOE using EOS 280: micrographs and corresponding porosity for each of the studied process parameters (hatch spacingand table displacement are in microns; scan speed is in mm/s)

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Fig. 11 Experimental DOE using ESO M280 and corresponding numerical porosity predictions

Fig. 12 Balling effects at higher energy densities: power 370 W, table displacement 30 lm, hatch 90 lm and scan speed 2250 mm/s

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During experimental series 1 the interaction between coaterarm and build surface was observed to increase when the hatchdirection is normal to the coater arm travel direction anddecreases if both directions were parallel. This behavior isattributed to the resistance acting against powder spreading. Inthe case of parallel coating and hatch directions the powderparticles fill and move easily in the trenches. In the case ofnormal directions, the powder accumulates before obstacles anddoes not readily fill trenches.

Numerical results with power reduced to 300 W show areducing of ball height to 60 lm—just enough to enablesuccessful build of the specimen with minimal interactionbetween the material and the coater blade.

3.6 Distortion During Build Process

Forster et al. (Ref 18) reported a similar problem pertainingto the interaction of the coater arm with the distorted surface ofthe build. During the build process the upper layers mightdeform or delaminate causing the build process to terminate.An engine mount is used to study the ability of macromodels topredict the distortions during the build process (Fig. 13). Theprocess parameters used were chosen based on couponexperiment series 1 and simulation results showing highdensity and no balling.

Figure 14 shows the state of the build after the build processwas interrupted. The build height achieved is approximately6 mm. The actual region where the coater arm collided with thebuild could not be identified exactly—the support structure isthought to be the region with most interaction. Residual stressmodeling was pursued (Ref 36). Taking the process parametersand the deposition strategy into account the macromodel resultsindicate a distortion of 15 lm after 2 mm and 102 lm after4.8 mm. As the build continues a maximum distortion of400 lm is reached (Fig. 15), which is much larger than the gapbetween the coater arm and the build upper surface.

The termination of experiment 2 is therefore attributed todistortion of deposited layers. Macromodels predicted thetermination at a build height of 4.8 mm as compared to 6 mmin the experiment.

Fig. 13 CAD model of the engine mount

Fig. 14 Status of engine mount build at the time distortions led tomachine stop

Fig. 15 Vertical distortion of the engine mount at the stage the experimental process stopped (�0:145 mm � UZ � 0:064 m)

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3.7 Final Workpiece Distortion

In search for a build strategy for the engine mount, the buildorientation was varied numerically (experiment 2). The enginemount built using on a horizontal orientation failed to build duea maximum distortion of �1.1 to 0.4 mm as shown in Fig. 16.Foster et al. (Ref 18) added supports to enable the horizontalbuild, but the sides broke off due to the excessive residualstresses. The numerical vertical distortions of the sides correlatevery well with the breaking reported in Ref 18. Numericalparameter studies indicated that rotating the build directionreduces distortion. Figure 17 shows that when rotated 45

degrees the maximal final vertical distortions (along Z+) arereduced to 0.4 mm being almost negligible (only the size of alayer thickness). The numerically optimized build direction wasconfirmed experimentally as shown in Fig. 18.

The maximal vertical distortion after each layer wascompared for both build orientations geometry in Fig. 19. Atthe beginning of the process the vertical distortion increasespositively for the 0� orientation while decreasing negatively forthe 45� orientation. Apart from some high positive valuesaround 15 mm of height (induces by a part deposited while notbeing linked to the main body), the 45� geometry distortion

Fig. 16 Predicted vertical distortion for horizontal build (�1:1 mm � UZ � 0:4 mm)

Fig. 17 Predicted vertical distortion for 45� inclined build (�0:95 mm � UZ � 0:4 mm)

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remains always negative and smaller than the gap between adeposited layer and the coater arm. As previously described thisis not the case for the 0� orientation for which negativedistortions appear after 25 mm of height.

4. Conclusions and Summary

Numerical simulations covering the multiscale and multi-physics scope of powder bed additive manufacturing wereshown to capture defects reliably. Defects modeled includethose related to the coating process, material consolidation andresidual stresses. Numerical results were compared withexperimental measurements and observations showing verygood correlations. Powder bed defects were compared quali-

tatively. High porosity predictions could be validated quanti-tatively. Trends of low porosity values are predicted reliably.The size of the numerical specimens limits the ability toquantify the final porosity. Numerical methods are none the lessable to optimize the process window for minimum porosity.Future work with the coating models should focus on how thepowder size distribution and the gap size are to be optimized tomaximize the powder layer thickness and layer uniformity.Melt pool models have the potential of optimizing the processwindow identifying the most suitable parameters to reduceporosity and surface roughness while maximizing build rate.Macromodels were shown to accurately predict distortionsduring the build process. The models were used to minimizedistortion by varying the build orientation. Future work is toinclude optimization (minimization) of support structures.

Fig. 18 Reduced distortions for 45o rotated build and the images of the successful build

Fig. 19 Maximal vertical displacement of the last deposited layer according to the total height achieved for both configurations: rotation of 0�(1st case) and rotation of 45� (2nd case)

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Acknowledgment

This work was performed under the DARPA Open Manufac-turing Program ‘‘Rapid Low Cost Additive Manufacturing’’contract number HR001-12-C-0037 to Honeywell InternationalInc. The authors acknowledge the financial support and theguidance of the managing panel. The views, opinions and/orfindings expressed are those of the author(s) and should not beinterpreted as representing the official views or policies of theDepartment of Defense or the US Government.

References

1. R.F. Housholder, Molding Process. US Patent 4,247,508 (1981)2. I. Yadroitsev, Selective Laser Melting: Direct Manufacturing of 3D-

Objects by Selective Laser Melting of Metal Powders, LAP LambertAcademic Publishing, Saarbrucken, 2009

3. FAA. (online). [cited 2015 August 8. Available fromhttps://www.faa.gov/aircraft/air_cert/design_approvals/media/CPI_guide_II.pdf

4. A.D. Peralta, M. Enright, M. Megahed, J. Gong, M. Roybal, and J.Craig, Towards Rapid Qualification of Powder-Bed Laser AdditivelyManufactured Parts, Integrating Materials and Manufacturing Inno-vation, 2016, doi:10.1186/s40192-016-0052-5

5. M. Seifi, A. Salem, J. Beuth, O. Harrysson, and J.J. Lewandowski,Overview of Materials Qualification Needs for Metal AdditiveManufacturing, J. Miner. Met. Mater. Soc. (JOM), 2016, doi:10.1007/s11837-015-1810-0

6. P. Mercelis and J.P. Kruth, Residual Stresses in Selective LaserSintering and Selective Laser Melting, Rapid Prototyp. J., 2006, 12(5),p 254–265. doi:10.1108/13552540610707013

7. P. Papadakis, G. Branner, A. Schober, K.H. Richter, T. Uihlein,Numerical Modeling of Heat Effects During Thermal Manufacturing ofAero Engine Components. Proceedings of the World Congress onEngineering, London, UK (2012)

8. H. Meier and C. Haberland, Experimental Studies on Selective LaserMelting of Metallic Parts, Mat.-wiss. u. Werkstofftech, 2008, doi:10.1002/mawe.200800327

9. C. Kamath, B. El-dasher, G.F. Gallegos, W.E. King, A. Sisto, Densityof Additively-Manufactured, 316L SS Parts Using Laser Powder-BedFusion at Powers Up to 400 W. LLNL-TR-648000 Lawrence Liver-more National Laboratory (2013)

10. C. Qiu, C. Panwisawas, M. Ward, H.C. Basoalto, J.W. Brooks, andM.M. Attallah, On the Role of Melt Flow into the SurfaceStructure and Porosity Development During Selective LaserMelting, Acta Mater., 2015, 96, p 72–79. doi:10.1016/j.actamat.2015.06.004

11. L. Rickenbacker, T. Etter, and S. Hobel, High Temperature MaterialProperties of IN738LV Processed by Selective Laser Melting (SLM)Technology, Rapid Prototyp. J., 2013, 19(4), p 282–290

12. A. Aversa, M. Lorusso, G. Cattano, D. Manfredi, F. Calignano, E.P.Ambrosio, et al., A Study of the Microstructure and the MechanicalProperties of an Al-Si-Ni Alloy Produced Via Selective Laser Melting,J. Alloys Compd., 2017, 695, p 1470–1478

13. M.A. Lodes, R. Guschbauer, and C. Korner, Process Development forthe Manufacturing of 99.94% Pure Copper via Selective Electron BeamMelting, Mater. Lett., 2015, 143, p 298–301. doi:10.1016/j.matlet.2014.12.105

14. S.J. Raab, R. Guschlbauer, M.A. Lodes, and C. Korner, Thermal andElectrical Conductivity of 99.9% Pure Copper Processed via SelectiveElectron Beam Melting, Adv. Eng., 2016, doi:10.1002/adem.201600078

15. M. Khan and P. Dickens, Selective Laser Melting (SLM) of Pure Gold,Gold Bull., 2010, 43(2), p 114–121

16. J.A. Choren, S.M. Heinrich, and M.B. Silver-Thorn, Young�s Modulusand Volume Porosity Relationships for Additive ManufacturingApplications, J. Mater. Sci., 2013, 48, p 5103–5112. doi:10.1007/s10853-013-7237-5

17. E. Jelis, M. Clemente, S. Kerwien, N.M. Ravindra, and M.R. Hespos,Metallurgical and Mechanical Evaluation of 4340 Steel Produced byDirect Metal Laser Sintering, JOM, 2015, doi:10.1007/s11837-014-1273-8

18. B.K. Foster, E.W. Reutzel, A.R. Nassar, B.T. Hall, S.W. Brown, C.J.Dickman, Optical, Layerwise Monitoring of Powder Bed Fusion. 2015Annual Solid Freeform Fabrication, Austin. In: University of Texas atAustin, pp. 295–307 (2015)

19. M. Megahed, H.W. Mindt, N. N�Dri, H. Duan, and O. Desmaison,Metal Additive Manufacturing Process and Residual Stress Modelling,Integr. Mater. Manuf. Innov., 2016, doi:10.1186/s40192-016-0047-2

20. J.A. Slotwinski, E.J. Garboczi, P.E. Stutzman, C.F. Ferraris, S.S.Watson, and M.A. Peltz, Characterization of Metal Powders Used forAdditive Manufacturing, J. Res. Natl. Inst. Stand. Technol., 2014, 119,p 460–493. doi:10.6028/jres.119.018

21. J.A. Slotwinski and E.J. Garboczi, Metrology Needs for Metal AdditiveManufacturing Powders, JOM, 2015, 67(3), p 538–543. doi:10.1007/s11837-014-1290-7

22. J. Dawes, R. Bowerman, and R. Trepleton, Introduction to the AdditiveManufacturing Powder Metallurgy Supply Chain, Johnson MattheyTechnol. Rev., 2015, 59(3), p 243–256

23. H.W. Mindt, M. Megahed, N.P. Lavery, M.A. Homes, S.G. Brown,Powder Bed Layer Characteristics—The Overseen First Order ProcessInput. 145th TMS Annual Meeting and Exhibition, Nashville, USA(2016)

24. H.W. Mindt, M. Megahed, N.P. Lavery, M.A. Holmes, and S.G.R.Brown, Powder Bed Layer Characteristics: The Overseen First-OrderProcess Input, Metall. Mater. Trans. A, 2016, doi:10.1007/s11661-016-3470-2

25. N. N�Dri, H.W. Mindt, B. Shula, M. Megahed, A.D. Peralta, P.Kantzos, et al., DMLS Process Modelling and Validation. Orlando. In:Wiley, TMS 2015 144th Annual Meeting & Exhibition (2015)

26. H.W. Mindt, M. Megahed, B. Shula, A.D. Peralta, J. Neumann, PowderBed Models—Numerical Assessment of As-Built Quality. AIAA,editor. (ed) SciTech, 4–8 January, San Diego. pp. 2016–1657 (2016)

27. M. Vogel, M. Khan, J. Ibarra-Medina, A. Pinkerton, N. N�Dri, M.Megahed, A Coupled Approach to Weld Pool, Phase and ResidualStress Modelling of Laser Direct Metal Deposition (LDMD) Processes.2nd World Congress on Integrated Computational Materials Engineer-ing, Salt Lake City, pp. 231–236 (2013)

28. Keller N, Ploshikhin V (2014) New method for Fast Predictions ofResidual Stress and Distortion of AM parts. Solid Freeform FabricationSymposium, Austin, Texas

29. C. Li, C.H. Fu, Y.B. Guo, and F.Z. Fang, Fast Prediction and Validationof Part Distortion in Selective Laser Melting, Proc. Manuf., 2015, 1, p355–365

30. (2011) ATI 718 Plus Alloy Data Sourcebook. : Revision 1.2, ATIAllvac

31. J. Guo and M. Samonds, Alloy Thermal Physical Property PredictionCoupled Computational Thermodynamics with Back Diffusion Con-sideration, J. Phase Equilib. Diffus. Basic Appl. Res. Sect., 2006, 1, p1863–7345. doi:10.1007/s11669-006-9005-6

32. J. Guo, W. Cao, and M. Samonds, The Application of IntegratedComputational Material Engineering (ICME) in Metal CastingsSimulations, IOP Conf. Ser. Mater. Sci. Eng., 2012, doi:10.1088/1757-899X/33/1/012003

33. Y.S. Touloukian, D.P. DeWitt, Thermophysical Properties of Mat-ter—Thermal Radiative Properties—Metallic Elements and Alloys.Purdue University (1970)

34. I. Kovaleva, O. Kovalev, and I. Smurov, Model of Heat and MassTransfer in Random Packing Layer of Powder Particles in SelectiveLaser Melting, Phys. Proc., 2014, 56, p 400–410. doi:10.1016/j.phpro.2014.08.143

35. N.P. Lavery, S.G.R. Brown, J. Sienz, F. Belblidia, A Review ofComputational Modelling of Additive Layer Manufacturing—Multi-Scale and Multi-Physics. International Conference on SustainableDesign and Manufacturing, Cardiff, UK (2014)

36. A. Mendizabal, J.B. Gonzalez-Diaz, M. San Sebastian, and A.Echeverria, Improved Accuracy of the Inherent Shrinkage Methodfor Fast and more Reliable Welding Distortion Calculations, J. Mater.Eng. Perform., 2016, doi:10.1007/s11665-016-2116-2

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