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Special Issue paper

Designing for Additive Manufacturing: Lightweighting Through Topology Optimization Enables Lunar SpacecraftOPEN ACCESS

[+] Author and Article Information
Melissa E. Orme

Morf3D, Inc.,
821 N Nash Street,
El Segundo, CA 90245
e-mail: Melissa@morf3d.com

Michael Gschweitl

Ruag Space,
Zürich 8052, Switzerland
e-mail: michael.gschweitl@ruag.com

Michael Ferrari

Ruag Space,
Zürich 8052, Switzerland
e-mail: michael.ferrari@ruag.com

Morf3D, Inc.,
821 N Nash Street,
El Segundo, CA 90245
e-mail: ivan@morf3d.com

Franck Mouriaux

Ruag Space,
Zürich 8052, Switzerland
e-mail: franck.mouriaux@ruag.com

1Corresponding author.

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 10, 2017; final manuscript received July 10, 2017; published online August 30, 2017. Assoc. Editor: Carolyn Seepersad.

J. Mech. Des 139(10), 100905 (Aug 30, 2017) (6 pages) Paper No: MD-17-1121; doi: 10.1115/1.4037304 History: Received February 10, 2017; Revised July 10, 2017

Abstract

An end-to-end development approach for space flight qualified additive manufacturing (AM) components is presented and demonstrated with a case study consisting of a system of five large, light-weight, topologically optimized components that serve as an engine mount in SpaceIL's GLPX lunar landing craft that will participate in the Google Lunar XPrize challenge. The development approach includes a preliminary design exploration intended to save numerical effort in order to allow efficient adoption of topology optimization and additive manufacturing in industry. The approach also addresses additive manufacturing constraints, which are not included in the topology optimization algorithm, such as build orientation, overhangs, and the minimization of support structures in the design phase. Additive manufacturing is carried out on the topologically optimized designs with powder bed laser technology and rigorous testing, verification, and validation exercises complete the development process.

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Introduction

The process of fabricating structural metallic components layer-by-layer via digital information obtained from computer-aided design engineering blueprints is known as additive manufacturing (AM) and is gaining considerable traction in the aerospace, automotive, medical, and energy industries [1]. The value added with additive manufacturing, or AM, includes the ability to: significantly lightweight structural components through topology optimization, consolidate a system of components into a single part for volumetric efficiency, incorporate added functionality, manufacture highly complex geometries, combat component obsolescence by fabricating on demand, and significantly reduce the lifecycle time between concept, design, manufacture, and validated component delivery [2].

This paper presents as a case study, the AM design considerations, topology optimization, manufacturing, and validation of a system of five components scheduled for lunar travel as part of SpaceIL's entry into the Google Lunar XPrize competition [3]. Part of the competition mission requires the landing of an unmanned spacecraft on the moon within the 2017 calendar year. The utilization of additive manufacturing is well-suited for the stated challenge in that: (1) it permits rapid turn-around between component concept and delivery of qualified part (i.e., required to meet the accelerated launch deadline); and (2) it enables the manufacturing of lightweight topologically optimized structural components, which is highly attractive for air and space applications.

Background

The term AM, generally describes the technologies that strive to build functional components layer-by-layer from the digital information. One of the most successful technologies of AM is that of selective laser melting, which is employed in this work. Details of this process are not in the purview of this paper and can be found elsewhere [47].

Additive manufacturing allows structural components to be fabricated without a mold or machining, and thus enables the benefits of lightweighting through topology optimization [8,9]. Hollow structures, structures with internal cooling channels, organic, bionic-shaped structures, and structures filled with lattice elements [10] can now be made via additive manufacturing.

Holistic Process Flow

In an effort to reliably produce repeatable microstructure and mechanical characteristics in AM parts, a holistic process flow has been developed that governs the design, manufacture, and testing of additively manufactured, topologically optimized components. The holistic process flow was detailed by Orme et al. [11] and is described pictorially in Fig. 1. In brevity, the process flow includes the main steps of: (1) candidate part selection and concept development, (2) topology optimization and design interpretation for AM, (3) finite element method (FEM) design verification, (4) AM manufacturing of component with in-process witness coupons, and (5) material verification and mechanical qualification testing.

As can be seen in the figure, feedback loops exist between steps 2 and 3 and between steps 4 and 5. Within step 2, mechanical, vibrational, and thermal constraints (depending on the application) are considered in addition to design constraints pertaining to design space and nondesign space. In the presented case study, thermal constraints are not considered. For AM applications, manual postprocessing after topology optimization is required in order to apply the design for AM guidelines that are not currently incorporated in the topology optimization algorithm such as overhang minimization, as well as for design interpretation (i.e., detailed attachment points and the spitting of large parts). Hence, the final geometry of the part is not always congruent with the topology optimization results. Typically, a margin is included in the constraints in the topology optimization exercise in order to meet the requirements once the final geometry is accepted. This is verified in step 3 of the process flow: FEM.

With respect to the feedback loop between steps 4 and 5, if the in-process coupons yield inferior material and mechanical qualities, or if the structural testing does not conform to the required allowables, then the manufacturing step 4 is repeated with different process parameters, and the coupons and artifacts are tested again. Process parameters include the laser parameters and build parameters. The laser parameters include laser power, laser scan speed, hatch distance, and beam offset. The laser parameters for the main core of the component (in-skin), the upward facing surfaces (up-skin), downward facing surfaces (down-skin), and the contours may each have different laser parameters. The build parameters include the build plate temperature, build layer thickness, part orientation, and support structure design.

This paper describes the results of application of the holistic process flow to the case study of the AM fabrication of the topologically optimized components for SpaceIL's entry to the Google XPrize competition.

Part Selection.

The components selected for AM design in this study are illustrated in Fig. 2 and consist of four legs connected to a hub that are intended to hold a spacecraft engine. All components are made from AlSi10Mg, a common aluminum alloy used in additive manufacturing. The five-component assembly interfaces with a heat shield (not a part of the AM assembly) and is connected to a spacecraft ring (not shown). The components to be fabricated via AM are required to withstand high loads and high temperature ranges and are desired to be characterized by low mass and high stiffness. This system of parts was considered as a good candidate for topology optimization so that the added value of light-weighting can be achieved.

The advent of additive manufacturing has resulted in significant activity in the discipline of topology optimization, because AM enables the fabrication of complex topologically optimized components that would be difficult to be manufactured with traditional methods [8].

A topologically optimized component often bears no outward resemblance of its unoptimized counterpart and often consists of members with nonconstant cross sections similar to tree branches or bones. Hence, topologically optimized designs are often called “bionic,” or “organic” designs [12,13].

In this work, topology optimization is employed with Altair's hyperworks 14.0 commercially available software, which employs the solid isotropic material with penalization method to determine the optimum placement of material with respect to the specific loading and geometric requirements specified by the customer. The design space is defined by considering the interface locations, stay-in volumes, and nondesign zones. The stay-in volume is the envelope in which the part needs to be in order to avoid interference with neighboring components on the spacecraft or to allow suitable clearance for part integration or tool access. Nondesign zones correspond to fixed regions where material must remain and may correspond to attachment points or other inherent features of the component. The resulting design space is a volume in which the topology optimization algorithm decides where the material is needed to fulfill the structural requirements of the part. The objective set for our optimization routine is to minimize the mass of the component while meeting constraints for the first natural frequency (>60 Hz) as well as the stress limit (<115 MPa). In order to save computing time, a coarser mesh has been chosen for the topology optimization trials than for the subsequent FEM verification.

A first optimization result is illustrated in Fig. 3, in which the design space was explored, and trends were identified. From the figure, it appears that the topologically optimized design concept is discontinuous. It should be noted, however, that the result is continuous but in order to show the volumetric area, which requires material within the design space, the elements of lower density are hidden (i.e., element density below 20% are hidden). Additionally, most of the blue (low density) elements have some higher density elements underneath.

Figure 4 schematically illustrates the design tendencies encountered during the design exploration phase that satisfy the loading requirements put forth by the customer (proprietary). Here, the solid legs of the original design tend to split into three separate branches (top view Fig. 4), each of which attach to the central hub. Additionally, the optimization routine seeks solutions in which the branch type structures attach to the central hub at two elevations (side view image of Fig. 4). Hence, it was decided to explore this avenue of topology optimization and to study whether this solution was mechanically viable. These results enabled a reduction of the design volume for further optimization runs for the sake of computational effort and the assessment of the viability of certain design solutions, as illustrated in Fig. 5 in the gray-shaded region. Figure 5 also shows the optimization results of the structure that fulfill the strength and stiffness requirements.

These initial studies were focused around the topology optimization of one integrated component that includes the connected legs and hub, with the understanding that once the topology optimization design concept was established, it would be determined where and how to split the components in order to fabricate them in the EOS M290 machine. Since the size of the components was a design driving factor, a final topology optimization was performed which incorporated the determined connection points for the legs and the hub and forces the topology optimization to pass through these connections (Fig. 6).

An important aspect of the design was to create self-supporting components, or when not possible, components with the minimal number of support structures. Hence, the design was also driven in part by component orientation with respect to build direction to ensure minimal support structures. We have adapted the general rule of thumb elements that overhang at angles greater than 45 deg from the build plate that can be printed without supports. Figure 7 illustrates a rendering of the final design of five split components that are bolted together by close tolerance shear bolts. The mass of the final system of components is 2.95 kg, reduced significantly from the original baseline design (illustrated in Fig. 2) of 4.0 kg.

Finite Element Modeling.

Topologically optimized designs are then analyzed with a separate finite element modeling routine. This step is necessary since the final design geometry is not exactly the same as the outcome of the topology optimization exercise due to the fact that manual postprocessing after topology optimization is required to incorporate the additional manufacturing criteria such as self-supporting structures, segmentation into multiple parts so that they will fit within the build envelope of existing machines, and details of the attachment points. Additionally, the modal characteristics of the components are verified by modal analysis in this step.

All analysis and optimization in this work was performed with Altair's hyperworks 14.0, which uses Hypermesh as preprocessor, OptiStruct as solver, and Hyperview for postprocessing. We have constrained the mesh size to be relatively small in order to properly assess stress concentrations (approximately 1 × 106 elements and around 300,000 nodes, with a mesh size of approximately 1.2–2.5 mm [47–98 thousandths of an inch]). Linear tetrahedral elements were chosen as a result of a compromise between accuracy and reasonable computing time. Smaller linear elements were used to represent the geometric features, which provide a sufficient representation of the stresses in the part. Due to the conservative value of the safety factor employed, it was determined that the stress level representation was sufficient.

The AM parts are modeled with solid elements (tetra). The expansion cone and heat shield are modeled using shell elements (quad). The engine FE model was created in detail to achieve a modal performance that matches with the modal results provided by the engine supplier. The heatshield is modeled with larger elements, as it is only used as mass representation. The meshed surface of the hub and leg (one of four) are illustrated in Fig. 8.

The engine mounting structure is clamped at all 4 feet (at the inner bore hole), constraining all translational and rotational degrees-of-freedom DOF123456 to reflect the mounting to the spacecraft structure. The results from the dynamic testing campaign will be fed back to this step to adjust the settings if necessary in order to correlate the model. Because this is a customer-driven project involved in a competition, many of the details are held as company proprietary by the customer (e.g., exact loading conditions).

Figure 9 illustrates how connections in between the structural components were realized in the FE model. In brevity, within each bore hole, a RBE2 (rigid body element) spider is created. A zero-length BUSH element is placed on the RBE2's independent node in order to read out the forces and accelerations. From the BUSH, several BAR (one-dimensional model element) elements, representing the titanium bolt, are used to attach to the connecting part. This provides a conservative representation of the stiffness behavior of the joint. Effects of bolt pretension and contact pressure are not considered in the FE model. The strength analysis of bolts and lugs (bearing) is performed separately. The connection from the hub to the engine was realized using the same methodology.

Due to the fact that AM is still a relatively new process and lacks a heritage database from which to draw, highly conservative design allowables were used. In addition to the usual safety factors employed for developing space products (shown in Table 1), we have also included an AM conservatism factor of 1.5, which allows for a “comfort zone” to compensate for its lack of heritage data.

The margin of safety (MoS) of the part is calculated according to the formula below Display Formula

(1)$MoSyield=σallw,AlSi10σvonMises⋅SFyield⋅SFAM−1$

where σallw,AlSi10Mg is the maximum allowable yield stress (design allowable), σvonMises is the Von Mises stress obtained from FEM analysis, SFyield is the yield strength safety factor, and SFAM is the additive manufacturing conservatism safety factor.

The structure is subjected to quasi static as well as sine and random vibration loads. The results of the analysis are presented in Figs. 10 and 11, which illustrate stress plots for static equivalent load cases based on the accelerations from the random analysis (3 × gRMS method) in the xy plane and z planes of the spacecraft, respectively. All stresses are well within the design allowables for the final optimized concept providing positive margins of safety (MoS_xy = 0.04 and MoS_z = 0.21), and hence according to the holistic process flow, this design is analytically verified and ready to be manufactured.

The static equivalent load level for the z-excitation is 154 g out of plane and 25 g in plane and 36 g out of plane and 79 g in plane for the xy-excitation, respectively. Moreover, fatigue and shock analysis were also performed. Fatigue analysis has been done with the ESA software esafatig v4.3.1a; the fatigue calculations are based on the linear damage accumulation (Palmgren–Miner) rule. The shock assessment was based on the ECSS “point source excitation method.”

Figure 12 illustrates a photograph of the three completed build plates that comprise the entire assembly. The leftmost plate contains two legs, the center contains the connecting hub, and the rightmost contains the remaining two legs. In-process coupons (tensile and density cubes) were built with each plate. The vertical coupons built with the hub were incorporated under the eight tabs around the main circumference that would otherwise require support structures; hence, these coupons serve two purposes.

Material Verification and Mechanical Qualification.

Test coupons are illustrated in Fig. 12 which are measured destructively for ultimate strength, yield strength, elongation, and Young's modulus. Density cubes are also fabricated from which cross sections are polished and examined with optical microscopy. Additionally, thin-walled, fully enclosed pyramid structures are fabricated in order to hermetically seal a sample of powder originating from the build process for archival purposes.

The results from the in-process testing campaign are presented in Table 2. All values exceeded the defined design allowables, and so the test campaign proceeded to nondestructively test the printed artifacts.

The printed artifacts were computed tomography (CT) scanned in order to detect internal flaws and voids, and also to check the dimensionality of the AM-produced component compared to the original computer-aided design blueprint. The results of the CT scan revealed no defects larger than 400 μm (the resolution of the CT scan), and the maximum deviations detected for all five AM-printed artifacts compared to nominal was in the range of 500 μm [0.019 in]. These deviations were well within the acceptance limits for the components as they do not impair fit and function in terms of the functional surfaces.

Dynamic structural testing of the components is currently ongoing and includes a low level sine sweep on each space craft axes as well as high level sine, random, and shock testing. If the structural testing results indicate that the manufactured component is lacking in structural fidelity, the AM process step will be re-examined, the process parameters adjusted, and the artifacts and coupons will be manufactured again with the new process settings. The new artifacts and coupons would then be retested as described above. This process continues until it is determined that the parts are of high quality, are structurally sound, and suitable for flight.

Figure 13 illustrates the assembly of AM-manufactured components along with a mock engine.

Summary

A holistic process flow guiding the design and fabrication of AM components from concept to part qualification has been applied to the case study of a system of five components scheduled for flight to the moon in the calendar year of 2017.

Specific attention has been paid on designing for AM including topology optimization and design interpretation for AM. Optimization goals included the minimization of component mass while meeting constraints for the first natural frequency as well as the maximum stress limit. Coupled design interpretation considerations addressed the segmentation of the large part into a system of components that fit within the build chamber and the minimization of support structures.

The resulting system of components includes four bionic inspired legs and a central hub that are significantly lighter in weight than the original baseline design (i.e., 2.95 kg compared to 4.0 kg). In the process, test coupons were fabricated with each build and their tested values were well within the design acceptance limits set by the customer. Computed tomography testing on the component level revealed that the fabricated components were within the geometric design limits, and there was a negligible porosity (density at 99.7%). Structural testing is ongoing.

The results presented validate that additive manufacturing is an enabling technology for rapid design and production of components destined for space flight because: (1) it permits rapid turn-around between component design and delivery of qualified part; and (2) it enables the manufacture of significantly lightweight components through topology optimization. Use of the holistic process flow guides the design and manufacture of AM parts to ensure structurally sound components suitable for spaceflight.

Acknowledgements

The authors wish to express their gratitude to EOS GmbH for their continued support of this and other endeavors at Morf3D.

References

Wohlers, T. , 2016, “ Additive Manufacturing and 3D Printing State of the Industry: Wohlers Report,” Wohlers Associates, Fort Collins, CO.
Zhai, Y. , Lados, D. A. , and Lagoy, J. L. , 2014, “ Additive Manufacturing: Making Imagination the Major Limitation,” JOM, 66(5), pp. 808–816.
XPrize, 2017, “ Google Lunar XPrize,” XPRIZE Foundation, Culver City, CA, accessed July 25, 2017,
Brandl, E. , Heckenberger, U. , Holzinger, V. , and Buchbinder, D. , 2012, “ Additive Manufactured AlSi10Mg Samples Using Selective Laser Melting (SLM): Microstructure, High Cycle Fatigue, and Fracture Behaviour,” Mater. Des., 34, pp. 159–169.
Buchbinder, D. , Schleifenbaum, H. , Heidrich, S. , Meiners, W. , and Bültmann, J. , 2011, “ High Power Selective Laser Melting (HP SLM) of Aluminum Parts,” Phys. Proc., 12, pp. 271–278.
Campbell, I. , Bourell, D. , and Gibson, I. , 2012, “ Additive Manufacturing: Rapid Prototyping Comes of Age,” Rapid Prototyping J., 18(4), pp. 255–258.
Frazier, W. E. , 2014, “ Metal Additive Manufacturing: A Review,” J. Mater. Eng. Perform., 23(6), pp. 1917–1928.
Bracket, D. , Ashcroft, I. , and Hague, R. , 2011, “ Topology Optimization for Additive Manufacturing,” Solid Freeform Fabrication Symposium (SFF), Austin, TX, Aug. 8–10, pp. 348–362.
Sigmund, O. , 2011, “ On the Usefulness of Non-Gradient Approaches in Topology Optimization,” Struct. Multidiscip. Optim., 43(5), pp. 589–596.
Yan, C. , Hao, L. , Hussein, A. , Bubb, S. , Young, P. , and Raymont, D. , 2014, “ Evaluation of Light-Weight AlSi10Mg Periodic Cellular Lattice Structures Fabricated Via Direct Metal Laser Sintering,” J. Mater. Process. Technol, 214(4), pp. 856–864.
Orme, M. E. , Gschweitl, M. , Vernon, R. , Ferrari, M. , Madera, I. J. , Yancey, R. , and Mouriaux, F. , 2016, “ A Demonstration of Additive Manufacturing as an Enabling Technology for Rapid Satellite Design and Fabrication,” International SAMPE Technical Conference, Long Beach, CA, May 23–26, pp. 1–17.
Reiher, T. , and Koch, R. , 2015, “ FE-Optimization and Data Handling for Additive Manufacturing of Structural Parts,” Solid Freeform Fabrication Symposium (SFF), Austin, TX, Aug. 8–10, pp. 1092–1103.
Emmelmann, C. , Sander, P. K. , and Wycisk, E. , 2011–2012, “ Laser Additive Manufacturing and Bionics: Redefining Lightweight Design,” Phys. Procedia, 12, pp. 364–368.
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References

Wohlers, T. , 2016, “ Additive Manufacturing and 3D Printing State of the Industry: Wohlers Report,” Wohlers Associates, Fort Collins, CO.
Zhai, Y. , Lados, D. A. , and Lagoy, J. L. , 2014, “ Additive Manufacturing: Making Imagination the Major Limitation,” JOM, 66(5), pp. 808–816.
XPrize, 2017, “ Google Lunar XPrize,” XPRIZE Foundation, Culver City, CA, accessed July 25, 2017,
Brandl, E. , Heckenberger, U. , Holzinger, V. , and Buchbinder, D. , 2012, “ Additive Manufactured AlSi10Mg Samples Using Selective Laser Melting (SLM): Microstructure, High Cycle Fatigue, and Fracture Behaviour,” Mater. Des., 34, pp. 159–169.
Buchbinder, D. , Schleifenbaum, H. , Heidrich, S. , Meiners, W. , and Bültmann, J. , 2011, “ High Power Selective Laser Melting (HP SLM) of Aluminum Parts,” Phys. Proc., 12, pp. 271–278.
Campbell, I. , Bourell, D. , and Gibson, I. , 2012, “ Additive Manufacturing: Rapid Prototyping Comes of Age,” Rapid Prototyping J., 18(4), pp. 255–258.
Frazier, W. E. , 2014, “ Metal Additive Manufacturing: A Review,” J. Mater. Eng. Perform., 23(6), pp. 1917–1928.
Bracket, D. , Ashcroft, I. , and Hague, R. , 2011, “ Topology Optimization for Additive Manufacturing,” Solid Freeform Fabrication Symposium (SFF), Austin, TX, Aug. 8–10, pp. 348–362.
Sigmund, O. , 2011, “ On the Usefulness of Non-Gradient Approaches in Topology Optimization,” Struct. Multidiscip. Optim., 43(5), pp. 589–596.
Yan, C. , Hao, L. , Hussein, A. , Bubb, S. , Young, P. , and Raymont, D. , 2014, “ Evaluation of Light-Weight AlSi10Mg Periodic Cellular Lattice Structures Fabricated Via Direct Metal Laser Sintering,” J. Mater. Process. Technol, 214(4), pp. 856–864.
Orme, M. E. , Gschweitl, M. , Vernon, R. , Ferrari, M. , Madera, I. J. , Yancey, R. , and Mouriaux, F. , 2016, “ A Demonstration of Additive Manufacturing as an Enabling Technology for Rapid Satellite Design and Fabrication,” International SAMPE Technical Conference, Long Beach, CA, May 23–26, pp. 1–17.
Reiher, T. , and Koch, R. , 2015, “ FE-Optimization and Data Handling for Additive Manufacturing of Structural Parts,” Solid Freeform Fabrication Symposium (SFF), Austin, TX, Aug. 8–10, pp. 1092–1103.
Emmelmann, C. , Sander, P. K. , and Wycisk, E. , 2011–2012, “ Laser Additive Manufacturing and Bionics: Redefining Lightweight Design,” Phys. Procedia, 12, pp. 364–368.

Figures

Fig. 1

Holistic process flow for additive manufacturing of high-quality, reliable metallic components

Fig. 2

LEROS engine support structure design baseline

Fig. 3

Preliminary exploration of design solutions within the design space (top image): ISO view, (bottom image): side view

Fig. 4

Identification of design tendencies encountered during exploratory topology optimization analysis; top image, top view of assembly; bottom image, side view of assembly

Fig. 5

Top image, top view of a topology optimized concept within the adapted design space (gray shaded region); bottom image, ISO view of topology results within the adapted design space (gray region)

Fig. 6

Final design space based on the evaluated concept to split the structure in order to allow printing in EOS M290. Individual design volumes of legs and hub connected at determined locations of the split.

Fig. 7

Rendering of the complete assembly of four identical legs and one hub that are joined by close tolerance shear bolts

Fig. 8

Separate components: top, hub; bottom, one of four identical legs

Fig. 9

Realization of connections: RBE–BUSH–BAR (6×)

Fig. 10

Results of the FEM analysis: stress plot of the complete engine mount structure subjected to (x/y) excitation

Fig. 11

Results of the FEM analysis: stress plot of the complete engine mount structure subjected to Z excitation

Fig. 12

Photograph depicting three build plates with the entire LEROS engine mount assembly components and their respective in-process coupons

Fig. 13

Photograph illustrating assembled components with a mock-up of the LEROS Apogee engine

Tables

Table 1 Safety factors and margin of safety used in the analyses
Table 2 Measured values of in-process coupons

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