WO2022108517A1 - Optimisation de structures de support pour la fabrication additive - Google Patents

Optimisation de structures de support pour la fabrication additive Download PDF

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Publication number
WO2022108517A1
WO2022108517A1 PCT/SG2020/050682 SG2020050682W WO2022108517A1 WO 2022108517 A1 WO2022108517 A1 WO 2022108517A1 SG 2020050682 W SG2020050682 W SG 2020050682W WO 2022108517 A1 WO2022108517 A1 WO 2022108517A1
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WO
WIPO (PCT)
Prior art keywords
support structure
simulation
build
failure
computer
Prior art date
Application number
PCT/SG2020/050682
Other languages
English (en)
Inventor
Keng Hui Lim
Jun Liu
Soon Mei CHAN
Kai Lee TAN
Zhonghong Alexander LIU
Shiyun ZHANG
Khim Yong LEE
Original Assignee
Agency For Science, Technology And Research
Singapore University Of Technology And Design
Temasek Polytechnic
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Agency For Science, Technology And Research, Singapore University Of Technology And Design, Temasek Polytechnic filed Critical Agency For Science, Technology And Research
Priority to PCT/SG2020/050682 priority Critical patent/WO2022108517A1/fr
Publication of WO2022108517A1 publication Critical patent/WO2022108517A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B33—ADDITIVE MANUFACTURING TECHNOLOGY
    • B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
    • B33Y10/00—Processes of additive manufacturing
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
    • B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
    • B22F10/80—Data acquisition or data processing
    • B22F10/85—Data acquisition or data processing for controlling or regulating additive manufacturing processes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
    • B29C64/30—Auxiliary operations or equipment
    • B29C64/386—Data acquisition or data processing for additive manufacturing
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
    • B29C64/40—Structures for supporting 3D objects during manufacture and intended to be sacrificed after completion thereof
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B33—ADDITIVE MANUFACTURING TECHNOLOGY
    • B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
    • B33Y50/00—Data acquisition or data processing for additive manufacturing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22F—WORKING METALLIC POWDER; MANUFACTURE OF ARTICLES FROM METALLIC POWDER; MAKING METALLIC POWDER; APPARATUS OR DEVICES SPECIALLY ADAPTED FOR METALLIC POWDER
    • B22F10/00—Additive manufacturing of workpieces or articles from metallic powder
    • B22F10/20—Direct sintering or melting
    • B22F10/28—Powder bed fusion, e.g. selective laser melting [SLM] or electron beam melting [EBM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/10—Additive manufacturing, e.g. 3D printing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/18—Manufacturability analysis or optimisation for manufacturability

Definitions

  • the present disclosure relates to methods and systems for optimization of support structures in additive manufacturing.
  • SLM Selective laser melting
  • AM additive manufacturing
  • SLM is a metal printing technology that is used to directly produce parts designed for functionality and performance.
  • DMLS Direct Metal Laser Sintering
  • AM additive manufacturing
  • Parts having a design with overhanging features at an angle of more than 45° typically require support structures to be able to be printed successfully by SLM.
  • the support structures function to transfer heat away from the laser melt pool, hold the weight of the overhanging features, and prevent warpage due to thermal stresses.
  • the support structures are printed with the same material as the main part and are welded together, they are as strong as the part and are thus extremely difficult, time consuming and expensive to remove.
  • the supports are removed manually by hand tools such as pliers, files, hammer and chisel, and/or by using machining processes, in which additional jigs and fixtures and time consuming CNC (Computer Numerical Control) programming are needed. This removal process is inevitably messy, and many stubborn supports cannot be removed completely without adversely affecting the surface of the main part.
  • the present disclosure relates, in a first aspect, to a computer-implemented method for optimizing a support structure of a part to be built in an additive manufacturing process, the method comprising:
  • the present disclosure also relates, in a second aspect, to a system for optimizing a support structure of a part to be built in an additive manufacturing process, comprising at least one processor in communication with computer- readable storage having instructions stored thereon for causing the at least one processor to perform an optimization process comprising:
  • the present disclosure relates to non-transitory computer readable storage having stored thereon instructions for causing at least one processor to perform a method as disclosed herein.
  • Figure 1 is a block diagram of an embodiment of a system for optimization of support structures for additive manufacturing
  • Figure 2 is a flow diagram of an example of a process for optimization of support structures for additive manufacturing
  • Figure 3 is a flow diagram of an example of a simulation process of the process of Figure 2;
  • Figure 4 shows an example sequence in a graphical user interface during generation of a mesh for the simulation process of Figure 3;
  • Figure 5 shows an example optimized support structure design
  • Figure 6 shows graphs of comparisons between measured and simulated results using the simulation process of Figure 3;
  • Figure 7 shows a comparison between measured and simulated results using the simulation process of Figure 3 for a first example build
  • Figure 8 shows a comparison between measured and simulated results using the simulation process of Figure 3 for a second example build.
  • Figure 9 shows a comparison between measured and simulated results using the simulation process of Figure 3 for a third example build.
  • Embodiments of the present disclosure provide techniques to optimize the support structure of a part built by an additive manufacturing process, and to thereby reduce the difficulty of support structure removal. Embodiments enable a significantly shortened end-to-end AM process, increase productivity, and reduce trial and error between design and 3D printing.
  • metal printing process calibration and build process setting by optimizing build parameters such as the laser power and speed, to enable printing of a soft support that can be blasted away and yet is hard enough to perform as a support in the usual manner;
  • embodiments of the present disclosure do not require modification of the design of the part to ensure printability. Instead, only the support design is modified to intentionally create a soft support that is brittle enough that it can be blasted away while still being strong enough to perform as a support during the additive manufacturing process. Accordingly, freedom of design for the part is preserved, while also greatly easing removability of the support once the part is built.
  • the presently disclosed embodiments provide a systematic solution for design and printing of support structures during metal 3D printing processes, such as SLM printing.
  • Embodiments provide a "soft" support system that can be removed easily by means of abrasive blasting (e.g.
  • hard supports In essence, a hybrid system of hard and soft supports can be applied to manufacture a part successfully, with few design constraints.
  • Embodiments also provide an integrated simulation platform to enable support system design.
  • the simulation platform may perform pre-processing work, as well as functioning as a solver to perform a simulation to calculate the residual stress and distortion.
  • FIG. 1 there is shown a high-level architecture of a system 100 for optimization of support structures for additive manufacturing.
  • the optimization system of the described embodiments is in the form of a computer system, this need not be the case in other embodiments.
  • the system of the described embodiments may be a 64-bit Intel Architecture computer system, and the optimization processes executed by the system may be implemented as programming instructions of one or more software modules 102 stored on non-volatile (e.g., hard disk or solid-state drive) storage 104 associated with the computer system.
  • non-volatile (e.g., hard disk or solid-state drive) storage 104 associated with the computer system e.g., hard disk or solid-state drive) storage 104 associated with the computer system.
  • non-volatile storage 104 associated with the computer system.
  • at least parts of these processes could alternatively be implemented in one or more other forms, for example as configuration data of a field-programmable gate array (FPGA), or as one or more dedicated hardware components, such as application-specific integrated circuits (ASICs), or as any combination of such forms.
  • FPGA field-programmable gate array
  • ASICs application
  • the optimization system 100 includes random access memory (RAM) 106, at least one processor 108, and external interfaces 110 and 114, all interconnected by a bus 116.
  • the external interfaces include a network interface connector (NIC) which connects the optimization system to a communications network such as the Internet, and universal serial bus (USB) interfaces, at least one of which may be connected to user input devices such as a keyboard and a pointing device such as a mouse, and a display adapter 114, which may be connected to a display device such as an LCD panel display 122.
  • the optimization system also includes an operating system 124 such as Linux or Microsoft Windows.
  • optimization processes according to embodiments may be performed at least in part by software modules 102.
  • the software modules 102 may comprise:
  • An I/O module 142 for importing input data, such as model data (e.g. in STL format), to the system 100, and for outputting build data (e.g. in VTK format).
  • the model data may comprise a model for the part and a model for the support structure.
  • the output build data may be viewed by a user via display 122, or exported to other software modules, executing on system 100 or another device, for post-processing.
  • the I/O module 142 may use a graphical user interface (GUI) component (not shown) to facilitate import, export and manipulation of data by the user.
  • GUI graphical user interface
  • the preprocessing module 144 may also enable the user to edit the support model (for example, to manually specify particular regions of the support as hard or soft), and to specify material properties for the simulation, for example. This may be done via the GUI component.
  • the preprocessing module 144 may automatically generate a proposed support structure, according to methods known in the art.
  • a simulation module 146 that simulates a build given a part geometry, a support geometry, material parameters and build parameters (such as laser power and speed).
  • the simulation model may implement a finite element method (FEM) solver that can predict the residual stress and distortion during printing.
  • FEM finite element method
  • An optimization module 148 that is responsible for overall control of the optimization process, including calling the simulation module, and modulating the support geometry and/or build parameters in accordance with the simulation results to generate an optimized support structure.
  • calibration data may be obtained for specific materials, build parameters, and 3D printing platforms, and these may be stored in a database 132 accessible by the system 100 for use by the optimization module 148 and/or simulation module 146, for example.
  • the database 132 may store a mapping from material parameters and/or build parameters (such as laser power, laser speed, layer thickness, and laser spot size) to inherent strain, such that the inherent strain can be used as input to the simulation module 146.
  • the database 132 may be part of storage medium 104, or may be stored externally for retrieval by the system 100.
  • a simulation may be conducted in two stages to achieve the design objective.
  • all support structures are assumed to be soft. This configuration is used to evaluate the residual stress and distortion in the simulation. If the stress and distortion profiles are acceptable at all locations on the part according to at least one build quality criterion (such as a distortion acceptance criterion), no further modifications are needed to the support structures. However, if the at least one build quality criterion is not met at one or more locations, the soft supports are replaced with hard ones at those locations and a second-stage simulation of the modified configuration is carried out. This second stage may be performed iteratively until the build quality criterion (e.g., distortion acceptance criterion) is satisfied at all locations.
  • the build quality criterion e.g., distortion acceptance criterion
  • the process 200 begins at step 202 by importing the part and support structure models, for example using one or more STL files. After importing the support structure(s), an option may then be made available to allow the user to assign the property of each part of the support structure, i.e. whether it is soft or hard in a particular region. In the following discussion, it is assumed that all support structures are set as soft at the start of process 200. The properties of selected materials may also be set at this stage.
  • a mesh is also generated for the part and the support structure.
  • voxel meshing may be used and the user may choose the number of voxels in each direction aligned to the x-, y-, and z-axis, and the preprocessing module 144 may automatically mesh the part and support structure in voxels.
  • FIG 4(a) shows a display generated by IO module 142 as part of a user interface on display 122 of the system 100.
  • the STL file containing the part model 402 is imported and displayed in the user interface.
  • the STL file or files containing support model 404 is or are imported, and the support model 404 is displayed in the user interface.
  • the part model 402 and support model 404 are voxelized to generate a voxelized part model 412 and a voxelized support model 414.
  • the voxelization may be performed using any suitable method.
  • a build is simulated. This is done using the generated mesh 412, 414, the type of input material, and one or more build parameters (such as support structure type, soft or hard).
  • the simulation module 146 may then retrieve, from database 132 of system 100, the inherent strain based on the type of input material and the one or more build parameters (e.g. support structure type).
  • the GUI component may present a "run" button on display 122 that may be clicked by the user to initiate the simulation. Simulation module 146 may then conduct the simulation layer-by-layer (as will be described in further detail below).
  • the simulation step 204 may generate a distortion profile and a residual stress profile for the part.
  • one or both of these profiles may be analyzed to determine whether any regions of the part do not meet a build quality criterion, such as whether any regions are above a distortion threshold, and thus whether the part is likely to fail during the build.
  • a distortion threshold that is approximately equal to a tolerance value for the additive manufacturing machine that is to be used to build the part (e.g. 0.2mm for the EOS M 280 3D printing machine) may be specified, and any regions of the part that are above that threshold may be flagged as failure regions.
  • the flow after step 206 may depend on whether iterative updating of the one or more build parameters for the support structure is to be performed.
  • the process 200 may involve two or more iterations in which build parameters are updated, and the build is re-simulated to assess whether any regions of the part to be printed are above the distortion threshold, with one or more further rounds of parameter updating and resimulation being performed until all regions of the part are below the threshold or a maximum number of iterations is reached.
  • the process may involve just a single instance of parameter updating and resimulation.
  • the optimization module 148 may check whether a maximum number of iterations has been reached (if only a single iteration is to be performed, then the maximum number of iterations is 1). If not, the flow proceeds to step 210, where sections of the support structure at the failure regions have their build parameters modified such that they are changed from soft to hard.
  • the build parameters may be modified to be the same as for the part, or to be such that the sections of the support structure at the failure region are harder than the soft sections so as to reinforce the failure regions, but softer than the part.
  • the build parameter may simply be an indication as to whether the support structure is soft or hard in a given region.
  • a soft or hard support structure may correspond to a specific respective processing condition or respective set of processing conditions, such as a specific energy density, or a specific laser power, laser speed, laser spot size, and/or layer thickness, or a value derived from a combination of any two or more of these.
  • processing conditions may be determined in advance from calibration experiments for a particular material and stored in database 132, for example.
  • processing returns to step 204 to perform another round of simulation, and a further check of the distortion profile is conducted at step 206. If all regions of the part are below the distortion threshold, then the modified build parameters for the support structure are output as part of the modified build data at step 214.
  • the support geometry may be modified to further reinforce it. This may be done manually by the user on visual inspection of the distortion profile and/or residual stress profile of the part. The modified elements in the support structure geometry may be flagged as hard, for example. Processing then continues to steps 204 and 206 for a further round of simulation and assessment of the distortion profile.
  • modified build data comprising the modified support structure build parameters and/or modified support structure geometry are output at step 214.
  • the GUI component may present a visualization of the distortion and residual stress in the part.
  • the results may be exported using VTK files, and processed using commercially available 3rd-party post-processing software such as Paraview.
  • the simulation process 204 makes use of the inherent strain method for simulation of the thermomechanical properties of the part and support structure.
  • the £ e represents the reversible elastic strain
  • ,e th ,£ ph ,e c represent all kinds of irreversible inelastic strain such as plastic strain, thermal strain, strain caused by phase transition, and creep strain.
  • the sum of all inelastic strain components may be defined to be the inherent strain E* :
  • FIG. 3 shows a process flow and implementation of inherent strain method for an example simulation process 204, for example implemented by the simulation module 146 of the system 100.
  • the simulation process 204 begins.
  • the voxel mesh data generated by the preprocessing module 144 is imported.
  • step 304 the next layer / of the voxel mesh data is retrieved to solve the static elastoplastic problem with inherent strain method for layer /'.
  • the simulation process 204 solves the motion equation with finite element discretization and Newton-Raphson method as
  • K J n B T DBdV
  • RHS right hand side
  • B is the strain matrix and D is the tangent stiffness matrix.
  • f ext is the vector of external force which imposed by inherent strain, and f int is the internal force vector caused by the material deformation.
  • the stiffness matrix K and RHS force vector f are determined for layer /'.
  • the determination of the RHS force vector uses the inherent strain. This in turn can be retrieved from database 132 (for example) based on the material type and one or more build parameters, such as whether a given region of the geometry corresponds to the part, a soft support, or a hard support. Further, standard boundary conditions may be applied to the stiffness matrix and the RHS in accordance with usual FEM procedures.
  • step 316 the simulation module 144 checks whether the last layer has been reached, and if so, the process ends at step 318. Otherwise, processing returns to step 304 to simulate the next layer.
  • Figure 5 shows an example optimized support structure design.
  • the part 600 is a castor fork, and has a corresponding support structure having a plurality of support elements. Some of the support elements, such as elements 602 and 604, extend between the part 600 and the build platform 610, while other support elements, such as elements 606 and 608, span between points on the part 600.
  • each of the support elements is designated as a hard support element.
  • Figure 5(b) which includes an example of a support structure optimized in accordance with embodiments of the present disclosure, some of the support elements, such as modified support elements 606' and 608', are able to be designated as soft support elements (that are therefore more easily able to be removed by methods such as bead blasting), while other support elements 602 and 604, which underlie relatively steep overhanging sections of the part 600, remain as hard support elements.
  • the geometry of the support structure may be modified to reinforce the part, such as shown in Figure 5(c) where support structure element 602' is of greater width than support structure element 602.
  • the support structure with modified geometry shown in Figure 5(c) is of conventional design.
  • the support structure may be optimized by softening at least part thereof, as shown in Figure 5(d), where support structure elements 606' and 608' are softer than support structure elements 602' and 604, and thus more easily removed.
  • Results from the simulation process 204 were compared with those from measurements of various different types of build to calibrate the simulation and to assess its accuracy.
  • Measurements were conducted on various 3D printed test coupons to determine 3D surface geometry data of each test coupon.
  • 3D scans actual data
  • 3D CAD model nominal data
  • a surface comparison enables deviations between the actual and nominal data to be identified.
  • Figure 8 shows a comparison between measured and simulated results for a real bracket part.
  • the part is designed with a soft support structure to support the large hollow cylinder, and the part was printed using 316L.
  • the part geometry together with the support structure was simulated in the simulation software.
  • Figure 8(a) shows the measurement of the as-printed part.
  • the distortion is essentially small, and all distortion values are within the tolerance of the printer. Comparing the experimental measurement and simulation results, it was found that even with these small distortion values, the simulation could essentially capture the characteristics of distortion in the experiment ( Figure 8(b)). All the corresponding points indicated in the picture show a reasonable match, validating the simulation.
  • Figure 9 shows a comparison between simulation and measurement for different designs with different support types.
  • Figure 9(a) shows the original design with a full-soft support structure. As this configuration was confirmed not to be able to be printed successfully, a pure simulation was performed for a first trial. The simulation result shows that a critical distortion will occur at Point B, where the support type should be changed from soft to hard, which could provide a stronger supporting effect.
  • Figure 9(b) shows the modified design, which did not change the shape of any support element, but just changed the support type below Point B from soft to hard. This configuration is referred to as "hybrid".
  • the hardness of the support elements may depend on one or more build parameters, such as laser scanning speed, laser power, laser spot size, and layer thickness. It has been found through experiment that the energy density, which is a function of all of these, may be used as a proxy for hardness.
  • a relative energy density e.g. for the support relative to the part
  • a "hard” support element may have the same hardness (i.e. be printed using the same energy density) as the part, or with an energy density that is less than that used to print the part, but more than that used to print a "soft" support element.
  • energy density is a dependent quantity, it may be varied by varying any one or more of the quantities on which it depends, while keeping others constant. For example, energy density may be varied by varying only the scanning speed, and it may be convenient to do this rather than varying multiple quantities at once.
  • Example values used for various printing platforms and materials, and which resulted in soft supports able to be removed by shot peening or sand blasting, are set out in the tables below.

Abstract

Procédé mis en œuvre par ordinateur pour l'optimisation d'une structure de support d'une pièce à construire dans un procédé de fabrication additive comprenant : (a) l'obtention de données de construction comprenant un ou plusieurs paramètres de construction pour la structure de support qui présentent des valeurs initiales de sorte que l'intégralité de la structure de support est conçue pour être plus souple que la partie lors de la construction ; (b) la détermination, par une première simulation, du fait qu'il pourrait y avoir de quelconques régions de défaillance si la partie et la structure de support sont construites selon les données de construction ; (c) pour chaque région de défaillance, la modification des données de construction en changeant lesdits paramètres de construction de la structure de support vers des valeurs modifiées respectives dans la région de défaillance pour durcir la structure de support au niveau de la région de défaillance ; et (d) la détermination, par une seconde simulation, du fait que de quelconques régions de défaillance demeurent après la modification des données de construction.
PCT/SG2020/050682 2020-11-20 2020-11-20 Optimisation de structures de support pour la fabrication additive WO2022108517A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100042241A1 (en) * 2006-10-10 2010-02-18 Tomoyuki Inoue Modeling data creating system, manufacturing method, and modeling data creating program
US20160370793A1 (en) * 2015-06-17 2016-12-22 Roland Dg Corporation Support arrangement determining apparatus, three-dimensional printing system, and method of determining support arrangement
US20180311732A1 (en) * 2017-04-28 2018-11-01 Divergent Technologies, Inc. Support structures in additive manufacturing
WO2019055181A1 (fr) * 2017-09-12 2019-03-21 General Electric Company Structures de support optimisées pour fabrication additive
EP3650222A1 (fr) * 2018-11-07 2020-05-13 Additive Works GmbH Procédé de fabrication additive, en particulier de fusion sélective de faisceau laser à base de poudre

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100042241A1 (en) * 2006-10-10 2010-02-18 Tomoyuki Inoue Modeling data creating system, manufacturing method, and modeling data creating program
US20160370793A1 (en) * 2015-06-17 2016-12-22 Roland Dg Corporation Support arrangement determining apparatus, three-dimensional printing system, and method of determining support arrangement
US20180311732A1 (en) * 2017-04-28 2018-11-01 Divergent Technologies, Inc. Support structures in additive manufacturing
WO2019055181A1 (fr) * 2017-09-12 2019-03-21 General Electric Company Structures de support optimisées pour fabrication additive
EP3650222A1 (fr) * 2018-11-07 2020-05-13 Additive Works GmbH Procédé de fabrication additive, en particulier de fusion sélective de faisceau laser à base de poudre

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