Flexibility of features of an object to be additively manufactured

Abstract

In one example, a method includes identifying features smaller than a threshold size of an object to be generated in additive manufacturing via processing circuitry. In some examples, the method also includes determining, by the processing circuitry, a first density of printing agent to be applied to the build material when generating the identified features of the object, based on the expected flexibility of the object's features. The method may also include determining, by the processing circuitry, a second density of printing agent to be applied to the build material when generating another feature of the object.

Description

[0001] Technical content

[0002] This disclosure generally relates to the flexibility of features of objects to be additively manufactured. Background Technology

[0003] Additive manufacturing techniques can, for example, generate three-dimensional objects by curing building materials on a layer-by-layer basis. In examples of this technique, building materials can be supplied layer by layer, and curing methods can include heating layers of building materials to melt selected areas. Other techniques may use chemical curing methods. Summary of the Invention

[0004] According to a first aspect of this disclosure, a method for additive manufacturing is provided, comprising: identifying by a processing circuit a feature of an object to be generated in additive manufacturing that is smaller than a threshold size; determining by the processing circuit a first density of printing agent to be applied to a building material when generating the identified feature of the object based on the expected flexibility of the feature of the object; and determining by the processing circuit a second density of printing agent to be applied to the building material when generating another feature of the object that is larger than the threshold size.

[0005] According to a second aspect of this disclosure, an apparatus for additive manufacturing including processing circuitry is provided, the processing circuitry comprising: a feature recognition module for recognizing features of a size less than a threshold in object model data to be generated in additive manufacturing; and a flux module for determining an area coverage of flux to be applied to a build material during the generation of the object based on a correlation between the flux and the flexibility of the identified features of the object at generation, wherein the flux module is further configured to determine: a first area coverage of flux to be applied when generating the identified features; and a second area coverage of flux to be applied when generating other features larger than the threshold size.

[0006] According to a third aspect of this disclosure, an object generated by additive manufacturing is provided, comprising: a flexible feature smaller than a threshold size generated using a first density of flux; and a feature larger than the threshold size generated using a second density of flux. Attached Figure Description

[0007] A non-limiting example will now be described with reference to the accompanying drawings, in which:

[0008] Figure 1 This is a flowchart of an exemplary method for determining the density of the flux to be used in additive manufacturing;

[0009] Figure 2 This is a flowchart of an example method for generating objects in additive manufacturing;

[0010] Figure 3 This is a flowchart of an exemplary method for identifying small features of an object to be generated in additive manufacturing;

[0011] Figure 4A This is a simplified schematic diagram of an example of an object generated in additive manufacturing;

[0012] Figure 4B It is a graph showing an example of the force used to insert the brush into the opening and remove the brush from the opening;

[0013] Figure 5 This is a simplified schematic diagram of an example of a device used for processing data from additive manufacturing;

[0014] Figure 6 This is a simplified schematic diagram of an exemplary apparatus for additive manufacturing; and

[0015] Figure 7 This is a simplified schematic diagram of an exemplary machine-readable medium associated with a processor. Detailed Implementation

[0016] Additive manufacturing technology generates three-dimensional objects by curing a build material. In some examples, the build material is a powdered particulate material, which can be, for example, plastic, ceramic, or metal powder, and the properties of the resulting object can depend on the type of build material and the type of curing mechanism used. The build material can be deposited, for example, on a print bed, and processed layer by layer, for example, in a manufacturing chamber. According to one example, a suitable build material could be the PA12 build material, commercially available from HP as V1R10A "HP PA12".

[0017] In some examples, selective curing is achieved through the directed application of energy, such as using a laser or electron beam, which causes the build material to solidify upon application of directed energy. In other examples, at least one printing agent can be selectively applied to the build material and can be liquid when applied. For example, a flux (also called a “coalescing agent” or “aggregator”) can be selectively distributed onto portions of the build material layer in a pattern derived from data representing slices of the three-dimensional object to be generated (which may be determined, for example, from structural design data). The flux may have an energy-absorbing component such that when energy (e.g., heat) is applied to the layer, it causes the build material to be applied to heat, coalesce, and solidify upon cooling to form slices of the three-dimensional object according to the pattern. In other examples, coalescence can be achieved in some other way.

[0018] According to one embodiment, a suitable flux may be an ink-type formulation containing carbon black, such as a flux commercially known as "HP Flux" V1Q60A, available from HP Inc., which may contain any one or any combination of infrared absorbers, near-infrared absorbers, visible absorbers, and ultraviolet absorbers. Examples of fluxes containing visible light absorption enhancers include dye-based and pigment-based colored inks, such as inks commercially known as CE039A and CE042A, available from HP Inc.

[0019] In addition to fluxes, in some examples, the printing agent may include a coalescing modifier, which is used to modify the effect of the flux, for example, by reducing or increasing coalescence, or to help produce a specific finish or appearance of the object, and such an agent may therefore be referred to as a refiner. In some examples, a refiner may be used near the edge surface of the object being printed to reduce coalescence. According to one example, a suitable refiner may be a formulation commercially available from HP as V1Q61A "HP Refiner". Colorants, such as dyes or stains, may be used in some examples as fluxes or coalescing modifiers, and / or as printing agents to impart a specific color to the object.

[0020] As described above, additive manufacturing systems can generate objects based on structural design data. This can involve a designer determining a 3D model of the object to be generated, for example, using a computer-aided design (CAD) application. The model can define the solid parts of the object. To generate a 3D object from the model using an additive manufacturing system, the model data can be processed to define slices or parallel planes of the model. Each slice can define a portion of a corresponding layer of building material to be cured or coalesced by the additive manufacturing system.

[0021] Additive manufacturing may be an attractive alternative to other manufacturing technologies; however, the costs associated with adopting new manufacturing processes can deter users from doing so. For example, a user might want to match the physical properties of an object generated using a new process with those generated using an existing process; however, the time, cost, and resources required for matching properties could be so great that they at least seem to outweigh the benefits of implementing the new process.

[0022] Figure 1 This is one example of a method that may include a computer-implemented approach for determining the density of a flux to be used in additive manufacturing.

[0023] In block 102, the method includes processing circuitry identifying features smaller than a threshold size for an object to be generated in additive manufacturing. Features smaller than the threshold size may also be referred to herein as small features, and in some examples, they are features that may have certain physical properties, such as flexibility, that can be affected by the density of the flux used in generating the feature. In some examples, small features are identified such that they can be treated differently from larger features during additive manufacturing to ensure that they form as expected or form with specific desired properties.

[0024] The identification of features smaller than a threshold size in the object to be generated can be performed by analyzing a model representing the object to be generated to determine whether there is a portion of the object model smaller than a threshold size. For example, dimensions such as cross-sectional dimensions, length dimensions, and / or cross-sectional areas can be compared with predetermined thresholds. The object model data can represent at least a portion of an object to be generated by an additive manufacturing apparatus through fusing building materials. In some examples, the object portion can include layers of the object, for example, layers formed in a layer-by-layer manufacturing process such as additive manufacturing. The object model data can include, for example, a computer-aided design (CAD) model, and / or can be, for example, a stereolithography (STL) data file.

[0025] Object model data can include features of various sizes. Some features can be relatively small, or relatively small within a layer. There can be minimum feature sizes that can be generated by a given additive manufacturing apparatus, for example, a finite resolution related to the precision at which build material and / or printing agent can be placed. Some techniques allow for precise placement of printing agent on the build material, for example, by applying printing agent using a printhead that operates according to the inkjet principle of 2D printing. In some examples, the printhead can be controlled to apply printing agent at a resolution of approximately 600 dpi or 1200 dpi. At 600 dpi, this theoretically means that features as small as 42 micrometers can be generated, depending on the thickness of the layers used to generate the object. However, as mentioned above, energy can be applied (e.g., using a heating lamp) to melt the build material, and such a small area of ​​reagent-treated build material may not absorb enough energy to reach the melting temperature of the build material, which can be at least substantially completely melted so that it will coalesce and melt to form solid portions of the object upon cooling. Therefore, in practice, in some examples, the minimum “printable” feature size may not be determined by the resolution of the object generating device, but by the temperature that this feature can reach during the fusion process. However, any threshold can be set in box 102, which can be set independently of the resolution of the object generating device, and therefore can be larger than the minimum printable feature size.

[0026] In some examples, box 102 may include identifying small features with additional dimensional characteristics and / or geometric properties such as aspect ratio. For example, a feature may have a relatively small cross-section and a relatively long length, such as needle-like, thread-like, silk-like, and / or brush-like features, or beams or pillars. Such features may be identified, for example, by comparing the cross-sectional dimension to the length dimension to determine the ratio. In some examples, this may be compared with thresholds, such as a threshold maximum cross-sectional dimension and / or a threshold minimum length dimension for features to be identified as smaller than the object size.

[0027] Analyzing object model data to identify such small features can include a variety of methods, examples of which are described in more detail below.

[0028] In block 104, the method includes determining a first density of the printing agent to be used when generating the identified features of the object, based on the expected flexibility of the object's features, by a processing circuit (which may be the same as the processing circuit used in block 102). In some examples, the printing agent is a flux. The physical properties of the object generated in additive manufacturing can depend on the type of build material used and the type of curing mechanism as described above. However, physical properties can also be affected by processing parameters used in the additive manufacturing process, such as temperature or the type and amount of reagent used. For example, if a higher density flux is deposited within a feature of the object during generation, that feature can absorb more heat and reach higher temperatures for a longer period, resulting in increased fusion of the build material at that location, which may be referred to as "overmelting," relative to locations with a lower density flux. Therefore, applying a higher density flux to some features can increase rigidity and decrease flexibility. Conversely, a lower density flux can produce features with relatively high flexibility. In some examples, the flux density may be referred to as area coverage (e.g., x drops per cm). 2 ).

[0029] Smaller features may be more susceptible to changes in flux density than larger features. For example, a relatively small change in flux density in a small feature can lead to a relatively significant change in physical properties, while a similar change in flux may have no perceptible effect on the physical properties of other larger features. For instance, in the case of flexible physical properties, large features may be relatively rigid due to their large size, while smaller features (especially including brush-like features) may be relatively flexible, and changing the flux density (without altering the composition of the flux itself) can affect how flexible they are.

[0030] Some objects may have a predetermined desired flexibility. For example, an object may have been previously manufactured using different processes, and a user may expect the properties of the object when manufactured using additive manufacturing to match the properties of the object when manufactured using the previous process. Therefore, desired flexibility can be the flexibility of a feature when manufactured using the previous process. In other examples, the desired flexibility of a feature can be related to the expected functionality of the resulting object.

[0031] In box 106, Figure 1 The method also includes a second density of the printing agent to be applied when generating another feature of the object (e.g., when generating other features larger than a threshold size), determined by processing circuitry (which may be the same as the processing circuitry used in boxes 102 and / or 104). In some examples, the printing agent is a flux. In some examples, a standard or default density of the flux is used as a second density for generating one or more other features. For example, a first density may be determined to be different from the second density. As described in more detail below, in some examples, a first default or initial density is used to determine the first density and / or a second (different) default or initial density is used to determine the second density, wherein these densities may be modified prior to generating the object such that the local density of the deposited flux may differ from the first and / or second default / initial densities. In some examples, the first default density is not modified, and the second default density may be modified. For example, the second default density may be modified based on the predicted temperature of the building material at each location, while the first density may be based on the predicted temperature of the feature regardless of the expected flexibility. This may, for example, reflect the possibility of a larger thermal gradient and / or deformation in portions of a “bulk” object (i.e., those portions that are not small portions) than in the identified small features. In some examples, the initial or default density can be modified across parts of the object. For example, the amount of flux applied can be locally adjusted. In some examples, the second density can be the average density of the flux to be applied to a part of the bulk object, and / or the local density to be applied within that part of the bulk object. For example, the second density can be a local density determined based on the default or initial density.

[0032] Figure 2An example of a process is shown that may include a computer-implemented method for generating an object in additive manufacturing. In this example, it may be intended to generate an object with a specific mass, referred to herein as a "product object". In practice, it may also be intended to mass-produce instances of product objects. In block 202, the method includes identifying features smaller than a threshold size of the product object during generation via processing circuitry, wherein identifying features includes selecting features with dimensions below the threshold size. In this example, the dimension is a cross-sectional area and / or the distance between opposite faces of the feature. The method may include determining the dimension of the feature, such as the length or width of the feature, and comparing the determined dimension to a threshold. In some examples, the distance may be a minimum distance between opposite faces. In other examples, a slice through the object may be analyzed and the cross-sectional area of ​​the feature may be determined. The threshold may be predetermined and may correspond to a size below which flexibility can be significantly affected by flux density. In some examples, the length of the feature may also be considered, for example, such that the feature has at least a threshold length, or a predetermined ratio to the cross-sectional size or area (e.g., a predetermined aspect ratio).

[0033] In this example, the expected flexibility can be selected by the user, and the method includes allowing the user to select the expected flexibility in box 204. The user can specify the flexibility that features of their intended product object will have when they are generated, and the flux density can be determined based on the user's selection, such as based on a lookup table. In other examples, the user can choose the flux density to use when generating small features, rather than selecting the expected flexibility. In some examples, the relationship between flux density and flexibility is predictable and / or predetermined, so the flux density can be selected accordingly. However, in this example, a test object is generated to help determine the flux density to be used in the product object. In some examples, an object model representing the object to be generated can define the expected flexibility of the object's features, and selecting the expected flexibility can include obtaining the expected flexibility from the object model.

[0034] In box 206, the method includes generating a first test object using a first candidate density of a printer while generating the identified features. The printer may be a flux. The first test object can be generated using the same object model as the product object intended to be generated, thereby generating a test object similar to the product object to be generated. In other examples, a portion of the object model can be used, wherein that portion includes the identified features. In other examples, the test object may differ from the object model of the product object intended to be generated and may be an abstract representation of the identified features, for example, it may include features of similar size and / or shape.

[0035] In box 208, the method includes generating a second test object using a second candidate density of the printing agent when generating the identified feature. The printing agent can be the same type of printing agent used in box 206. In the example, the printing agent can be a flux. It should be noted that since the generated feature is nominally the same as the feature generated in box 208, the density of the printing agent applied is different in each case due to the difference between the first and second candidate amounts. The second test object can be generated using substantially the same instructions as the first test object, except for the amount of flux used to generate the identified feature. Using different amounts of flux to generate the small feature when the flux density is substantially constant throughout the identified small feature results in the deposition of flux with different densities. In some examples, additional test objects are generated to characterize the space of possible flux amounts and their relationship to flexibility. (The following section discusses...) Figure 4B In the illustrated example, five test objects can be generated, each using a different density of flux to construct or generate the identified feature, but otherwise remaining essentially the same. Therefore, in this example, the amount of flux used to generate the feature varies for different objects. In other examples, such as those aimed at accurately determining the relationship between flux density and flexibility, more or fewer test objects can be generated, each using a different density of flux to construct the identified feature.

[0036] In box 210, the method includes selecting a first density of the printing agent based on the flexibility of features on a first test object and the flexibility of features on a second test object (or more generally, based on the flexibility of features on multiple test objects). In some examples, the flexibility of a feature on each test object can be measured to determine the feature and the flexibility of the test object that best approximate the selected desired flexibility. The first density can then be set to the density used when generating the selected test object. For example, multiple test objects can be generated, each using a different density of printing agent to generate the identified features. The flexibility of each test object(s) feature(s) can be measured, and an object comprising features(s) having a flexibility that best matches the desired flexibility can be identified, and the density of the printing agent used to generate the features in that object can be used when generating the features of the object in production. In other examples, interpolation between measurements of flexibility can also be used to determine the first density. For example, the flexibility of each test object(s) feature(s) can be measured, and the measured flexibility of each test object(s) feature(s) is used to characterize the relationship between the density and flexibility of the flux, for example, using line fitting, regression analysis, such as linear regression or multinomial regression. In such an example, the density of the flux used to generate a flexibility between the flexibility of the two test objects can be extrapolated or interpolated based on this relationship.

[0037] In this example, a lower first density is selected for higher expected flexibility, and a lower first density is used for higher expected flexibility.

[0038] In some examples, each test object can be generated in a separate build process (i.e., one test object at a time in the manufacturing chamber), while in other examples, multiple test objects can be generated in a single build process (i.e., multiple test objects in the manufacturing chamber, each using a different density to generate the identified feature). Generating multiple objects within a single build process reduces the time spent characterizing flux density. Although multiple test objects are generated in this example, in some examples, a single object with multiple features corresponding to the identified features can be generated, where each of the multiple features is generated using a flux of different densities.

[0039] In some examples, the method includes determining the relationship between the flux density, flexibility, and size of a feature of the object to be generated. In these examples, the flux density can be determined based on measurements of the flexibility of features of objects with different sizes, for example, the flux density can be determined based on the expected flexibility and feature size. Therefore, the flux density can be determined to achieve the expected flexibility without generating test objects for every possible feature size.

[0040] In box 212, the method includes determining an initial second density of the printing agent to be applied when generating other features of the object. See reference... Figure 1 The second density of the printing agent is determined as described in box 106.

[0041] In box 214, the method includes modifying an initial second density. In some examples, determining the second density includes modifying the initial second density value at at least one location on the build material layer. In some examples, the initial density value is modified based on at least one of a predicted or measured temperature distribution within the build chamber, the location of the object within the build chamber, the proximity of the object to another object to be generated within the build chamber, and / or the expected deformation of the object. This further modification may be intended to improve the dimensional accuracy or quality of the generated object by taking into account shrinkage, warping, or deformation caused by the build and cooling processes. While the initial second density may be the same for all object portions associated with it (e.g., bulk object portions), the modification of the second density may vary across these portions. For example, a lower density may be used at locations of predicted hot spots within the object. In some examples, the bulk portion may be printed with a second density, which is an average density, and the local density at which the printing agent is applied may be based on the initial second density but may vary between locations. In other examples, the second density may be a local density to be used within the object portion and may be determined based on the initial second density. Thus, in some examples, multiple second densities may be determined based on an initial or default density. In some examples, the first density is determined in a manner similar to the second density, although, as mentioned above, this modification can be performed with respect to object portions not identified as small features (which, as described herein, may be referred to as “bulk” object portions) rather than with respect to small features. In some examples, small features may be less susceptible to any effects from hot spots, deformation, and / or temperature gradients. In this way, the density of the flux applied to the small features can be controlled taking flexibility into account without additional consideration of such thermal control, whereas thermal control (e.g., localized modifications to the print dose) can be applied to the bulk object portions.

[0042] In block 216, the method includes determining an additive manufacturing instruction or printing instruction that, when executed by an additive manufacturing apparatus, causes the apparatus to generate an object using a first density of printing agent when generating a feature, and to print another part of the object using a modified printing dose based on an initial second density. The instruction may include instructions for specifying the amount of an agent, such as printing agent, flux, or refiner, to be applied to each of a plurality of locations on a layer of build material. For example, determining the printing instruction may include determining “slices” of a virtual manufacturing chamber containing at least one virtual object and rasterizing these slices into pixels. For example, a certain amount of printing agent (or no printing agent) may be associated with each pixel to provide a first density on average across the location of the feature. Typically, if the pixels of a slice are associated with an area of ​​a manufacturing chamber intended to be cured, the printing instruction may be determined to specify that flux should be applied to the corresponding area of ​​the build material during object generation. However, if the pixels are associated with an area of ​​the manufacturing chamber intended to remain uncured, the printing instruction may be determined to specify that no agent or coalescing modifier, such as a refiner, may be applied thereto. Furthermore, the amount of such agent may be specified in the printing instruction. For example, for fluxes, these quantities can be determined based on a first density and a second density.

[0043] In box 218, the method includes executing instructions to generate an object. This could include, for example, forming a build material layer, applying printing ink to a specified location in the object model data corresponding to a slice of the object model using at least one inkjet liquid distribution technique, and applying energy, such as heat, to the layer. Some techniques allow for precise placement of printing ink on the build material, for example, by applying printing ink using a printhead that operates according to the inkjet principle of 2D printing; in some examples, the printhead can be controlled to apply printing ink at a resolution of approximately 600 dpi or 1200 dpi. Another build material layer can then be formed, and the process can be repeated, for example, using the object model data of the next slice.

[0044] Figure 3 An example of a process is shown, which may include a computer-implemented method for identifying small features of an object to be produced in additive manufacturing, and may replace... Figure 1 box 102 or Figure 2The process is performed in box 202. In box 302, the method includes reducing the initial volume of an object by a threshold amount in at least one spatial dimension to provide an eroded object volume. In this example, the initial object volume is reduced by erosion in two spatial dimensions within the layer (i.e., the layer is considered a two-dimensional plane). For example, if the layer comprises a slice along the Z-axis in the XY plane, the object portion can be eroded in the X and Y dimensions. In other examples, if the object portion is associated with depth (e.g., depth varying across the object portion), it can be eroded in three dimensions. For example, the object portion can be eroded approximately 1-3 mm, or 1-5 mm, in each of the X and Y dimensions. The threshold amount in this example can be determined based on the fact that the characteristics of this size can be associated with the expected flexibility that can be controlled by flux density. In other examples, the threshold can vary, for example, depending on the material. In a practical case, in this example, eroding the initial object volume by 1 mm includes determining an inner perimeter 1 mm away from any remaining edges.

[0045] In box 304, the method includes increasing the volume of the object to be eroded by the threshold amount in at least one spatial dimension to provide an expanded object volume. Features smaller than the threshold size will be completely eroded and therefore not recovered by this expansion, while features larger than the threshold size will be recovered and thus can be identified.

[0046] In box 306, the method includes comparing the expanded object volume to the initial object volume, and in box 308, identifying the difference between the expanded object volume and the initial object volume as candidate features smaller than a threshold size. In some examples, any feature of the object / object portion present in the initial object volume but not in the expanded object volume can be identified as a small feature. In other examples, user and / or object model data can indicate which of the identified features will be associated with the expected flexibility (and in some examples, different features can be associated with different expected flexibility), where other features have a default flexibility and / or are associated with a default printhead density.

[0047] In other examples, small features can be identified in a different way. For example, the distance between opposite faces in an object's mesh model can be determined, or the number of voxels in a cross-section of an object feature can be counted. In yet another example, a user can "label" a feature as a small feature. In other examples, as mentioned above, the aspect ratio of (multiple) features can be considered. In some examples, a combination of these methods can be used.

[0048] Figure 4AAn example of an object 400 that can be generated in additive manufacturing is depicted. The object 400 includes a flexible feature smaller than a threshold size generated using a first density of flux and a feature larger than the threshold size generated using a second density of flux, wherein the second density of flux is greater than the first density of flux.

[0049] In this example, object 400 is a brush, and the flexible feature is each of the plurality of bristles 406. Object 400 also includes features larger than a threshold size, in this example, the handle portion 402 and the shaft 404. Features larger than the threshold size (i.e., in this example, the handle portion 402 and the shaft 404) may be rigid or relatively rigid relative to the bristles 406. This is at least in part due to their larger size and / or the larger density of the flux used in generating those portions of the object.

[0050] The handle 402 is located at the first end of the shaft 404, and the bristles 406 extend from the shaft 404 near its second opposite end. In some examples, the brush 400 can be used to apply cosmetics, such as mascara. In other examples, the object can be a different type of brush, such as a toothbrush or hairbrush, or a brush used in a machine. In other examples, the object can be an object such as a sponge or foam material (e.g., for shoe insoles) that includes a network of small interconnected portions and / or can have a mesh-like form or be formed with narrow supports.

[0051] In some examples, the first density used to generate the object is substantially constant throughout the generation of the first feature. The second density can be an average density, and the flux density can vary throughout the generation of the second feature. When the second feature is large, it may be subjected to thermal gradients, which can lead to overheating and / or deformation introduced during build-up and cooling. Therefore, additional variations in the flux density throughout the second feature can be used to compensate for these effects.

[0052] In this example, the small feature of the object 400 identified by this method is the bristles 406 of the brush 400. The bristles 406 can be identified as described above; for example, the cross-sectional diameter or area of ​​the bristles 406 can be compared to a threshold, and if the diameter or area is less than the threshold, the bristles 406 are identified. In other examples, [the method could be described using...]. Figure 3 The erosion and expansion methods described in the document, or the user can instruct the brush bristles on the object model to be flexible, or some other method can be used to identify small features.

[0053] To measure the flexibility of the bristles 406 of the brush 400, the brush 400 can be inserted into and removed from the orifice 408. The force used to push the brush 400 through the orifice in the first direction 410 can be measured as a function of the distance the brush 400 moves. Similarly, the force used when removing the brush 400 from the orifice 408 can be measured as the brush moves in the second direction 412.

[0054] Figure 4B This is a series of six graphs depicting the forces used to insert and remove the brush from the orifice. The first graph is labeled "Control," and the others are labeled with the letters A through E. All six graphs share a common vertical axis, which represents the force applied to the brush, in grams. The horizontal axis, in millimeters, shows the brush displacement—how far the brush 400 has moved relative to the orifice 408. Each of the six graphs shows two sets of data: data above and below the zero line. Data below the zero line represents the force applied to move the brush in the first direction 410, and data above the zero line represents the force applied to move the brush in the second direction 412. For a brush with high flexibility, a smaller force will be used when inserting and removing the brush 400 from the orifice 408, and for a brush with low flexibility, a larger force will be used when inserting and removing the brush 400 from the orifice 408.

[0055] This control graph illustrates the forces required to insert and remove a brush that has been manufactured (e.g., using a different manufacturing technique than those described herein) to achieve the desired quality. Each graph, labeled A through E, represents five brushes (brushes A through E) manufactured additively, where the bristles of each brush are made with a flux of varying densities. Brush A is made with the lowest flux density in its bristles, and each subsequent brush from B to E is made with an increasing flux density in its bristles. It can be seen that brush A requires the least force to insert into and remove from the orifice (the bristles are relatively flexible), while brush E requires the greatest force to insert into and remove from the orifice (the bristles are relatively stiff). Each of brushes A through E is a test object that has been generated to determine the flux density so that the additive manufacturing process is matched to the control brush. The dashed lines represent the target range of forces. It can be seen that the bristles of brush A are “too flexible” compared to the control brush, and therefore, the flux density used in the bristles of brush A is too low. Conversely, the bristles of brushes D and E are "not flexible enough" compared to the control brushes, therefore the flux density used in the bristles of brushes D and E is too high. The force used for brushes B and C is advantageous compared to the control brushes. Therefore, when producing additional brushes, a flux density equal to or between that used in producing the bristles of brushes B and C can be used.

[0056] Figure 5An example of an apparatus 500 including a processing circuit 502 is shown. The processing circuit 502 includes a feature recognition module 504 and a flux module 506.

[0057] In the use of device 500, feature recognition module 504 identifies features in the object model data that are smaller than a threshold size and are intended to be generated in additive manufacturing. Feature recognition module 504 can perform... Figure 1 Frame 102 Figure 2 Box 202 and / or Figure 3 The method described in boxes 302 to 308 identifies features smaller than the threshold size.

[0058] In the use of apparatus 500, flux module 506 determines the area coverage of flux to be applied to the build material when generating the object based on the correlation between flux density at generation and the flexibility of the identified features of the object. Fluid module 506 can determine the area coverage to be applied when generating the identified features of the object by determining the flux density to be applied when generating the identified features and other features of the object. Fluid module 506 can determine different area coverages to be used when generating different portions of the object; for example, different area coverages can be determined for the identified features relative to other features. The area coverage can be determined in a manner similar to, and can be considered similar to, a first density. A second area coverage for generating other features can be determined in a manner similar to, a second density, and can be considered similar to, it.

[0059] Figure 6 An example of a device 600 including processing circuitry 602 is shown. Processing circuitry 602 includes... Figure 5 The processing circuit 502 is a module of the processing circuit 602. The processing circuit 602 also includes an instruction module 604, and the apparatus 600 also includes an additive manufacturing apparatus 606 to generate an object according to an object generation instruction. In use of the apparatus, the flux module 506 is also used to determine a first area coverage of flux to be applied when generating the identified features and a second area coverage of flux to be applied when generating other features. For example, the flux module 506 can determine the area coverage of flux to be applied according to an object generation instruction. Figure 1 box 102 or Figure 2 When generating the identified features, the first area coverage or density to be applied to the box 210, and the determination based on... Figure 1 Box 104 or Figure 2 The second area coverage or density to be applied when generating other features in box 212.

[0060] In use of the apparatus, instruction module 604 is used to create object generation instructions that instruct the additive manufacturing apparatus to generate an object using a first area coverage of flux when generating identified features and based on a second area coverage of flux when generating other features.

[0061] Object generation instructions (which may also be referred to as printing instructions) can, in their use, control the additive manufacturing apparatus 606 to generate each of the multiple layers of an object. This can, for example, include specifying multiple area coverages of some printing agents (such as flux, dye, refiner, etc.). In some examples, object generation parameters are associated with object model sub-volumes (voxels or pixels). In some examples, printing instructions include a printing dose associated with the sub-volume. In some examples, other parameters can be specified, such as any one or any combination of heating temperature, build material selection, and the intent of the printing mode. In some examples, halftone processing can be applied to determine where to place the flux to provide first and second area coverages, or where to place other printing agents.

[0062] In use, the additive manufacturing apparatus 606 can generate objects in multiple layers (which may correspond to corresponding slices of an object model) according to printing instructions. For example, this may include generating at least one object layer-by-layer by selectively curing portions of the build material layers. In some examples, selective curing can be achieved by selectively applying printing ink, for example, using an "inkjet" liquid distribution technique, and applying energy, such as heat, to the layers. The additive manufacturing apparatus 606 may include any or any combination of additional components not shown herein, such as a manufacturing chamber, a print bed, multiple printheads for dispensing printing ink, a build material dispensing system for providing build material layers, and an energy source such as a heating lamp.

[0063] In some examples, processing circuits 502 and 602 can perform... Figure 1 , 2 Or any one or any combination of the 3 boxes.

[0064] Figure 7 A machine-readable medium 702 associated with processor 704 is shown. The machine-readable medium 702 includes instructions 706 that, when executed by processor 704, cause processor 704 to perform a task.

[0065] Machine-readable medium 702 may include instructions that, when executed by processor 704, cause processor 704 to perform [the following actions]. Figure 1 , Figure 2 or Figure 3 Any one or any combination of the boxes. In some examples, the instructions can make the processor act as Figure 5 or Figure 6 Any part of the processing circuits 502 and 602.

[0066] In this example, instruction 706 includes instruction 708 to cause processor 704 to identify features smaller than a threshold size of the object to be generated in additive manufacturing. Features smaller than this threshold size can be identified by executing... Figure 1 Frame 102 Figure 2 box 202 or Figure 3 The methods described in blocks 302 to 308 can be used for identification, and / or instruction 708 can provide feature identification module 504.

[0067] In this example, instruction 706 includes instruction 710 to cause processor 704 to determine a first density of flux, which, when applied to the build material during the generation of the identified feature, will produce a feature with predetermined flexibility. The determination of the first density to be applied during the generation of the identified feature can be based on… Figure 1 Box 104 or Figure 2 The box 210 is used to execute, and / or the instruction 710 can provide the flux module 506.

[0068] In this example, instruction 706 includes instruction 712 to cause processor 704 to determine a second density of flux to be applied when generating (a plurality of) other features of the object. The second density may be an average density. It can be determined according to... Figure 1 Box 106 or Figure 2 Boxes 212 and 214 are used to determine the second density to be applied when generating other features.

[0069] Instruction 706 may also include instructions for determining the generated object. The determined instructions may include instructions for using a first density of flux when generating the identified features and instructions for using a second density of flux when generating other features.

[0070] The instructions for generating the object, when executed by the additive manufacturing apparatus, cause the apparatus to generate the object using a first density of the flux when generating the identified features and a second density of the flux when generating other features, wherein, in some examples, the second density is greater than the first density. Instruction 712 may provide instruction module 604.

[0071] The examples in this disclosure can be provided as methods, systems, or machine-readable instructions, such as software, hardware, firmware, etc., any combination thereof. Such machine-readable instructions can be included on or on a computer-readable storage medium (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-readable program code thereon.

[0072] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and systems according to examples of this disclosure. Although the flowcharts above illustrate a particular order of execution, the order of execution may differ from that described. A block described in one flowchart may be combined with a block in another flowchart. It should be understood that each block in a flowchart and / or block diagram, and combinations of blocks in flowcharts and / or block diagrams, can be implemented using machine-readable instructions.

[0073] Machine-readable instructions can be executed, for example, by a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to implement the functions described in the specification and figures. In particular, a processor or processing device can execute machine-readable instructions. Therefore, functional modules of the apparatus and device (such as feature recognition module 504, flux module 506, and / or instruction module 604) can be implemented by a processor that executes machine-readable instructions stored in memory or by a processor that operates according to instructions embedded in logic circuitry. The term "processor" should be interpreted broadly to include CPUs, processing units, ASICs, logic units, or programmable gate arrays, etc. The methods and functional modules can be executed entirely by a single processor or divided among several processors.

[0074] Such machine-readable instructions can also be stored in a computer-readable storage device, which can instruct a computer or other programmable data processing device to operate in a specific mode.

[0075] Such machine-readable instructions can also be loaded onto a computer or other programmable data processing device to cause the computer or other programmable data processing device to perform a series of operations to produce computer-implemented processing. Thus, the instructions that execute on the computer or other programmable device implement the functions specified by the boxes(s) in the flowchart and / or block diagram.

[0076] Furthermore, the teachings herein can be implemented in the form of a computer software product stored in a storage medium and including multiple instructions for causing a computer device to implement the methods described in the examples of this disclosure.

[0077] While methods, apparatuses, and related aspects have been described with reference to certain examples, various modifications, alterations, omissions, and substitutions can be made without departing from the spirit of this disclosure. Therefore, it is intended that the methods, apparatuses, and related aspects be limited only by the scope of the appended claims and their equivalents. It should be noted that the foregoing examples are illustrative and not limiting of the content described herein, and those skilled in the art should be able to devise many alternative implementations without departing from the scope of the appended claims.

[0078] The word "comprising" does not exclude the presence of elements other than those listed in the claims, and "a" or "an" does not exclude multiple elements, and a single processor or other unit can perform the functions of the several units recited in the claims.

[0079] Features of any dependent claim may be combined with features of any of the independent claim or other dependent claims.

Claims

1. A method for additive manufacturing, comprising: The processing circuitry identifies features of the object to be generated in additive manufacturing that are smaller than a threshold size. The processing circuitry determines the first density of the printing agent to be applied to the building material when generating the identified features of the object, based on the expected flexibility of the object's features; and When another feature of the object is generated that is larger than the threshold size, the processing circuitry determines a second density of the printing agent to be applied to the building material. Wherein, the first density and the second density are represented by the number of flux drops per unit area, and wherein the first density is based on the predicted temperature of the expected flexibility without taking into account the feature of being smaller than the threshold size, while the second density is based on the predicted temperature of the material being constructed at each location.

2. The method according to claim 1, wherein, Identifying the features includes: Select features with dimensions below the threshold size.

3. The method according to claim 2, wherein, The dimension refers to the cross-sectional area of ​​the feature and / or the distance between opposing faces.

4. The method according to claim 1, wherein, Identifying the features includes: The initial volume of the object is reduced by a threshold amount in at least one spatial dimension to provide the volume of the eroded object; Increase the volume of the eroded object in at least one spatial dimension by the threshold amount to provide an expanded object volume; Compare the expanded object volume with the initial object volume; and The difference between the expanded object volume and the initial object volume is identified as a candidate feature that is smaller than a threshold size.

5. The method according to claim 1, wherein, Determining the first density of the printing ink includes: When generating the identified features, the first candidate density of the printer is used to generate the first test object; When generating the identified features, a second candidate density of the printer is used to generate a second test object; and The printing agent of the first density is selected based on the flexibility of the features on the first test object and the flexibility of the features on the second test object.

6. The method according to claim 1, wherein, The determined first density is higher for lower expected flexibility and lower for higher expected flexibility.

7. The method according to claim 1, wherein, The desired flexibility can be selected by the user.

8. The method according to claim 1, wherein, Determining the second density includes: Determine the initial density value; and Modify the initial density value at at least one location on the building material layer based on at least one of the following: Construct a predicted or measured temperature distribution within the room; The location of the object within the constructed interior; The proximity of the object to another object to be generated within the construction chamber; and / or The expected deformation of the object.

9. The method according to claim 1, further comprising: A determination instruction, when executed by an additive manufacturing apparatus, causes the additive manufacturing apparatus to use a first density of the printing agent to generate the object when generating the feature; as well as Execute the instructions to generate the object.

10. The method according to claim 1, wherein, The object is a brush, and the feature smaller than the threshold size is the brush bristles.

11. An apparatus for additive manufacturing including a processing circuit, the processing circuit comprising: The feature recognition module is used to identify features in the object model data that are smaller than a threshold size for the object to be generated in additive manufacturing. as well as A flux module is configured to determine the area coverage of flux to be applied to the building material during the generation of the object based on the correlation between the flux and the flexibility of features identified in the object at the time of generation, wherein the flux module is further configured to determine: The first area coverage of the flux to be applied when generating the identified features; and A second area coverage of flux to be applied when generating other features larger than the threshold size. Wherein, the first area coverage and the second area coverage are represented by the number of flux drops per unit area, and wherein the first area coverage is based on the expected flexibility of the feature smaller than the threshold size without taking into account the predicted temperature of the feature smaller than the threshold size, while the second area coverage is based on the predicted temperature of the building material at each location.

12. The apparatus according to claim 11, wherein, The processing circuit further includes: An instruction module is configured to create object generation instructions that instruct the additive manufacturing apparatus to generate the object using a first area coverage of the flux when generating identified features and based on a second area coverage of the flux when generating other features; and The device further includes: An additive manufacturing apparatus for generating an object according to the object generation instructions.

13. An object produced by additive manufacturing, comprising: A flexible feature smaller than a threshold size is generated using the first density of the flux; Features larger than the threshold size are generated using the second density of the flux. Wherein, the first density and the second density are represented by the number of flux drops per unit area, and wherein the first density is based on the expected flexibility of the flexible feature without regard to the predicted temperature of the flexible feature, while the second density is based on the predicted temperature of the material being constructed at each location.

14. The object according to claim 13, wherein: The second density of the flux is greater than the first density of the flux; The first density remains substantially constant throughout the flexible feature; as well as The second density is the average density, and the density of the flux varies throughout the feature that is larger than the threshold size.

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